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title: Effects of Parental Dietary Restriction on Offspring Fitness in Drosophila
melanogaster
authors:
- Hye-Yeon Lee
- Bora Lee
- Eun-Ji Lee
- Kyung-Jin Min
journal: Nutrients
year: 2023
pmcid: PMC10005678
doi: 10.3390/nu15051273
license: CC BY 4.0
---
# Effects of Parental Dietary Restriction on Offspring Fitness in Drosophila melanogaster
## Abstract
Dietary restriction (DR) is a well-established strategy to increase lifespan and stress resistance in many eukaryotic species. In addition, individuals fed a restricted diet typically reduce or completely shut down reproduction compared to individuals fed a full diet. Although the parental environment can lead to changes epigenetically in offspring gene expression, little is known about the role of the parental (F0) diet on the fitness of their offspring (F1). This study investigated the lifespan, stress resistance, development, body weight, fecundity, and feeding rate in offspring from parental flies exposed to a full or restricted diet. The offspring flies of the parental DR showed increases in body weight, resistance to various stressors, and lifespan, but the development and fecundity were unaffected. Interestingly, parental DR reduced the feeding rate of their offspring. This study suggests that the effect of DR can extend beyond the exposed individual to their offspring, and it should be considered in both theoretical and empirical studies of senescence.
## 1. Introduction
Since McCay et al. reported that the restriction of food intake increases the maximum lifespan in rats [1], dietary restriction (DR), i.e., reduced nutrient intake without malnutrition, has been well described as an intervention to delay aging in a wide array of organisms from yeasts to primates and to prevent the onset of age- or diet-associated diseases in rodents and primates [2]. DR significantly impacts the life-history characteristics of organisms, including development, reproduction, locomotion, and lifespan. Altered life-history characteristics by the parental environment can lead to epigenetic modification in offspring gene expression. Life history theory asserts that a fundamental trade-off between the number of offspring tends to shift to “fewer but better-provisioned offspring” under stressful conditions [3]. In addition, the environmental programming of gene expression during gestation and early postnatal periods can produce long-term changes in the structure and function of an organism, allowing it to adapt better to its current environment [4,5]. Thus, DR may alter the phenotype of individuals exposed directly to DR and their offspring via parental effects [6,7].
Numerous studies have reported that the parental diet of rodents affects the fitness of their offspring. In most of these studies on rodents, however, a dietary regimen has been applied only during gestation and lactation [8,9,10,11]. Most studies on the effects of the parental diet during pre-pregnancy focused on the negative effects of obese parents [12,13,14] or the transgenerational effects of parental DR. For example, some researchers reported that a low maternal protein diet during pregnancy leads to reduced lifespan [8] or hyperinsulinemia and lowered insulin-signaling protein expression [9] in rat offspring or changes in the mitochondria gene expression in the liver and skeletal muscle of mice offspring [11]. Restricted diet during pregnancy also increased the low-density lipoprotein and global histone H3 acetylation but did not change the hepatic DNA methylation and expression in the offspring of rat fetuses [15,16,17]. Although the transgenerational effects of pre-pregnancy diets in vertebrates are still lacking, there are several studies of the transgenerational effects of DR in invertebrate model studies. In fruit flies, high sugar levels in the parental diet influence offspring obesity [12] or the obese-like phenotype [13], but parental sugar consumption does not affect the offspring’s lifespan [18]. In nematodes, the normal diet-fed offspring of a fasting-induced DR mother showed reduced fecundity, slower growth rate, decreased body size, and/or decreased mortality [19,20]. Although there is increasing evidence that the father’s diet or fitness also influences fitness in their offspring [18,21,22,23], most previous studies have focused only on the maternal DR effects and studies examining both maternal and paternal effects in a single experimental design are limited. In addition, the effect of early parental DR without fasting on offspring fitness is still unclear.
This study examined the effects of parental DR on the lifespan and fitness of their offspring using Drosophila melanogaster, one of the most widely used model organisms for studies on genetics and transgenerational effects. In this study, we investigated the intergenerational effect of DR (F0–F1) since previous studies suggested that the longevity effect of DR is instant, not persisting for multiple generations [20,24,25]. The results showed that short periods of parental DR increased the lifespan, resistance to environmental stress, and body weight but had little effect on the development, fecundity, and feeding rate of the offspring.
## 2.1. Fly Strain and Husbandry
The experiments were conducted using wild-type Canton-S flies, originally obtained from the Bloomington Drosophila Stock Center (Indiana University, Bloomington, IN, USA). All flies were cultured and reared at 25 °C and $65\%$ humidity on 12:12 h light:dark cycles. Larval crowding was avoided by laying approximately 150 eggs on 250 cm3 fly bottles containing 25–30 mL of medium and were developed until the eclosion to adult. Standard CSY medium (52 g/L cornmeal, 110 g/L sugar, 25 g/L baker’s yeast, 8 g/L agar, 5 mL/L propionic acid, and 2.2 mL/L tegosept in $95\%$ ethanol) was used to rear the fly larvae. Parental DR was administered by feeding the parent flies CSY food, including 160 g/L (full diet) or 40 g/L (restricted diet, $25\%$ protein level of full diet) Saf-yeast extract.
## 2.2. Fly Collection and Parental Breeding Design
Newly eclosed flies for parental generation were collected within 12 h and allocated to Φ25 × 100 mm vials containing full or restricted medium. Approximately 20 single-sex flies were kept per vial. The flies were transferred to fresh vials twice a week. After a 7–10 days feeding period, the parental flies were allocated to obtain eggs in standard CSY medium. The larval density of the offspring was adjusted by controlling the number of mating pairs of parents because the larval viability is influenced by larval density [26]. The number of parent flies used was 5 to 10 pairs in a full-fed diet group and 10 to 15 pairs in a restricted diet. Figure 1 outlines the experimental scheme.
## 2.3. Lifespan
Newly eclosed Canton-S adult flies were collected over 48 h and assigned randomly to a 500 cm2 demography cage to a final density of 100 males and 100 females per cage. All flies were cultured at 25 °C and $65\%$ humidity on 12:12 h light:dark cycles. The fresh food was changed every two days, and all deaths were recorded. Three replicates were established for each group. Four trials were conducted to confirm the offspring’s lifespan. The parental flies were kept in mixed-sex groups similar to natural conditions (trials I and II) or in single-sex groups eliminating the reproductive effects (trials III and IV) to consider sexual interactions. The Kaplan–Meier survival estimator was used to estimate the survival function from the lifetime data. Log-rank tests were carried out to determine the statistical significance of the differences in the mean lifespan. In this study, maximum lifespan is defined as when the last $25\%$ of the fruit flies used in the experiment are alive. The JMP statistical package (SAS Institute, Cary, NC, USA) and Statistical Package for the Social Sciences (SPSS, SPSS Inc., Chicago, IL, USA) were used for the analyses.
## 2.4. Stress Resistance
Seven- to ten-day-old offspring flies were transferred into a new vial (Φ25 × 100 mm) with ten single-sex flies in each. Fifteen vials were established for each group in each stress resistance test. All flies were tested at 25 °C and $65\%$ humidity on 12:12 h light:dark cycles. For the stress resistance test with heat shock, the F1 flies were exposed to 39.5 °C by transferring heat through the air. The number of dead flies was recorded every 10 min until all the flies had died. For the starvation resistance test, the F1 flies were exposed to a medium that contained only 8 g/L agar. The number of dead flies was recorded every six hours until all the flies had died. The F0 flies were exposed to SY medium for the oxidative stress resistance test. The F1 flies were exposed to acute oxidative stress (18 mM paraquat). The food for oxidative stress contained 50 g/L sucrose (Sigma–Aldrich, St. Louis, MO, USA) to exclude the effects of starvation. The number of dead flies was recorded every three hours until all the flies had died. The Kaplan–Meier survival estimator was used to estimate the survival function from the lifetime data. Log-rank tests were carried out to determine the statistical significance of the differences in the mean lifespan. The JMP statistical package (SAS Institute, Cary, NC, USA) was used for the analyses.
## 2.5. Developmental Viability and Time
The 2-day-old flies were put in egg collection plate (90 × 15 mm diameter) containing standard CSY with $4\%$ agar, and the eggs were collected for 12 h using the fast egg collection method. Then, 10 eggs were gently transferred to Φ25 × 100 mm vials containing standard CSY medium using a small brush. All flies were cultured at 25 °C and $65\%$ humidity on 12:12 h light:dark cycles. The pupae and adult flies were counted every 12 h until no additional flies emerged. Fifteen replicates were established for each group. The data are presented as the mean ± SEM values.
## 2.6. Fecundity
Newly eclosed virgin flies were collected, and 24 h later, 1 female and 2 males were placed together in the Φ25 × 100 mm vial containing CSY medium. All flies were tested at 25 °C and $65\%$ humidity on 12:12 h light:dark cycles. The flies were transferred carefully every 24 h, and the number of eggs laid on the medium was counted for 10 days. Twenty replicates for each group were established. The data are presented as the mean ± SEM.
## 2.7. Feeding Rate
The flies were pre-exposed to starvation for four hours before feeding. All flies were tested at 25 °C and $65\%$ humidity. The 20-day-old-flies were fed a standard CSY medium containing $2.5\%$ FD&C blue No.1 for 1 hour. The fly heads were cut off by quick freezing in liquid N2 to remove the red eyes of a fly. Then, 4 flies were placed in a 1.5 mL tube and homogenized with 100 µL of distilled water. The samples were centrifuged at 13,000 r/min (8400 × g) for 5 minutes at 4 °C. Subsequently, 100 µL of supernatant of the sample without lipid was transferred to another 1.5 mL tube and 100 µL distilled water was added. The absorbance was measured at 595 nm using a Sunrise microplate reader (TECAN, Männedorf, Switzerland). Eighteen replicates were established for each group. The data are presented as the mean ± SEM.
## 2.8. Body Weight
Newly eclosed flies were collected and transferred to Φ25 × 100 mm vials containing 10 single-sex flies each. All flies were reared at 25 °C and $65\%$ humidity on 12:12 h light:dark cycles. The body weight of the flies was measured on a microbalance (PAG214C, Ohaus, Parsippany, NJ, USA) after CO2 anesthesia. After seven days on CSY foods, the body weight of the same flies was measured. Fifteen replicates were established for each group. The data are presented as the mean ± SEM.
## 2.9. Statistical Analysis
The log-rank tests for the lifespan and stress resistance data were carried out using survival models (Kaplan–Meier survival analysis) in the IBM SPSS statistics 21 (IBM, Armonk, NY, USA). The test for normality (Shapiro–Wilk test) and the statistical probabilities (F-test, t-test, and Wilcoxon rank sum test) of the data in this study were performed using R studio software.
## 3.1. Dietary Restriction Increases the Lifespan and Reduces the Fecundity in Parent Flies
To determine optimal DR conditions in our laboratory conditions, parent fruit flies (F0) were fed a 2, 4, 12, or $16\%$ yeast extract. The F0 male flies fed the 2 or $4\%$ yeast extract diet had higher survival than those fed the $16\%$ yeast diet (Figure 2a, Table 1, $16\%$, 50.89 ± 0.57 days; $12\%$, 53.64 ± 0.53 days, $5.4\%$ increase, log-rank test, χ2 = 13.50, $p \leq 0.0005$; $4\%$, 53.21 ± 0.66 days, $4.6\%$ increase, log-rank test, χ2 = 19.01, $p \leq 0.0001$; $2\%$, 53.05 ± 0.78 days, $4.2\%$ increase, log-rank test, χ2 = 28.86, $p \leq 0.0001$). The F0 female flies fed the 2 or $4\%$ yeast extract diet also had higher survival than those fed the $16\%$ yeast diet (Figure 2b, Table 1, $16\%$, 47.66 ± 0.91 days; $12\%$, 60.41 ± 1.01 days, $26.8\%$ increase, log-rank test, χ2 = 105.47, $p \leq 0.0001$; $4\%$, 72.36 ± 1.39 days, $51.8\%$ increase, log-rank test, χ2 = 225.84, $p \leq 0.0001$; $2\%$, 66.73 ± 1.36 days, $40.0\%$ increase, log-rank test, χ2 = 175.00, $p \leq 0.0001$). In the F0 females, however, the lifespan of the flies fed $2\%$ yeast was $7.78\%$ shorter than the flies with $4\%$ yeast, indicating that this $2\%$ yeast diet could lead to malnutrition in fruit flies [27,28]. Thus, $16\%$ yeast was considered the full diet (FD), and $4\%$ was the restricted diet (DR) for parental diet conditions.
DR in individuals reduced reproduction. The fecundity of F0 females was measured and adjusted for the number of eggs between the F0 groups fed an FD or DR to remove the possibility that reduced offspring population affects the physiological changes. As described elsewhere, DR reduced the fecundity of F0 females (Figure 2c; full diet, average number of eggs/days = 41 ± 2.3; restricted diet, average number of eggs/days = 11 ± 0.6, $73.1\%$ decrease, student’s t-test, $p \leq 0.0001$). According to these results, the number of parent flies was controlled to 5 to 10 pairs in the full-fed diet group and 10 to 15 pairs in DR to obtain the offspring fruit flies (F1). Figure 1 shows the detailed mating scheme.
## 3.2. Parental Dietary Restriction Increases the Lifespan of the Offspring Flies
Four combinations of flies were used to determine if the longevity effect of parental DR affects the lifespan of their F1 flies: FD male and FD female (♂FD × ♀FD), DR male and FD female (♂DR × ♀FD), FD male and DR female (♂FD × ♀DR), and DR male and DR female (♂DR × ♀DR) were mated (Figure 1). The lifespan of F1 flies was measured under normal diet conditions (Figure 3). The F1 lifespan was increased, and the F1 mortality was decreased when their parents were fed on a DR instead of FD (Figure 3a,b and Table 2; Male, ♂FD × ♀FD, 55.87 ± 0.89 days; ♂DR × ♀DR, 63.42 ± 0.88 days, $13.5\%$ increase, log-rank test, χ2 = 43.3299, $p \leq 0.0001$; Female, ♂FD × ♀FD, 56.04 ± 0.78 days; ♂DR × ♀DR, 63.20 ± 1.03 days, $13.3\%$ increase, log-rank test, χ2 = 92.7941, $p \leq 0.0001$). Interestingly, cross-combination with the FD and DR (♂DR × ♀FD or ♂FD × ♀DR) extended the lifespan of only the F1 female (♂DR × ♀FD, 60.50 ± 0.74 days, $8.0\%$ increase, log-rank test, χ2 = 24.138, $p \leq 0.0001$; ♂FD × ♀DR, 65.49 ± 0.92 days, $16.9\%$ increase, log-rank test, χ2 = 109.4231, $p \leq 0.0001$) but not the F1 male (♂DR × ♀FD, 60.50 ± 0.74 days, log-rank test, χ2 = 3.6747, $$p \leq 0.0552$$; ♂FD × ♀DR, 58.80 ± 0.86 days, log-rank test, χ2 = 3.6341, $$p \leq 0.0566$$). The results were re-analyzed with the paternal or maternal diet to clarify which one of the father or mother affects the F1 lifespan. FatherFD included the ♂FD × ♀FD and ♂FD × ♀DR, FatherDR included the ♂DR × ♀FD and ♂DR × ♀DR, MotherFD included the ♂FD × ♀FD and ♂FD × ♀FD, and MotherDR included the ♂FD × ♀DR and ♂DR × ♀DR (Figure 3c,d and Table 3). The lifespan of an F1 male that had a DR-fed father or mother increased regardless of what the opposite parent was fed (FatherFD, 57.33 ± 0.62 days; FatherDR, 60.78 ± 0.63 days, $6.0\%$ increase, log-rank test, χ2 = 24.2517, $p \leq 0.0001$; MotherFD, 57.06 ± 0.63 days; and MotherDR, 61.10 ± 0.62 days, $7.1\%$ increase, log-rank test, χ2 = 25.3409, $p \leq 0.0001$). In females, only the DR-fed mother increased the lifespan; the father’s diet did not affect the lifespan of the F1 female (FatherFD, 60.90 ± 0.64 days; FatherDR, 61.85 ± 0.64 days, $1.6\%$ increase, log-rank test, χ2 = 0.7762, $$p \leq 0.3783$$; MotherFD, 58.31 ± 0.55 days; and MotherDR, 64.35 ± 0.69 days, $10.4\%$ increase, log-rank test, χ2 = 137.0076, $p \leq 0.0001$). Thus, in the case of the female offspring, the longevity effect by parental DR was more prominent than that of the male offspring, but it appears to be influenced mainly by the mother’s diet. On the other hand, the male offspring tended to extend their lifespan even if only one of the parents ate the restricted diet, but it showed a significant increase in lifespan when both parents restricted the diet.
The lifespan of F1 was measured independently in three repeated trials (Figure S1). These parental DR-induced longevity effects were also shown when the yeast type of the parental diet was changed from yeast extract to Brewer’s inactive yeast (Figure S1a). The mean lifespan of flies from parents fed on a DR tended to increase in the F1 males and females in all trials. In particular, the F1 females from parents fed a DR had significantly longer lifespans in all trials. Parents fed a DR tended to increase the lifespan of the F1 males, but only two trials (first and third) were statistically significant. Overall, seven days of exposure to different dietary regimens in parental flies had significant effects on the F1 lifespan, and there were also sex-specific effects on the offspring’s lifespan. Accordingly, subsequent experiments were performed using only the FD × FD and DR × DR groups to investigate the effects of parental DR on offspring by removing maternal–paternal factors.
## 3.3. Parental (F0) Dietary Restriction Increases the Resistance to Various Stressors in Offspring (F1) Flies
The effects of parental diet on the F1 susceptibility to environmental stresses were examined because resistance to environmental stress is closely related to individual longevity. The survival rate of the offspring under heat shock stress, oxidative stress, or starvation stress was evaluated. The resistance to heat shock was significantly greater in both male and female offspring from the parents fed on a DR ($24\%$ and $37\%$, respectively) than the offspring from parents fed on an FD (Figure 4a, Log-rank test, male, χ2 = 39.0, $p \leq 0.0001$; female, χ2 = 50.0, $p \leq 0.0001$). The resistance to oxidative stress was also increased by $18\%$ and $17\%$ in the male and female offspring of parents fed a DR, respectively, than offspring from parents fed on an FD (Figure 4b, Log-rank test, male, χ2 = 6.7, $p \leq 0.01$; female, χ2 = 4.1, $p \leq 0.05$). In the case of starvation stress, parental DR increased resistance to starvation in female offspring by $23\%$ but not in male offspring (Figure 4c, Log-rank test, male, χ2 = 1.0, $$p \leq 0.316$$; female, χ2 = 33.6.0, $p \leq 0.0001$). Thus, stress resistance was generally greater in F1 from parents fed a DR than those from parents fed on an FD. Moreover, there also appeared to be sex-specific effects on offspring similar to the results of the lifespan tests. These results suggest that parental DR has beneficial effects on various environmental stress resistances of their offspring.
## 3.4. Parental Dietary Restriction Affects the Offspring’s Body Weight and Feeding Rate but Not the Development and Fecundity of the Offspring Flies
The offspring’s developmental viability, body weight, fecundity, and feeding rate, which can influence the ability to survive and thrive in the environment, were measured to examine the intergenerational effects of parental DR on various offspring (F1) traits. DR leads to delayed developmental timing and reduced fecundity, but the parental diet did not affect their offspring’s developmental viability (Figure 5a left, Wilcoxon rank sum test; egg-to-pupa, $$p \leq 0.124$$; pupa-to-adult, $$p \leq 0.164$$), developmental timing (Figure 5a right, Wilcoxon rank sum test; emergence to pupa, $$p \leq 0.361$$; emergence to adult, $$p \leq 0.401$$), and fecundity (Figure 5b, Student’s t-test, $$p \leq 0.382$$). The feeding rate of their offspring was measured to determine if parental feeding behaviors enhanced by DR were transmitted to the offspring. Interestingly, parental DR decreased the feeding rate of F1 males but not F1 females (Figure 5c; F1 ♂, Student’s t-test, $p \leq 0.0001$; F1 ♀, Student’s t-test, $$p \leq 0.733$$). The offspring’s body weight was then measured because it has been reported that parental diet could alter the body composition of their offspring [13]. In these results, newly hatched young adult male or female offspring (1 day of age) were heavier when their parents were fed DR than when their parents were fed the FD (Figure 5d; F1 ♂, Day 1, Student’s t-test, $p \leq 0.05$; F1 ♀, Day 1, Wilcoxon rank sum test, $p \leq 0.05$). These differences in the body weight at the 1-day-old age disappeared at the 7-day-old age (Figure 5d; F1 ♂, Day 7, Student’s t-test, $$p \leq 0.366$$; F1 ♀, Day 7, Student’s t-test, $$p \leq 0.289$$). Interestingly, the offspring of the 2 groups showed a difference in the rate of body weight change over 7 days in the same medium composition (male, F1Full Diet, $0.37\%$ decrease, F1Restricted Diet, $9.37\%$ decrease; female, F1Full Diet, $16.68\%$ increase, F1Restricted Diet, $14.92\%$ increase). It indicates that the offspring of DR-fed parents have more difficulty gaining weight than the offspring of FD-fed parents. Thus, the offspring’s body weight was increased by parental DR, but there were no changes in developmental time. Hence, the effects of reduced food intake might be a factor that leads to a longevity effect by parental DR because lifespan extension can be induced by the reduction of food intake [29].
## 4. Discussion
This study examined the effects of parental (F0) dietary restriction on the offspring (F1) fitness, such as lifespan, resistance to environmental stress, development, body weight, fecundity, and feeding rate, using the fruit fly, Drosophila melanogaster. In this study, we investigated intergenerational (F0–F1) effects of DR because previous studies indicated DR effects did not persist for several generations. Great-grand-offspring (F3) of fasting-induced DR mother showed a reduced fitness and increased mortality risk indicating that the transgenerational benefit of DR is instant in nematode [20]. Similarly, after 25 generations of DR, male DR flies had increased reproduction, but their survival rate did not increase [24]. In the case of females, after 50 generations of DR, female DR flies had increased reproduction, but their survival rate decreased [25]. These results indicate that longevity effects induced by DR may not persist over multiple generations, and organisms evolve to increase reproductivity under long-term DR conditions. This study showed that short periods of parental dietary restriction during the adult stage had a significant impact on fitness changes, particularly the lifespan in offspring flies (Figure 3). The offspring phenotype could be altered in response to adaptive parental effects, which might explain why lifespan extension occurred by reducing the intrinsic mortality in the individuals and their offspring. The lifespan and environmental stress resistance are correlated [30,31]. The offspring’s resistance to heat shock, starvation, or oxidative stress was examined in this study (Figure 4). Both male and female offspring from parents fed a restricted diet survived better than offspring from parents fed a full diet, except for survival with starvation resistance in male offspring. The intergenerational DR effects on lifespan and resistance to starvation stress were sex specific. Previous studies reported that parental environmental conditions might induce different epigenetic effects in sons versus daughters. For example, exposure to environmental compounds, such as endocrine disruptor vinclozolin, induced sex-specific transgenerational alterations in the brain transcriptomes and behavior in rats [32]. In a study using the springtail Orchesella cincta, similar to our results, the offspring of a DR-fed mother showed increased flexibility in low-food conditions, but these maternal effects were not observed in the sons [33]. Heat shock proteins (HSPs) are induced by several stressors, such as heat shock, oxidative stress, or even DR [34]. These proteins perform a chaperone function by assisting new proteins in correcting folding or refolding proteins damaged by cell stress [35]. DR, as hormetic metabolic stress, can activate several defense mechanisms to increase the lifespan and could induce the heat shock response [36]. For example, DR promoted the HSP70 protein and hsp70 mRNA synthesis in hepatocytes of 28-month-old rats [34]. Offspring from parents in the Artemia model who survived heat shock have greater tolerance to thermal stress than their respective offspring controls [37]. Thus, the parental DR can lead to compensatory effects related to extended lifespan and increased environmental stress resistance in the offspring. The theory of hormesis explains that a stressful circumstance could enhance the organism’s fitness after exposure [38].
The thrifty phenotype hypothesis is conceptually associated with hormesis, and these epigenetic effects can be passed from one generation to the next [39]. This hypothesis explains how flies might quickly and semi-permanently modify gene expression to adapt to a restricted regimen and pass these effects on to the next generation, thereby increasing the offspring’s survival by increasing their stress resistance. Several studies showed prenatal DR significantly changed the expression of major genes related with two epigenetic mechanisms (DNA methylation and histone modification) [15,16,17]. We investigated the expression of the sir2 gene, a well-known longevity effecter of DR, but the sir2 gene expression did not increase in the offspring of DR-fed parents (Figure S2). Previous studies reported that parental diet affects offspring’s energy metabolism, fat content, glucose homeostasis, and insulin resistance [40,41,42]. Thus, future studies are necessary to investigate the epigenetic effects of DR on energy metabolism, including fat and glucose homeostasis, to improve our understanding of the mechanisms of beneficial DR effects.
Increased resistance to starvation might be connected to the increased body weight of the offspring since body weight and starvation resistance are tightly related to each other. Our results showed that parents fed a restricted diet produced heavier offspring than those fed a full diet at birth (Figure 5d). Similarly, the offspring from parents fed a restricted diet showed increased body size [43] or body mass [44] in fruit flies. Given the well-known intraspecific trade-off between the number of offspring and offspring size [3,45], parents fed a restricted diet produce larger offspring. This outcome might be one of the mechanisms of adaptive parental effects [46]. In organisms without parental care, parental provisioning can be estimated by their offspring’s egg or newborn size [47]. However, the differences in body weight observed between the two groups of newly eclosed offspring disappeared after seven days in both sons and daughters (Figure 5d), suggesting that the effect of the parental diet on the offspring’s body weight is not sustained to the early adult stage of the offspring. In a mice study, interestingly, there was a similar result that food restriction of obese mice during pregnancy induced decreased body weight gain of offspring compared to that of high-fat diet mother’s offspring [48]. Additionally, many rodent studies showed that restricted protein content is associated with higher food intake in female offspring, but these differences in food intake disappeared when food intake was adjusted for body mass [49]. These results indicate that parental DR can reduce the risk of obesity in offspring as well as parents.
According to this present study, developmental viability from egg-to-pupa (pupation) or pupa-to-adult (eclosion) and developmental time to emergence showed no differences between the groups by their parental diet. Nevertheless, previous studies reported several contradictive effects of parental diet on offspring development in fruit fly or nematodes. The eggs from parents raised on a poor larval diet (approximately $\frac{1}{4}$ to $\frac{1}{8}$ of the standard diet) were heavier and developed faster than those from parents raised on standard food [44,46,50], but there are strain-specific differences in development time [44]. In contrast, the offspring from both parents raised on a poor diet have a longer development time than those from parents raised on standard food [43,44]. Interestingly, developmental time of the offspring was shortened when only one parent was raised on a poor diet [28] indicating that both maternal and paternal nutrition can influence offspring development. In the nematode, female offspring of fasting-induced DR mother showed reduced fecundity, slowed development time, and decreased body size [19,20]. On the other hand, most previous studies used “poor and standard” levels of dietary regimens or fasting-induced DR, but this study used full-fed and dietary restriction (reduced nutrient intake, especially yeast-protein source, without malnutrition) conditions. Additionally, this study adjusted the number of eggs between the F0 groups fed an FD or DR to remove the possibility that offspring population affects the physiological changes. Thus, the differences in the results of the previous and present studies might be due to the difference in fly strain, species, dietary composition, and/or population density control.
Parental DR reduced the food intake of their male offspring, but there were no significant changes in that of the female offspring (Figure 5c). This difference might be due to sexual dimorphic effects on feeding behavior. Because the lifespan is influenced significantly by food intake [29], reduced food consumption in male offspring whose parents were exposed to a restricted diet might be one factor that leads to an extended lifespan. Several reports suggested that transgenerational feeding behavior of offspring is influenced by parental dietary regimen in rat or fruit fly [51,52,53]. Several studies using fruit flies have shown that the longevity effect of DR is more effective in females than in males [54,55]. This may be because male and female generally pursue divergent reproductive strategies and thus have different sensitivities to nutritional interventions [56,57]. Therefore, the intrinsic changes in the mothers that led to the greater lifespan extension by DR may have been passed on to their daughters also. Our results suggest that the feeding behavior may be a heritable predisposition to their offspring, and the effects of reduced food intake might be one of the factors that lead to their extended lifespan.
## 5. Conclusions
Previous studies revealed the effects of diet restriction on aging, with lifespan extension and other beneficial effects observed in flies, nematodes, yeasts, insects, rodents, and possibly even primates [2]. The underlying mechanisms of hormetic DR effects on cellular responses remain conserved across the evolutionary tree, even though many organisms have diverged evolutionarily over time [58]. At the same time, theoretical models show that maternal effects can influence the speed and trajectory of evolutionary changes [59]. In this light, it would be worth determining if the parental effects of DR observed here apply to other organisms and developing an explicit theoretical treatment of the role of parental effects on the evolution of senescence.
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|
---
title: An Analysis of the Toxicity, Antioxidant, and Anti-Cancer Activity of Cinnamon
Silver Nanoparticles in Comparison with Extracts and Fractions of Cinnamomum Cassia
at Normal and Cancer Cell Levels
authors:
- Y. G. El-Baz
- A. Moustafa
- M. A. Ali
- G. E. El-Desoky
- S. M. Wabaidur
- M. M. Faisal
journal: Nanomaterials
year: 2023
pmcid: PMC10005684
doi: 10.3390/nano13050945
license: CC BY 4.0
---
# An Analysis of the Toxicity, Antioxidant, and Anti-Cancer Activity of Cinnamon Silver Nanoparticles in Comparison with Extracts and Fractions of Cinnamomum Cassia at Normal and Cancer Cell Levels
## Abstract
In this work, the extract of cinnamon bark was used for the green synthesis of cinnamon-Ag nanoparticles (CNPs) and other cinnamon samples, including ethanolic (EE) and aqueous (CE) extracts, chloroform (CF), ethyl acetate (EF), and methanol (MF) fractions. The polyphenol (PC) and flavonoid (FC) contents in all the cinnamon samples were determined. The synthesized CNPs were tested for the antioxidant activity (as DPPH radical scavenging percentage) in Bj-1 normal cells and HepG-2 cancer cells. Several antioxidant enzymes, including biomarkers, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), glutathione-S-transferase (GST), and reduced glutathione (GSH), were verified for their effects on the viability and cytotoxicity of normal and cancer cells. The anti-cancer activity depended on apoptosis marker protein levels (Caspase3, P53, Bax, and Pcl2) in normal and cancerous cells. The obtained data showed higher PC and FC contents in CE samples, while CF showed the lowest levels. The IC50 values of all investigated samples were higher, while their antioxidant activities were lower than those of vitamin C (5.4 g/mL). The CNPs showed lower IC50 value (55.6 µg/mL), whereas the antioxidant activity inside or outside the Bj-1 or HepG-2 was found to be higher compared with other samples. All samples execrated a dose-dependent cytotoxicity by decreasing the cells’ viability percent of Bj-1 and HepG-2. Similarly, the anti-proliferative potency of CNPs on Bj-1 or HepG-2 at different concentrations was more effective than that of other samples. Higher concentrations of the CNPs (16 g/mL) showed greater cell death in Bj-1 ($25.68\%$) and HepG-2 ($29.49\%$), indicating powerful anti-cancer properties of the nanomaterials. After 48 h of CNPs treatment, both Bj-1 and HepG-2 showed significant increases in biomarker enzyme activities and reduced glutathione compared with other treated samples or untreated controls ($p \leq 0.05$). The anti-cancer biomarker activities of Caspas-3, P53, Bax, and Bcl-2 levels were significantly changed in Bj-1 or HepG-2 cells. The cinnamon samples were significantly increased in Caspase-3, Bax, and P53, while there were decreased Bcl-2 levels compared with control.
## 1. Introduction
Nanotechnology is the newest branch of modern science and the most promising area of research. It helps scientists to use applied knowledge of science and technology to govern matter on the atomic and molecular level. Biological sources have been used for progress of ecological and consistent methodology for the synthesis of various nanomaterials [1]. The interaction of inorganic nanoparticles with biological structures is one of the most exciting areas of research in the modern field of nanotechnology [2,3,4]. Nanoparticles are usually the size of 1 to 100 nm and exhibit different characteristics based on their smaller size, distribution, and morphology compared with bulk materials of the original sources [5,6]. A variety of plant extracts have been tested as potential reductants in *Ag nanosynthesis* instead of toxic chemicals [7,8], which are used in chemical reduction, photoreduction in reverse micelles, and radiation chemical reduction [9]. In addition to being expensive, these methods involve hazardous chemicals which may pose serious health and environmental risks to mankind. Spices have been the spice of life for human beings since time immemorial. There is something very appetizing about the scent and pungency of herbs, so they have become one of the most indispensable ingredients in the preparation of food that is palatable. In addition, spices possess antibacterial and medical/health benefits [10]. Cinnamomum zeylanicum is a small, evergreen tropical tree. Cinnamon Ceylon refers to Sri Lanka, the country that originally produced *Cinnamomum zeylanicum* [11]. Among the most popular herbs used to spice food is cinnamon bark. Cinnamon bark, branches, twigs, and leaves all contain useful phytochemicals; however, the bark is the most commercialized part among them. Additionally, its processed products include essential oils, oleoresins, and food additives. They are widely used in the cosmetic, beverage, pharmaceutical, and food industries as well.
In traditional medicine, it has been used as an anti-cold treatment, an anti-diarrhea treatment, and to treat other problems of the human digestive system. C. zeylanicum bark is rich in terpenoids, polyphenols, and flavonoids, which categorizes it as an antioxidant material [12]. The bark has also shown remarkable pharmacological effects to cure diabetes (type II) and insulin resistance [13]. Several antimicrobial properties of cinnamon essential oils make them suitable preservatives for foods [14]. Compared with commercially available products, zinc oxide nanoparticles reinforced with cinnamon extract have possessed anti-cancer, antioxidant, and anti-inflammatory properties [15]. The nano-cinnamon capsule has also shown remarkable pharmacological effects to cure diabetics (type II) and insulin resistance [16]. Aminzadeh et al. [ 17] studied the anti-tumor activities of aqueous cinnamon extract on cell line 5637, while Das et al. [ 18] mentioned the application of the extracts of C. cassia for improving blood circulation and inhibiting platelet coagulation. The extracts of Ceylon cinnamon are useful α-glucosidase and pancreatic α-amylase inhibitors and are involved in modifying glucose production in the liver [19]. Cinnamon oil-loaded chitosan nanoparticles had higher physical stability and were found to be also more effective against breast tumors, and the mechanisms associated with cinnamon effects on such diseases are connected to carbohydrate digestion as well as the release of fatty acids [20].
In this current work, we have used cinnamon bark extract for the green synthesis of Ag-nanoparticles and investigated its chemical characteristics. To the best of our knowledge, there are no reports in the literature on the possible cytotoxic, apoptotic, antioxidant, and carcinogenic effects of this plant or its fractions on HepG-2, human hepatoma cells. Therefore, considering the importance of cinnamon bark and its phytochemical constituents, the present work was designed to assess the possible cytotoxic, oxidative stress, and carcinogenic potential of cinnamon Ag-nanoparticles (CNPs), cinnamon extracts (CE), and their fractions (HF, CF, EF, and MF) on Bj-1 normal cells and HepG-2 cancerous cells. The findings also suggest the strong activity of cinnamon samples as a natural candidate for chemoprevention or therapeutic agents.
## 2.1. Ethical Approval
The current research study was ethically allowed by the Institutional Animal Caring and Use committee (CU-IACUC) reviewers. The first semester of Cairo University began in September 2022, and the last semester is in September 2024.
## 2.2. Chemicals and Supplies
All the chemicals used in this work were of analytical reagent grade and procured from Sigma-Aldrich (Burlington, MA, USA) and Fluka (Buchs, Switzerland) chemical companies. Folin–Ciocalteu reagent, quercetin, and gallic acid standards were provided by Sigma-Aldrich Co. (St. Louis, MO, USA). AlCl3 hexahydrate, sodium carbonate, and CH3OH were collected from Fisher Scientific (Fair Lawn, NJ, USA). A Milli-Q system (Millipore Corporation, Bedford, NH, USA) was used to produced purified water.
## 2.3. Preparation of Cinnamon Zeylanicum Bark Extracts and Fractions
Cinnamon barks (Figure 1) were dried up at room temperature and mechanically crushed into powder form using an electrical grinder. The aqueous infusion of the sample was prepared by taking cinnamon bark crumb (10 g) with 100 mL of distilled water in an infusion pan, followed by heating at 90 °C for a time span of 20 min. Then, the mixture was filtered to obtain the cinnamon extract (CE). The ethanolic extract (EE) of the cinnamon samples was prepared by taking the moistened cinnamon bark (100 g) with $96\%$ ethanol into a percolator, and then soaked with $96\%$ ethanol at the ratio of 1:8 for 24 h. It was continuously extracted by percolation until complete exhaustion had taken place. Water baths were used to evaporate the solvent until a semisolid extract was obtained, which was referred to as EE.
Preparation of crude fractions: Sixty grams of grounded cinnamon bark material was extracted successively into chloroform, hexane, methanol, and ethyl acetate at room temperature. After this, the extracts were filtered individually and concentrated using a rotary evaporator (Rota vapor R-/; BÜCHI Labortechnik AG, Flawil, Switzerland) to produce the fractions of hexane (HF), chloroform (CF), ethyl acetate (EF), and methanol (MF), respectively, and the samples were stored at 20 °C until their analysis. Six extracts and fractions were prepared (CE, EE, CF, HF, EF, and MF) and all of them were screened for various potential activities.
## 2.4. Biosynthesis of CNPs
One mL CE and 50 mL of 1 mM aqueous silver nitrate (AgNO3) solution were mixed and kept at room temperature for 8 h to produce Ag nanoparticles following Sathishkumar, et al. ’s methods [21]. On reduction of silver (Ag+) to its reduced form (Ag°), the solution color changes from yellowish to dark. The preparation and stability of synthesized CNPs in sterile distilled water was established with zeta potential, UV-Vis analysis, and transmission electron microscopy (TEM). The CNPs were centrifuged at 10,000 rpm for 30 min. To remove free proteins/enzymes that were not capping the Ag nanoparticles, the pellets were washed three times with DI water, and then dried at 60 °C to remove the free proteins/enzymes [22].
## 2.5.1. Measurement of Zeta Potential and Zeta Size
Zeta potential and zeta size were determined by using Malverns Zetasizer according to the method of Honary and Zahir [23].
## 2.5.2. UV-Vis Spectral Analysis
The CNPs were characterized by widely used UV-Vis spectroscopy (Thermo Electron-Vision pro Software V2.03). The reduction of pure Ag+ to Ag° was scrutinized by optimizing the pH from 4 to 9 and the UV-*Vis spectrum* was prepared by the sampling of aliquots (0.3 mL) of CNPs solutions and further diluting them in DI water up to the 3.0 mL mark. In the range of wavelengths from 100 to 700 nm, UV-*Vis spectra* were analyzed.
## 2.5.3. Morphological Characterization of CNPs
A drop of the prepared samples of the aqueous solution of CNPs was placed on carbon-coated copper grids, the films on the transmission electron microscopy (TEM) (model S-3400-N, Hitachi, Tokyo, Japan), the grids were allowed to stand for 2 min, and the extra solution was separated using a blotting paper for drying the grid. The size distribution of the CNPs were assessed based on TEM micrographs [24].
## Sample Preparation
About 10–50 mg of the CE sample was dissolved in 5 mL CH3OH and sonicated for 45 min while the temperature was kept at 40 °C. Then, the sample mixture was separated out by centrifuge for 10 min at 1000× g. In an amber bottle, the clear supernatant was collected and stored for analysis.
## Total PC Analysis
As described earlier [25], Folin–Ciocalteu reagent was utilized to determine the total PCs of the extracts. A Cary 50 Bio UV-Vis spectrophotometer was used by Varian to measure the samples against a reagent blank at 765 nm. To 0.2 mL of the sample, phenolic Folin–Ciocalteu’s reagent was added (1:1) along with DI water (0.6 mL). After 5 min, saturated Na2CO3 solution ($8\%$w/v in water) was further poured to the mixture, and made up the volume of 3 mL with DI water. The sample mixture was then kept in the dark for 30 min. After centrifuge, the absorbance of blue color samples were measured at 765 nm. The PC content was measured based on the gallic acid equivalents (GAE/g) of dry plant material using the standard curve (5–500 mg/L, $Y = 0.0027$x − 0.0055, R2 = 0.9999). Triplicate measurements were performed for all the analyses.
## Total FC Analysis
The reported aluminum chloride colorimetry was adopted for the quantification of the total FC of the target sample [26]. For this, quercetin-based standard calibration curve was used. Stock quercetin solution was prepared by dissolving 5.0 mg quercetin in 1.0 mL methanol, then the standard solutions of quercetin were prepared by serial dilutions using methanol (5–200 μg/mL). Standard quercetin solutions (5–200 g/mL) were prepared by serial dilutions by dissolving 5.0 mg quercetin in 1.0 mL methanol. The resultant quercetin solutions were individually mixed with 0.6 mL of $2\%$ AlCl3 and incubated for 60 min at room temperature. At 420 nm, we measured the absorbance of the reaction mixtures against a blank. The total FC in the test samples was quantitated from the standard quercetin calibration plot ($Y = 0.0162$x + 0.0044, R2 = 0.999) and stated as the quercetin equivalent (QE/g) of dried plant material. Triplicate quantifications were made for all measurements.
## 2.7. Determination of Antioxidant Capacity as Radical Scavenging Activity Percentage of DPPH and IC50 of Cinnamon Samples
A total antioxidant capacity test was carried out using 50 μg/mL diphenyl-2- picrylhidrazyl (DPPH), then the both maximum wavelength and absorbance were obtained and used as control absorbance. We tested cinnamon samples with concentrations of 20, 40, 60, 80, and 100 μg/mL. Furthermore, vitamin C of concentration 2, 4, 6, 8, and 10 μg/mL were used as the comparative standard. All these standards were (0.5 mL) individually reacted with 3.5 mL of DPPH and based on Spectrophotometry Genesys 30 Vis absorbance readings; this reaction result was noted. Using these absorbance and concentration data, the percentage inhibition (%inhibition) of cinnamon samples and vitamin C were quantitated using the equation, DPPH ∗ scavenging activity (inhibition %) = [(Ac − As)/Ac] × 100 where the absorbance of the DPPH solution is Ac and the absorbance of the samples is As. A linear equation was created by using the %inhibition of cinnamon samples and vitamin C samples. The IC50 levels of cinnamon samples and vitamin C were calculated based on the above linear equation. We collected data based on the results of a DPPH test which were further used to determine total antioxidant capacity. In addition, GraphPad Prism V.0.9 was used in the experimental research data.
## 2.8. Cytotoxicity and Anticancer
American Type Culture Collection (ATCC, Manassas, VA, USA) provided all cell lines used in this study. The study used hepatocellular carcinoma (HepG-2, ATCC HB-8065) and skin fibroblast BJ-1 (ATCC CRL-2522) human cancer cell lines. Corning 75 cm2 u-shaped canted neck cell culture flasks with vent caps (Corning, New York, NY, USA) were utilized to culture cell lines in DMEM/high glucose augmented with $10\%$ FBS, 2 mM L-glutamine, and $1\%$ penicillin/streptomycin. In the following step, sub-on fluent cultures (70–$80\%$) were trypsinized (trypsin $0.05\%$/0.53 mM EDTA) and split according to the seeding ratio [27].
## 2.8.1. Cellular Antioxidant Activity
According to the manufacturer’s instructions, total antioxidant potency of the materials was calculated using a cellular antioxidant assay kit (Abcam ab242300). HepG-2 and Bj-1 cells were seeded in 96-well plates and treated with 25 g/mL for each treatment compared with the respective control. A cell-permeable DCFH-DA fluorescence probe dye was incubated after 24 h of treatment, and the bioflavonoid quercetin served as a control. After 30 min of incubation time, the cells were washed, and a free radical initiator was added to it to initiate the radical generation. The non-fluorescent DCFH-DA was converted to highly fluorescent DCF by the addition of a free radical initiator. The scavenging for free radicals increases with higher antioxidant potency, which inhibits the formation of DCF in a concentration-dependent manner. Thus, in a standard microplate fluorometer, fluorescence is measured over time, and antioxidant values are calculated as follows:The % in vivo antioxidant activity = [(Fc − Fs)/Fc] × 100 where Fc and Fs are the fluorescence of DCF and the sample, respectively. The antioxidant activity was determined by comparing antioxidant values to quercetin within the cell.
## 2.8.2. 3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyltetrazolium Bromide (MTT) Assay for Cell Viability, Proliferation, and Cytotoxicity
The 100 µL of medium/well in 96-well/plates (Hi media) were used for plating the cells (1 × 105/well) into them. Confluence was reached by the cells after 48 h of incubation. Cinnamon samples were then added to RPMI-1640 media containing a variety of concentrations (0.5, 1.0, 2.0, 4.0, 8.0, and 16.0 g/mL). Following removal of the sample solution and washing with phosphate-buffered saline (pH 7.4), 20 L (5.0 mg/mL) of $0.5\%$ MTT phosphate-buffer saline was added to each well. After 4 h of incubation, a mixture solution of 0.04 M HCl/isopropanol was added to the well. Viable cells’ absorbance was determined at 570 nm with reference to 655 nm using a microplate reader manufactured by Bio-Rad, Richmond, (CA, USA) using wells, while the cells containing no samples were considered as blanks. All experimental readings were collected in triplicates. Cinnamon samples were assessed for their effect on cancer cell proliferation using the following formula [28]:Cytotoxicity % = 100 − A570 of treated cells/A570 of control cells × $100\%$.
## 2.9. Determination of Cellular Oxidative Stress Enzymes
In order to evaluate the effect of cinnamon samples on the activity of HepG-2 and BJ-1, cells were seeded in RPMI-1640 medium containing 25 mg/mL of each cinnamon sample, with one 106 cell per flask, and incubated for 48 h at 37 °C under a humidified atmosphere of $5\%$ CO2. After this, the cell medium was converted to serum-free medium (SFM) comprising 10 µL/mL of yogurt extract. Then, trypsin $0.05\%$/0.53 mM EDTA solutions were used to trypsinize the cell cultures after incubation. After washing in PBS, the cells were centrifuged at 2000 rpm for 5 min at 4 °C and resuspended in 1 mL PBS containing $0.1\%$ Triton X-100. Using a 1.5 mL micro centrifuge tube, cells were sonicated twice for 10 s at 100 Hz (Vibra-cell, Sonics & Material) and centrifuged for 30 min at 4 °C at 14,000 rpm. The cells were sonicated for 2 min at 100 Hz in a 1.5 mL micro centrifuge tube placed on ice, then centrifuged at 14,000 rpm for 30 min at 4 °C after the sonication (Vibra-cell, Sonics & Material). Enzyme activity was tested in the supernatant of the tube. We measured the enzyme activity of SOD, CAT, GSH, and GPx in cell culture according to the manufacturer’s instructions for colorimetric kits ab65354, ab83464, ab142044, and ab102530 Abcam.
## 2.10. Determination of p53, Bax, Caspase-3, and Bcl-2 Protein Levels
The levels of the apoptosis markers p53, Bax, caspase-3, and Bcl-2 were determined 24 h after cinnamon doses of 25 g/mL were applied to cells. For 24 h, HepG-2 and Bj-1 cells were seeded at a concentration of 2 × 103 cells/well in 6-well plates. Treatment media were replaced and the cells were incubated for an additional 24 h. After collecting the cells, they were lysed and centrifuged at 4 °C for 20 min at 10,000 rpm. A Bradford protein assay was used to determine the protein concentration in the supernatant [29]. For 1 h in the dark, 50 mg of total protein was incubated with 5 mL of caspase substrate in 100 mL of the reaction buffer. According to the manufacturer’s instructions, caspase-3 activity was assessed using a microplate reader at 405 nm using an Abcam colorimetric assay kit (AB39401) [11]. Following manufacturer instructions (ab207225, ab119506, and ab199080; Abcam), ELISA (enzyme-linked immunosorbent assay simple step) was used to measure the levels of apoptotic markers p53, Bcl-2-associated X (Bax), and B-cell lymphoma-2 (Bcl-2) in cell lysate [30].
## 2.11. Statistical Analysis
The Costal statistical package was used to analyze the data. Results are expressed as mean ± standard deviation (SD). ANOVA was used to verify both the significance of the difference parameters between mean values and the analysis of variance. Different letters within each column indicate significant differences at p ≤ 0.05 as detected by Duncan’s multiple range tests.
## 3.1. Zeta Potential and Zeta Size
Based on Figure 2 and Figure 3, their zeta potential is −12.3 (mV), indicating stability of CNPs with 201 (d.nm) size distributions. Zeta potential is a physicochemical parameter that influences the stability of nanoformulation. When zeta potentials are extremely positive or negative, they cause large repellent forces, whereas when their electric charges are similar, repulsion prevents particle aggregation, which in turn ensures easy redispersion. We performed the DLS in the water medium so it could become hydrated, and the increase the size was due to the hydrophilicity or agglomeration of nanoparticles, which can be seen in the TEM results (6–35 nm) [31]. Dynamic filtration analyzer (DFA) particle with a zeta potential greater than +30 mV or greater than −30 mV is also considered to be stable, as stated by Honary and Zahir [23].
## 3.2. UV-Visible Spectroscopy
Color change and UV-Vis spectroscopy were used to evaluate the synthesis of CNPs. CNPs absorb and scatter light very efficiently. Upon being excited by light at particular wavelengths, conduction electrons on metal surfaces undergo collective oscillations (surface plasmon resonance, SPR) that make them interact strongly with light. Synthesized CNPs exhibit higher absorption and scattering intensities compared with non-plasmonic nanoparticles of the same size due to SPR [32]. Within one hour, the reaction mixture turns yellowish-brown, and after eight hours, it becomes dark brown.
The color is ascribed to the reduction of Ag+ to Ag0 that activates SPR. CNPs solutions have a single sharp SPR band at 405 nm in their absorption spectra (Figure 4). In silver solution, a narrow plasmon absorption band is most prominent between 325 nm and 570 nm. At 405 nm, such a distinct peak could be seen, suggesting silver reduction as reported earlier [33]. In the absorption spectrum of CNPs solutions, there was a surface plasmon absorption band with a maximum of 446 nm, coinciding with the silver plasmon absorption band (325–525 nm).
## 3.3. Electron Microscopy
Figure 5 shows images of CNPs solutions obtained by electron microscopy. The results indicate that NPs adsorb and/or deposit on the surfaces of roughly sphere-shaped polydispersed particles. There are three different shapes of CNPs in the images: spheres, triangles, and irregularities. The spherical nanoparticles with a preferred growth direction along the Ag growth direction can be seen in Figure 5 as a typical example of ring patterns in the selected area electron diffraction. The average size of CNPs ranged from 6.0 to 37.0 nm for C. zeylanicum bark extract.
## 3.4. Polyphenol and Flavonoid Content in Cinnamon Extracts and Their Fractions
Hepatic damage can be effectively treated by applying medicinal plants high in antioxidant compounds [12]. There is a close correlation between the content of phenolic compounds in plant extracts and their antioxidant activity [31]. It has been suggested that flavonoids, which exist naturally in plants, can have positive effects on human health [14]. The anti-inflammatory, anti-cancer, anti-bacterial, and anti-allergic activities of flavonoid derivatives have been demonstrated in various research. In addition to this, flavonoids have also been shown to be highly efficient oxidant scavengers [13]. Table 1 lists the concentrations of polyphenols and flavonoids in CE, EE, and its fractions (HF, CF, EF, and MF). The total polyphenol content was highest in CE (79.43 mg GAE/g) and was followed by CE > EE > MF > EF > CF > HF. A higher concentration of polyphenols was found in both CE and EE, while lower concentrations of polyphenols (4.53 mg GAE/g) were found in HF and CF. There was a decreasing order of flavonoid contents: CE > EE > MF > EF > CF. The highest concentration of total FC (24.63 mg QE/g) was found in CE, while the lowest concentration (0.65 QE/g) was found in CF (Table 1, Figure 6 and Figure 7). The results are in good agreement with those reported by Ervina et al., who mentioned that infusion extracts and fractions of cinnamon can produce varying amounts of polyphenols and flavonoids [34]. Consequently, the yield of infusions and extracts is affected by different preparation methods.
## 3.5. Antioxidant Activity and IC50 of Cinnamon Samples
The absorbance at 517 nm was recorded at each concentration level of cinnamon samples. A spectrophotometer was used to measure the absorbance and calculate the percentage of inhibition (Table 2, Figure 8). The X-axis reflects the concentration of the cinnamon samples and the Y-axis represents the percentage of inhibition. From these data, a linear calibration curve was prepared (Table 3) and the IC50 values for CE, CNPs, EE, CF, EF, and MF samples were noted. Similarly, a standard linear regression curve for vitamin C was constructed, with the X-axis representing vitamin C concentration and the percent inhibition represented by the Y-axis; the linear equations are $Y = 6.934$X + 12.52 and R2 = 0.9988 (Table 3). The IC50 value for standard vitamin C was found to be 5.40 μg/mL. Based on standard curve data for vitamin C, the value was R2 = 0.9988, and R2 for CE, CNPs, EE, CF, EF, and MF were found to be 0.9973, 0.9993, 0.9983, 0.9991, 0.9984, and 0.9965, respectively. All equations showed good linear data, indicating reliable linearity. Cinnamon bark samples and vitamin C were evaluated by calculating the IC50, which suggests their ability to reduce radicals in DPPH by $50\%$. The IC50 value (Table 3 and Figure 8) for vitamin C was 5.4 μg/mL, while for CE, CNPs, EE, CF, EF, and MF samples the values were 64.3, 55.6, 65.68, 70.32, 65.102, and 66.9 μg/mL, respectively. In comparison with vitamin C, cinnamon bark samples exhibit a lower antioxidant capacity based on their IC50 value. CNPs show lower IC50 (49.51 g/mL) than other cinnamon samples, indicating higher antioxidant activity, while CF samples have the highest IC50 (70.32 g/mL) compared to all cinnamon samples, indicating lower antioxidant activity. According to Latief et al. ’s findings, the antioxidant capacity of cinnamon bark extract was 49.0 μg/mL [35]. Additionally, Prahasti et al. reported that cinnamon bark extract had a 193.139 mg/mL antioxidant capacity [36]. Based on the results and discussion of the reported research, it can be decided that cinnamon bark extract has a strong total antioxidant capacity with IC50 value of 64.3 µg/mL. There are numerous constituents of cinnamon bark extract, including phenolics, alkaloids, tannins, saponins, flavonoids, glycosides, terpenoids, quinones, coumarins, cardiac glycosides, and betacyanides.
## 3.6. Cellular Antioxidant Activity
At 25µg/mL (below IC50), CE, CNPs, and its fractions (CF, EF, and MF) were assessed for their potential to scavenge DPPH radicals either within or outside cells (Bj-1 and HepG-2). The data were expressed as scavenging activity percentages. As shown in Table 4, CNPs outside the cells displayed higher scavenging activity against DPPH radicals compared with other investigated samples (CE, EE, EF, MF, CF) and followed the decreasing order (23.561 ± 2.11 > 22.64 ± 2.10 > 21.33 ± 2.65 > 20.67 ± 1.62 > 19.84 ± 1.33 > 19.029 ± 1.34), respectively. The lowest antioxidant activity was showed by the CF sample (19.029 ±1.34). All other samples including CE, EE, or CNPs outside the cells showed higher anti-oxidant activities compared with cinnamon fractions (CF, EF, MF), Table 4.
As compared with Bj-1 normal cells treated with CE, CNPs, or cinnamon fractions, cellular antioxidant activity increased by $12.46\%$ and $7.0\%$, respectively, in cells treated with CE or CNPs compared with related control samples (untreated cells). In Bj-1 cells, after 24 h, the highest cellular antioxidant activity ($35.43\%$) was recorded when treated with CNPs, followed by CE, which achieved an antioxidant activity of 29.54 percent, while CF treatment displayed the lowest antioxidant activity ($24.93\%$) compared with the control and other samples Table 4.
According to previous outside antioxidant data, CNP treatment increased cellular antioxidant activity more than other cinnamon samples; this may be due to its higher solubility and bioavailability in aqueous cellular environments [16]. Additionally, the IC50 value of CNPs showed lower value (55.60) than that of other cinnamon samples, as shown in Table 3, which indicates that the antioxidant capacity is inversely proportional to the IC50 value. The higher contents of polyphenols and flavonoids in cinnamon extracts (Table 1 and Figure 6 and Figure 7) might increase the antioxidant activity compared with cinnamon fractions. We have found that CE and EE contain the highest concentrations of polyphenols and flavonoids, whereas CF contains the lowest amount of flavonoids. This indicates that polyphenol and flavonoid contents are responsible for increasing the antioxidant activity [37]. The results of this study suggest that the standard preparation method for obtaining infusions (i.e., CE), which is heating cinnamon bark at 90 oC for 20 min, is sufficient and results in significant antioxidant activity, and the conversion of CE to CNPs increases cinnamon’s antioxidant power owing to its greater solubility and bioavailability in living cells than that of other cinnamon samples [16].
These results also indicated that the CE, CNPs, EE, and cinnamon fractions are compounds with substantial antioxidant activity and they are from the class of polyphenols including phenolic volatile oil, flavonoids, and tannin. As Brewer [38] noted, phenolic compounds from plants have demonstrated antioxidant activity in general. These compounds are comprised of phenolic acids (gallic acid, caffeic acid, protocatechuic acid, and rosmarinic acid), phenolic diterpenes (carnosol, rosmanol carnosic acid, and rosmadial), and phenolic volatile oils (eugenol carvacrol, thymol, and menthol), as well as polyphenols such as flavones, flavonols, isoflavones, catechins, and tannins [39].
## 3.7. Cytotoxicity and Cell Viability Percent
There is a growing interest in establishing novel and effective treatment models for cancers such as hepatocellular carcinoma by exploiting the cytotoxic properties of natural compounds [40]. According to the results of the present study, CNPs, CE, and its fractions exhibit dose-dependent cytotoxicity on Bj-1 and HepG-2 cancerous cells (Table 5, Figure 9). These effects had higher potencies on HepG-2 cancerous cells than on Bj-1 normal cells. In the same respect, the anti-proliferative potency of CNPs and CE on Bj.1 or HepG-2 cells at different concentration levels was more effective than that of other cinnamon samples or fractions. With the higher concentration (16 g/mL), CNPs showed higher cell mortality ($25.68\%$) of Bj-1 normal cells than that of HepG-2 cells ($29.49\%$), while CF samples showed lower cell mortality of $15.1\%$ and $16.2\%$ for Bj-1 and HepG-2 cells, respectively, when compared with other cinnamon samples at this concentration. The CE sample showed higher cytotoxic effects on HepG-2 cancerous cells at different concentrations than EE or CF, EF, and, MF at the same concentration levels. These results indicate that CNPs and CE have powerful cytotoxicity against HepG-2 cancerous cells than against other cinnamon samples, and also increase the viability of Bj-1 normal cells at the lowest concentrations (0.5 and 1.0 µg/m) due to the higher content of polyphenols and flavonoids (Table 1, Figure 6 and Figure 7). Our results indicate that plants with high polyphenol and flavonoid contents have enhanced antioxidant activity, decreased IC50, and decreased cytotoxicity on cells.
## 3.8. Effects on Oxidative Stress Enzymes
CE, CNPs, EE, and cinnamon fractions (CF, EF, and MF) influence cellular antioxidant enzyme activity. In cell lysates of Bj-1 and HepG-2 after treatment with 25 µg/mL of cinnamon samples, the activity of SOD, reduced GSH, CAT, GPx, and glutathione-s-transferase (GST) was measured.
As compared with untreated cells, CE and CNPs significantly increased SOD, CAT, GSH, GPx, and GST levels in Bj-1 normal cells after 48 h (Table 6, Figure 10). A significant decrease in the enzymes’ activity was noticed with the CF compared with control, or other cinnamon samples treatment, while Bj-1 enzyme activities were not significantly altered by EE, EF, or MF samples.
The data in Table 6 show a significant increase ($p \leq 0.05$) in antioxidant enzyme biomarkers of Bj-1 cells under the influence of CNPs compared with other tested cinnamon samples. A significant increase in antioxidative enzymes was observed in HepG-2 cancerous cells treated with cinnamon extracts, CNPs, and cinnamon fractions, but CF treatment significantly decreased oxidative enzymes. CNPs showed significant increases in antioxidant enzymes in normal and cancer cells compared with control and other cinnamon samples. The findings suggest that CNPs have a powerful antioxidant effect and have the ability to reduce levels of oxidative stress in the cells, since they are more soluble and bioavailable [16].
On the other hand, several mechanisms are accountable for cinnamon’s effect on free radicals. By scavenging free radicals such as ROS and reactive nitrogen species (RON), it modulates the activity of catalase, GSH, and SOD that neutralize free radicals, and inhibits ROS-generating enzymes, including xanthine hydrogenase/oxidase and lipoxygenase/cyclooxygenase [41]. Cinnamon’s hydrophilic properties make it a good scavenger of peroxyl radicals, just like vitamin C [42].
## 3.9. Bj-1 and HepG-2 Treated Cell’s Poptotic Marker Protein Levels
In the present study, we have investigated the spectrum and modes-of-action of cinnamon samples against HepG-2 cancer cells, as well as compared their effects on Bj-1 normal cells. Among the six cinnamon samples, CNPs, CE, EE, and fractions (CF, EF, and MF) generated, only CNPs, CE, EE, and MF cinnamon bark fractions soluble in water exhibited potent anti-cancer activities as indicated by MTT cytotoxicity tests (Table 5). Apoptosis plays an important role in the development and health of a multicellular organism. In apoptosis, cells die in a controlled and regulated manner. Apoptosis has been shown to be a major pathway through which many medicinal and non-medicinal plants mediate anti-cancer effects. Apoptosis is regulated by two major pathways (intrinsic and extrinsic). As a result of both of these pathways, caspases enzymes act as death effector molecules in numerous types of cell death and converge to form a common pathway [27]. Generally, two types of caspases are involved in the regulation and execution of apoptosis: initiator caspases, which include caspases 2, 8, 9, and 10, and effector caspases, which include caspases 3, 6, and 7 [43].
Apoptosis is mediated by a number of genes besides caspases. The Bcl-2 family of proteins, including proapoptotic Bax and anti-apoptotic Bcl-2, regulate intrinsic pathways of apoptosis [40,41]. Apoptosis is regulated by proapoptotic Bax, which activates caspase initiators. Bax is one of the members of the Bcl-2 family that initiate apoptosis through the p53 gene, which functions as a tumor suppressor gene. The protein contents of p53, Bax, Bcl2, and Caspase-3 were calculated in Bj-1 and HepG-2 cells exposed to CE 25µg/mL, CNPs, EE, CF, EF, and ME (Table 7). No significant changes were observed on Caspas-3, P53, Bax, and Bcl-2 of Bj-1 normal cells due to CE or CNPs treatments, whereas EE, CF, EF, and MF treatments significantly decreased these apoptosis marker protein levels compared with control. On the other hand, HepG-2 cancerous cells treated with CE, CNPs, EE, or cinnamon fractions (CF, EF, MF) significantly increased Caspase-3, Bax, and P53 compared with control. However, all cinnamon samples significantly decreased Bcl-2 marker protein, except the CF sample, which showed no significant changes compared with control. Additionally, the data in the present study showed significant upregulation of pro-apoptotic caspase3, Bax, and p53 and down regulation of the anti-apoptotic BcL2 protein in HepG-2 cells treated with CNPs or CE after 24 h of incubation. CNPs treatment to HepG-2 exhibited highly significant changes in caspase-3, Bax, P53, and Bcl-2 levels compared with control or other treatments. According to the results obtained, CNPs and CE mediate their anti-cancer effects through apoptosis.
Several signal transduction pathways are reported to be involved in CNP’s potent anti-cancer activity, including pro-apoptotic (caspase3, P53, and Bax) and anti-apoptotic (Bcl-2). Based on Kerr et al. ‘s findings, apoptosis is inversely associated with tumor progression, hyperplasia, and the formation of abnormal cells [44]. Many cancer cells are affected by cinnamon extract and its active compounds. In an in vivo melanoma model and in B16F10 cells, CE stimulated caspase-3 activity. However, the level of Bcl-2 was significantly decreased [45]. According to Sadeghi et al., cinnamon has an array of pharmacological properties, including antimicrobial, antioxidant, and anti-cancer properties [46]. Cancer is triggered and progressed by impaired apoptosis. There is increasing evidence that cinnamon, as a therapeutic agent, inhibits cancer cells by upregulating caspase3, P53, and Bax proteins and downregulating Bcl-2 proteins in apoptosis-related pathways.
## 3.10. Cellular Mechanism and Comparison of Works with Reported Literatures
In our earlier research study [43], the apoptosis related mechanism was discussed where the proteolytic activation of caspase-3 leads to DNA fragmentation and phosphatidylserine exposure causes degradation of nuclear protein and plasma membrane reversion [47]. The effects of plant-derived phytochemicals were suggested to be mediated by the induction of cell cycle arrest as well as apoptosis [28].
Ervina et al. [ 48] reported the antioxidant activity of Indonesian cinnamon bark for antioxidant activity. They reported that cinnamon bark infusion possesses the highest antioxidant activity, with IC50 value of 3.03. Additionally, the phytochemical analysis results indicated that polyphenol and phenolic volatile oil are the major antioxidant compounds. Ewyes at al. [ 49] reported in their research how fermentation effects the antioxidant and anti-cancer properties of Cinnamomum cassia. However, the authors do not report the preparation of nanoparticles and their application for various biological activities.
## 4. Conclusions
In the present study, CNPs, CE, and its fractions exhibit dose-dependent cytotoxicity on Bj-1 normal cells and HepG-2 cancer cells. Using CNPs significantly enhanced the anti-cancer and antioxidant activities of cinnamon samples on normal cells (Bj-1) or cancer cells (HepG-2), as they are more soluble and bioavailable. Additionally, as compared with control samples, all cinnamon samples increased the Caspase-3, Bax, and P53 levels, while decreasing Bcl-2 levels. Synthesized CNPs were found to be more effective as antioxidants and anti-cancer agents than other tested cinnamon samples. All these outcomes indicate that cinnamon has anti-cancer effects in cancer cells via apoptosis-related pathways by up- and down-regulating multiple proteins.
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|
---
title: The Effect of Omega-9 on Bone Viscoelasticity and Strength in an Ovariectomized
Diet-Fed Murine Model
authors:
- Mahmoud Omer
- Christopher Ngo
- Hessein Ali
- Nina Orlovskaya
- Vee San Cheong
- Amelia Ballesteros
- Michael Tyrel Garner
- Austin Wynn
- Kari Martyniak
- Fei Wei
- Boyce E. Collins
- Sergey N. Yarmolenko
- Jackson Asiatico
- Michael Kinzel
- Ranajay Ghosh
- Teerin Meckmongkol
- Ashley Calder
- Naima Dahir
- Timothy A. Gilbertson
- Jagannathan Sankar
- Melanie Coathup
journal: Nutrients
year: 2023
pmcid: PMC10005705
doi: 10.3390/nu15051209
license: CC BY 4.0
---
# The Effect of Omega-9 on Bone Viscoelasticity and Strength in an Ovariectomized Diet-Fed Murine Model
## Abstract
Few studies have investigated the effect of a monosaturated diet high in ω-9 on osteoporosis. We hypothesized that omega-9 (ω-9) protects ovariectomized (OVX) mice from a decline in bone microarchitecture, tissue loss, and mechanical strength, thereby serving as a modifiable dietary intervention against osteoporotic deterioration. Female C57BL/6J mice were assigned to sham-ovariectomy, ovariectomy, or ovariectomy + estradiol treatment prior to switching their feed to a diet high in ω-9 for 12 weeks. Tibiae were evaluated using DMA, 3-point-bending, histomorphometry, and microCT. A significant decrease in lean mass ($$p \leq 0.05$$), tibial area ($$p \leq 0.009$$), and cross-sectional moment of inertia ($$p \leq 0.028$$) was measured in OVX mice compared to the control. A trend was seen where OVX bone displayed increased elastic modulus, ductility, storage modulus, and loss modulus, suggesting the ω-9 diet paradoxically increased both stiffness and viscosity. This implies beneficial alterations on the macro-structural, and micro-tissue level in OVX bone, potentially decreasing the fracture risk. Supporting this, no significant differences in ultimate, fracture, and yield stresses were measured. A diet high in ω-9 did not prevent microarchitectural deterioration, nevertheless, healthy tibial strength and resistance to fracture was maintained via mechanisms independent of bone structure/shape. Further investigation of ω-9 as a therapeutic in osteoporosis is warranted.
## 1. Introduction
Osteoporosis is a systemic, metabolic disease that progresses via gradual deterioration of the macro- and micro-architecture of skeletal tissue where the interconnecting porous system is slowly resorbed and bone mass is reduced [1,2]. As this occurs, bone porosity increases and the pores become enlarged with diversification in shape, contributing to the most important clinical complication, which is an increasingly fragile structure predisposed to low-energy insufficiency fractures [3]. It is estimated that more than 9.9 million Americans have osteoporosis and an additional 43.1 million live with low bone density [4]. As such, osteoporosis affects a substantial number of people [5], and carries a significant economic burden where the main cost driver is considered to be fracture-related treatment and associated surgical costs [6]. In the United States, ~USD 5–6.5 trillion per year is spent on disability and loss of productivity (not counting indirect costs) for the targeted treatment of osteoporosis [6]. Although most common in postmenopausal women, age-related osteoporosis is inevitable in both men and women [7,8]. In many of these cases, patients experience a significant decrease in quality of life and an increased cumulative mortality [4]. For example, hip fractures are associated with 20–$40\%$ excess mortality within 1 year post-fracture [9,10], with a higher mortality in older individuals [11], and in men (one in seven following non-hip fracture; one in three following hip fracture) more so than women (one in 11 following non-hip fracture; one in five following hip fracture) [12,13]. The greatest reduction in survival occurs within the first year post-fracture, however, a fragility fracture occurring at any fracture site, has also been associated with reduced patient survival for up to six years post-fracture [13], and even longer [14]. Patients who experienced hip fractures have a ~2.7-fold increased risk of future fractures [15,16], increasing at a rate of $4\%$ for each year of age. The risk for fracture is $41\%$ higher in women than men [16], with women ≥ 75 years of age experiencing a 2 year fracture risk of $25\%$ [17]. Approximately $20\%$ of hip fracture patients require long-term nursing home care, and only 40–$60\%$ fully regain their pre-fracture level of function, mobility, and independence [18]. Given that populations in developed nations are rapidly aging, hence an increase in the incidence of osteoporosis is inevitable. Together, these statistics indicate a clear need for improved mitigation and treatment strategies.
Several approaches for preventing osteoporosis are currently recommended. These include adopting a diet with a daily intake high in calcium-rich foods (1000 mg/day for men aged 50–70 years, and 1200 mg/day for women <51 years of age) and vitamin D (800–1000 IU/day), especially low-fat and pasteurized dairy products, fiber, protein-rich foods (e.g., meat), and regular weight-bearing and muscle-strengthening exercise [19,20,21]. The primary molecular and cellular processes involved in bone metabolism and turnover are significantly influenced by nutrients, such as fats, sugars, and proteins [22]. Although the pathogenesis of osteoporosis is multifactorial, there is emerging evidence that an unhealthy diet increases the risk of postmenopausal osteoporosis, whereas a healthy diet may decrease its occurrence [21,23]. The effect of diets high in polyunsaturated fats (PUFAs), in particular omega-3 (ω-3), and ω-6, have been widely investigated in pre-clinical and clinical studies. Results suggest that diets high in PUFA significantly increase the peak load, bone stiffness, and bone strength [24,25,26], in addition to bone mineral content (BMC), and bone mineral density (BMD) [27,28]. Diets high in ω-3 fatty acids are typically understood to support bone health, and have been associated with a reduced bone fracture risk [29,30]. However, the influence of an enriched ω-6 fatty acid diet remains inconclusive. While an increase in BMD [31,32], and a decrease in fracture risk have been reported [30,33], studies have also reported an increase in fracture risk [34,35], or no effect was measured [36]. Few studies have investigated the role of monosaturated (MUFA) ω-9 fatty acids in bone health. A recent study by our group [23], demonstrated that a 50:50 mix (saturated: unsaturated) diet high in ω-9, and relatively low in ω-3, delivered an anabolic response to bone. Our results demonstrated significantly increased bone strength and architecture when compared to the control, and also ω-6 fed animals following investigation in an 8-week high-fat diet-fed male murine model. Although all of the high-fat diets investigated in this study induced similar levels of obesity and loading in the animals, the bone response to diet varied, and the mechanism/s behind this specific ω-9 bony response remains elusive.
Due to the ω-9-induced anabolic response measured in our previous study, here we investigated whether a high-fat diet rich in ω-9, served as a modifiable dietary intervention able to protect against the progression of post-menopausal osteoporosis. We hypothesized that a diet enriched with ω-9, and with relatively low levels of ω-3 and ω-6, would protect against a progressive osteoporotic decrease in architectural bone loss, and mechanical strength when investigated in a murine ovariectomy (OVX) model. Results were compared to OVX animals that received ‘gold standard’ estrogen supplementation. Skeletal parameters were quantified both mechanically and histologically.
## 2. Materials and Methods
All procedures were approved by the Institutional Animal Care and Use Committee at the University of Central Florida and were performed in accordance with the American Veterinary Medical Association guidelines. Female 8-week-old C57BL/6J “wild-type” mice were purchased from The Jackson Laboratory (Bar Harbor, ME, USA) and allowed to acclimatize for 2 weeks prior to ovariectomy. During this time, the mice were maintained on a 12:12-h light-dark schedule and given ad libitum access to an introductory, purified control diet (D07020902, consisting of $10\%$ fat (Research Diets, Inc, New Brunswick, NJ, USA) and water. Following this two week period, mice were randomized into the experimental groups ($$n = 6$$), and bilateral ovariectomy surgery was performed.
## 2.1. Ovariectomy and Dietary Intervention
Animals were anesthetized using $2\%$ isoflurane prior to bilateral ovariectomy. Mice received subcutaneous prophylactic analgesia (carprofen, 20 mg/kg) before, and 12 h following surgical intervention. Female mice in the control group received analgesia and anesthesia, and underwent dorsal skin incision, and suturing similar to OVX animals, but without removal of the ovaries [37]. Mice were allowed to recover for 1 week post-surgery. To serve as a positive study control, and beginning on the first day following OVX, mice received either an intrascapular s.c. injection of 2 µg of 17β-estradiol benzoate (Sigma, St. Louis, MO, USA) dissolved in 0.1 mL sesame oil (OVX + E2), or the vehicle (sesame oil) only (OVX group). This hormonal regimen mimics the physiological range of estradiol (E2) levels found in a young adult female mouse [37]. Hormone treatment was repeated every 4 days and lasted the duration of the study. Thus, the study comprised of 3 experimental groups, namely; group 1: sham control, group 2: OVX; and group 3: OVX + E2.
Beginning 2 weeks post ovariectomy, the animal feed was switched to a balanced saturated: unsaturated diet high in ω-9 (D12492; Research Diets, Inc, New Brunswick, NJ, USA) and ad libitum over the 12-week study period (Table 1). This diet provided 60 kcal% energy from fat. Fat ingredients contained soybean oil (~$13\%$ ω-3, ~$55\%$ ω-6 and $18\%$ ω-9) and lard ($0\%$ ω-3, ~6–$10\%$ ω-6 and ~44–$47\%$ ω-9) only, introducing ω-3, ω-6 PUFA, and ω-9 MUFAs, as well as a range of saturated fatty acids, including stearic acid, palmitic acid, oleic acid, and linoleic acid (a full description is presented in Table 2). The mean kcal fraction of each component of the diet with respect to the total kcal was calculated. The amount of ω-3, ω-6, and ω-9 was estimated (g) to determine the % contribution of ω-3, ω-6, and ω-9 within the diet.
## 2.2. Measurement of Body Weight and Body Composition
Mean body weight (g) and gain in bodyweight (g) were quantified following the introductory control diet period, and immediately prior to switching to the HFD. Weight was then measured weekly over the 12-week study period. Body composition (lean mass, and fat mass) was also measured at the end of the introductory control diet period, and prior to the mice starting the HFD diet. A Bruker minisoec LF-50 body composition analyzer (Billerica, MA, USA) was used to quantify the body composition, where data were collected immediately prior to HFD feeding, and immediately following completion of the study. Lean body mass was calculated as the difference between total body weight and body fat weight, (e.g., organs, skin, bones, body water, muscle mass).
Mice were euthanized 12 weeks post-feed intervention, and the left and right tibia collected. All of the right tibiae were immediately plastic wrapped and stored frozen at −20 °C in preparation for, (i) dynamic mechanical testing (DMA) (storage modulus (E’), loss modulus (E’’) and loss tangent (δ)), (ii) 3-point bending analysis (ultimate stress (σu), fracture stress (σf), yield stress (σy) and elastic modulus (E)), (iii) tibial length measurement (mm) and bone area (mm2), (iv) cross-sectional moment of inertia (mm4), and, (v) micro-computed tomography (microCT) scanning (BMD (g.cm3), BMC (g), and BV/TV%). All mechanical testing was carried out within 2 weeks of tissue retrieval. Following dissection, all left tibiae were immediately placed in $10\%$ buffered formaldehyde and processed for undecalcified histology. To evaluate the effect of diet alone in comparison with hormonal treatment on bone loss, samples were qualitatively assessed following toluidine blue (soft tissue) and paragon (bone) staining. The trabecular area (%), was measured in each of the groups.
## 2.3. Dynamic Mechanical Analysis
Each tibia was analyzed under dynamic loading conditions and assessed using a dynamic materials analyzer (DMA) 242E (Artemis, Netzsch, Selb, Germany). Prior to testing, the tibiae were thawed and immersed in phosphate buffered saline (PBS) for at least 1 h at room temperature. Tibiae were orientated face down, such that the posterior aspect was subjected to three-point bending loads while under isothermal conditions. To prevent rigid body motion, 3D printed support fixtures were positioned at the proximal and distal ends of the bone, and separated at a distance of 8 mm. The central region of the (posterior) tibial midshaft, was loaded at frequencies of 0.05, 0.1, 1.0, and 10.0 Hz, and under the elastic limit of the bone. A sinewave form with a constant stress amplitude of 0.25 MPa, with a maximum (1 MPa) and minimum (0.5 MPa) stress was applied. Specimens were tested at room temperature for 60 min and the viscoelastic properties of each tibia (storage modulus E’, loss modulus E’’ and loss tangent (δ)) were obtained and results compared between the three experimental groups ($$n = 6$$). The storage modulus (E’) indicates how the specimen stores elastic energy while the loss modulus (E’’) is a measure of energy dissipation. The loss tangent (δ) is the phase angle between stress and strain and relates to damping. These parameters characterize the viscoelastic mechanical properties of the tibiae.
## 2.4. Three-Point Bending
Each tibia was loaded to failure at a displacement rate of 0.015 mm/s using a 50 N load cell, and universal testing machine (Criterion® 43, Eden Prairie, MTS, Minnesota, USA). The tibiae were positioned on the fixture to lie perpendicular to the applied load, and with the anterior surface facing upwards. Fixtures were used to prevent rigid body motion, and the support bars were separated at a distance of 6 mm. Using a 3 mm diameter loading roller, a vertical force was applied to the tibial mid-shaft until structural failure. Load-displacement curves were then obtained. As an animal model was being investigated, it was acknowledged that the cross-sectional area of the tibia was non-uniform. Similar to other previously published studies [38,39], we assumed that the cross-sectional area was circular and calculated the mechanical properties including the stress, σ (Pa), in Equation [1], and elastic modulus, E (Pa), in Equation [2]. [ 1]σ=F∗L∗co4∗I [2]E=FL3d∗48∗I where F is the applied load (N), $L = 0.006$ is the span distance between the supports (m), co is the outer radius of the tibia’s midshaft (m), which was measured using a calliper (Digital, Cole-Parmer, IL, U.S.), d is displacement (m) and I is the moment of inertia (m4) calculated using Equation [3]:[3]I=π4co4−ci4 where ci is the inner radius of the tibia’s midshaft (m). The inner radius of each midshaft was calculated using data obtained from the μCT scans (Figure 1C). The inner and outer diameter were calculated from four differing μCT cross-sectional regions from one bone from each experimental group. The mean values of inner-to-outer diameters from the four different cross-sections were calculated to determine the inner radius of the remaining tibia in each group. Bone area A (m2) was calculated using Equation [4]: [4]A=π4co4−ci4 The yield point was determined using an offset of 0.015 mm parallel to the linear portion at the beginning of the load-displacement plot [39]. The post yield displacement (PYD) was obtained as the displacement from the yield point to the fracture point in the load-displacement plot. Yield stress (σy), ultimate stress (σu), fracture stress (σf), elastic modulus (E), and cross-sectional moment of inertia (m4), were calculated. As changes in mouse bodyweight were clear, the mechanical strength parameters were adjusted for body size (ratio of body weight to tibial length) [40] ($$n = 6$$).
## 2.5. Histological Analysis
Following euthanasia, tibiae ($$n = 6$$) were prepared for undecalcified histological analysis. Samples were dehydrated in serial dilutions of alcohol, defatted, and embedded in hard grade acrylic resin (LR White, Electron Microscopy Sciences, Hatfield, UK). Using cutting and grinding techniques, longitudinal thin sections (~60 µm) were prepared through the proximal cancellous region of each tibia using a 300CP and 400CS EXAKT system (EXAKT, Germany). Sections were subsequently stained with Toluidine Blue and Paragon, which stained the soft tissue and bone, respectively. Using light microscopy and image analysis (×10 objective lens), the % trabecular bone area was measured. Five random areas (×10 objective lens) in the region immediately beneath the growth plate were imaged and % bone area was calculated using ImageJ software. Data were quantified and compared between each of the experimental groups.
## 2.6. Scanning Electron Microscopy
To assess bone viability via the presence of osteocytes located within the lacunae, scanning electron microscopy (SEM) (Jeol, Akishima, Japan; Zeiss, Jena, Germany; Tescan, Brno, Czechia) was used to image the bone. Samples were prepared for imaging by firstly polishing the samples, and then acid-etching the surface using $9\%$ phosphoric acid for 20 s. The samples were then washed in distilled water and $5\%$ sodium hypochlorite, prior to further washing in distilled water, dried overnight, and sputter coated prior to observation under SEM.
## 2.7. MicroCT
Similar to our previous study [23], one tibia ($$n = 1$$) from each of the experimental groups was thawed in a 15 mL Eppendorf tube. MicroCT scanning was performed using a cone beam scanner (GE Phoenix Nanotom-MTM, Waygate Technologies, Hürth, Germany) using a 90 kV source voltage, 110 μA source current (mode 0), and a tungsten-diamond target with a 500 ms exposure time at 7–9 μm isotropic voxel resolution (depending on tibial size). Data were collected for 1080 projections over 360° (0.33° steps) with three averaged images per rotation position. The volume reconstructions were performed using Phoenix Datos software. Visualization and production of DICOM images were created using VG Studio Max (v 2.1) software. Using MATLAB 2018A (The MathWorks Inc., Natick, MA, USA), a volume of interest (VOI) was selected immediately beneath the growth plate. Bone mineral density (BMD), bone mineral content (BMC), and bone volume to total volume fraction (BV/TV%) were calculated in the anteroposterior and medio-lateral sectors in 1 (proximal)–7 (tibio-fibular joint) equi-distant regions at $80\%$ of the tibial length below the growth plate, along the tibia in each group (Figure 1A,B) [41]. Density expressed as mg/cm3 of hydroxyapatite was determined using calibration phantoms of 0, 50, 200, 800, and 1200 mgHA/cm3. Three-dimensional Slicer software (v4.11.20210226; Brigham and Women’s Hospital and Massachusetts Institute of Technology, Boston, MA, USA) was used to create 3D models of the trabecular network within the proximal tibia. As $$n = 1$$, the analysis was conducted by treating all of the longitudinal sections as individual data points (Supplementary Tables S10–S13).
## 2.8. Statistical Analysis
Analysis of the data was performed using SPSS software (v25; SPSS, Chicago, IL, USA). Data obtained were nonparametric and the Mann–Whitney U-test was used for statistical comparison between experimental groups. The Kruskal–Wallis test with a post hoc Mann–Whitney U-test was used to compare data longitudinally and over the different time points within one experimental group. p values < 0.05 were considered significant. Means are presented with standard error (SE) values.
## 3.1. Body Weight and Body Weight Gain
All animals remained healthy throughout the duration of the study. The mean body weight per animal and weight gain in each of the groups and over the 12-week study duration is shown in Figure 2. Starting body weight, final body weight, and total cumulative gain in body weight are presented in Table 3.
The mean body weight of animals in the control and OVX + E2 groups, gradually and similarly, increased over time. However, a significant increase in body weight and an overall gain in weight was observed in OVX animals throughout the 12-week period, when compared with both the control and OVX +E2 groups (detailed results are presented in Table 4). The mean values calculated for body weight and weight gain (g) in each group, and over each week of the study, are presented in Supplementary Tables S1 and S2.
## 3.2. Body Composition
Our findings revealed that a significant increase in lean mass was measured in each of the groups when week 1 data was longitudinally compared with week 12 ($p \leq 0.01$ in all groups) (Figure 2C). At the beginning of the study (week 1), no significant differences in lean mass were measured when each of the three experimental groups were compared. However, by week 12, a significant increase in lean mass was measured in the OVX E2 group (19.55 ± 0.30 g) when compared with the sham control (18.23 ± 0.22 g) and OVX (17.98 ± 0.41 g) groups ($p \leq 0.05$ in both cases). Further, significantly increased lean mass was measured in the control group when compared to the OVX animals ($p \leq 0.05$) at this 12-week time point. When levels of fat mass were examined, no significant difference in adiposity was found when each of the groups were compared at the beginning of the study. However, results demonstrated that adiposity significantly increased in animals in all three groups when week 1 was longitudinally compared with week 12 (Figure 2D). By week 12, a significant increase in fat mass was measured in the OVX group (26.22 ± 0.28 g), when compared to the OVX + E2 (3.29 ± 0.27 g) and control (9.33 ± 1.98 g) groups. As such, fat mass was measured to significantly increase ~10-fold in OVX animals when compared to the OVX + E2 group ($p \leq 0.01$) and ~2.5-fold when compared to control animals ($p \leq 0.01$). Finally, significantly decreased adiposity was measured in the OVX + E2 group of animals when compared to the control group ($p \leq 0.05$) at the 12-week timepoint. No other significant differences between lean and fat mass in each group were found.
## 3.3. Dynamic Mechanical Analysis (DMA)
When storage modulus E’ (elastic energy) was examined in each group, and at increasing frequencies of 0.05, 0.1, 1.0, and 10.0 Hz, a trend was seen where E’ increased with increasing frequency (Figure 3A). It is generally accepted that within the lower frequency range (e.g., 0.05 Hz), there is greater movement of the organic component of bone, with less movement of this fraction as the frequency increases. Although a trend of a higher E’, and thus elastic component was observed in the OVX group at each frequency (0.05 Hz = 1.72 ± 0.22 MPa; 0.1 Hz = 1.98 ± 0.25 MPa; 1.0 Hz = 2.23 ± 0.24 MPa; and 10.0 Hz = 2.69 ± 0.33 MPa), no significant differences were found when compared to the control (0.05 Hz = 1.36 ± 0.18 MPa; 0.1 Hz = 1.44 ± 0.14 MPa; 1.0 Hz = 1.79 ± 0.18 MPa; and 10.0 Hz = 2.19 ± 0.23 MPa) and OVX + E2 (0.05 Hz = 1.25 ± 0.09 MPa; 0.1 Hz = 1.61 ± 0.10 MPa; 1.0 Hz = 1.84 ± 0.12 MPa; and 10 Hz = 2.17 ± 0.13 MPa) groups. Similarly, the loss modulus (energy dissipation) measured in the OVX group was higher when compared with the control and OVX + E2 groups (except at 0.05 Hz), however, no significant differences were found (Figure 3B). Changes in the loss tangent (damping) were also observed between groups (Figure 3C). At the lower frequency and stress levels of 0.05 Hz, the loss tangent reduced in the OVX group (0.15 ± 0.04) compared to the control (0.21 ± 0.04) and OVX + E2 (0.22 ± 0.02) groups. However, when investigated at 1.0 Hz, the lowest loss tangent was measured in the OVX + E2 group (0.09 ± 0.03), when compared with the control (0.16 ± 0.01) and OVX samples (0.13 ± 0.03). At the higher frequency of 10 Hz, the loss modulus was found to be similar in each of the groups. Results demonstrated that on the nano- and micro-scale, bone tissue in the OVX + E2 group possessed a greater viscous component when compared to the OVX group. Moreover, in the OVX group, the stiffer ‘solid’ component of bone appeared more pronounced (Figure 3D).
## 3.4. Tibial Structural Parameters
Analysis of the structural morphometric parameters measured following the 12-week study period in the control, OVX, and OVX + E2 groups are presented in Table 5. Our results showed that total bone area was significantly lower in the OVX group (0.84 ± 0.01 mm2) when compared with the control group (1.10 ± 0.04 mm2, $$p \leq 0.009$$). No significant difference was found when the OVX + E2 (1.13 ± 0.26 × 10−3 mm2) and control groups were compared. A trend was seen where the mean outer diameter decreased in the OVX group (1.37 ± 0.014 mm) when compared with the control group (1.46 ± 0.032 mm, $$p \leq 0.014$$) and OVX + E2 group (1.39 ± 0.075 mm), however, no significant differences were found. The decrease in cortical thickness observed in the OVX group was further confirmed as the inner diameter was also significantly highest in this group (0.90 ± 0.009 mm) when compared to the OVX + E2 group (0.72 ± 0.03 mm, $$p \leq 0.009$$). A significantly decreased inner diameter was measured in the OVX + E2 group when compared with the control group (0.86 ± 0.19 mm, $$p \leq 0.028$$), suggesting the cortices were thickest in the OVX + E2 group when compared with all other groups. The cross-sectional moment of inertia was lowest in the OVX group (0.14 ± 0.006 mm4), and significantly lower than the control group (0.20 ± 0.016 mm4, $$p \leq 0.028$$).
## 3.5. Three-Point-Bending
Following the dynamic loading of the tibiae under three-point bending and until failure, the mechanical properties of yield stress σy, ultimate stress σu, strength σf, and Young’s modulus E, varied between tibiae in each of the experimental groups. Following normalization of data to body size (ratio of body weight to tibial length), a trend was seen where animals in the OVX group displayed lower fracture, and ultimate stresses when compared to OVX + E2 animals. However, no significant differences were found (Figure 4). Notably, fracture strength was highest in the OVX + E2 group of animals (130.66 ± 13.16 MPa), and lowest in the OVX group (121.63 ± 10.05 MPa). Conversely, the elastic modulus was higher in the OVX group (4.63 ± 0.11 GPa), indicating a stiffer bone structure, and lowest in the OVX + E2 (2.85 ± 0.55 GPa) group of animals. The PYD is the displacement from the yield point to the fracture point, and is a measure of ductility. Higher values are considered to reflect ‘ductile’ bone, which may sustain a lot of damage before fracture [42]. Lower PYD values indicate that bone is more ‘brittle’ in nature. Our results showed that the highest PYD was measured in the OVX + E2 group (0.49 ± 0.17 m), followed by the OVX (0.47 ± 0.17 m) and control (0.28 ± 0.11 m) groups. No significant differences were found. The unadjusted non-normalized biomechanical properties of each of the groups are presented in Supplementary Figure S1 and Tables S3–S8.
## 3.6. Histological, Scanning Electron Microscopy, and microCT Analyses
Quantitative assessment of %trabecular bone area showed significantly increased bone in the OVX + E2 group (0.182 ± $0.024\%$), when compared with both the control group (0.089 ± $0.009\%$) and OVX group (0.068 ± $0.005\%$; $p \leq 0.05$ in both cases) (Figure 5A). Samples were assessed for disparity in osteoclastic activity; however, no obvious and heightened activity was noted in the OVX group when compared with the control and OVX + E2 samples. Analysis using SEM showed the presence of osteocytes within lacunae in all samples, indicating similar levels of bone viability in each of the groups (Figure 5B–D).
These results were supported following a qualitative analysis of the 3D reconstructed models and μCT images. Here, results showed substantial trabecular bone loss in the proximal region of the tibiae in the OVX group when compared to the control samples (Figure 6A–C). The anabolic effect of estradiol administration was evident via the increase observed in the size and thickness of the trabeculae when compared with the control group. Its effect was also evidenced by a thicker cortical bone structure in the transverse plane, and when compared to the control tibiae (Figure 6E).
Levels of BMD, BMC, and BVTV were assessed in one tibia from each experimental group (Supplementary Figure S2, and Tables S9–S11). Tibiae were transversely divided into seven blocks where block 1 represented the proximal region of bone, and block 7, the most distal. Bone mineral content, and BV/TV gradually increased in the proximal to distal direction in each group, while conversely, BMD gradually decreased in the proximal to distal direction. Higher BMD, BMC, and BV/TV levels were measured in the OVX + E2 tibiae, with lowest values displayed in OVX bone. When these parameters were compared in the antero-posterior, medio-lateral planes along the tibia (Supplementary Table S12), increased BMC, BMD, and BVTV was found in the OVX + E2 groups, with similar levels observed when the OVX and control tibiae were compared (Table 6).
## 4. Discussion
Presently, $8.5\%$ of people worldwide (617 million) are elderly (≥ 65 years), and this number is forecast to increase to ~$17\%$ by 2050 (1.6 billion) [43]. As such, the prevalence of osteoporosis and incidence of fragility fractures will also undoubtedly increase [44]. Historically, osteoporosis was studied in the context of endocrine dysfunction, low estrogen, and vitamin D. However, in recent years it has been discovered that osteoporosis is multifactorial with causes stemming from immunology, the gut flora, diet, and cellular senescence among others [45]. However, the exact mechanisms remain unknown. The importance of diet on governing the development and progression of osteoporosis may be significant, but has not been fully discerned, and the role of ω-9 as a potential disease mediator has not been largely explored. Omega-9 fatty acids may work individually, additively, or synergistically as precursors and essential factors within metabolic pathways [46]. Hu et al. [ 47] showed that osteoporosis was regulated via 13 related metabolism pathways, including via MUFA palmitoleic acid, and therefore ω-9 fatty acids may actively contribute to regulating membrane fluidity, cell structure, and subsequent disease pathogenesis. The aim of this study was to investigate whether ovariectomized mice fed a diet high in ω-9 fatty acids, were protected against the progression of post-menopausal osteoporosis; thereby potentially serving as a modifiable dietary intervention against osteoporotic deterioration. Our rationale was based on the literature [34,48,49,50], and our previous study [23] where this targeted high fat diet induced a significant and anabolic bone response; increasing tibial viscosity, bone area, trabecular thickness, and ultimate strength, while also reducing microcrack damage in a male, murine high-fat diet-fed model. Here, our findings revealed that despite the ingestion of a diet high in ω-9 for 12 weeks following ovariectomy, a significant decrease in cortical, and trabecular area, and architecture was found when compared to the control and OVX + E2 groups. However, and remarkably, when the data was normalized to body weight and tibial length, no significant decrease in yield, ultimate, and fracture strength were determined when compared to the sham-operated control group. Along with other published studies [51,52,53], our results also confirmed that the progression of osteoporosis can be counteracted by estrogen treatment. Therefore, these results support our hypothesis in part.
Estradiol is a major circulating estrogen, and estrogens are known to regulate many physiological functions (e.g., reproduction, inflammation, bone formation, energy expenditure, and food intake) [52]. While increasing estrogen levels (via ERα [54,55]) have been associated with decreased eating [51], our results showed no significant difference in food and water uptake when each of the groups were compared [data not shown]. Animals in the OVX + E2 group exhibited a reduced calorific intake when compared with control and OVX animals however, no significant differences were found. Body compositional results showed that although all animals were fed a high fat diet rich in ω-9, the OVX animals (with reduced E2 levels) gained significantly more body weight via increased fat mass. Estrogens synergized with adipose tissue genes, and estrogen loss results in increased total adipose tissue mass [51]. As such, and following ovariectomy, this increase in fat mass was not unexpected [53] as ovulating females are generally protected from diet-induced obesity, and maintain higher energy expenditure [56], likely via upregulation of aromatase [57], the enzyme pivotal in synthesizing estrogens.
The mechanical properties of cortical bone can be characterized as: [1] an initial elastic domain where the material deforms in a reversible fashion, [2] a post-yield domain where irreversible strains and damage are produced, and [3] a fracture zone where a macrocrack or “pop-in” event is formed [42]. As such, the visco- (type-I collagen/proteins/water) elastic (hydroxyapatite (HA)) properties [58,59] of bone are of high importance in its mechanical response to load. For example, the energy absorptive properties of cancellous bone play a pivotal role in protecting bone and articular cartilage during loading [60], and falls leading to hip fracture are a high-speed event, where the dissipation of energy by bone will depend in part, on its viscoelastic properties (i.e., mass, stiffness, and damping) [61,62,63]. However, ultimate, yield, and fracture stress, and elastic modulus (stiffness) are considered primary in controlling catastrophic and monotonic bone failure [64]. Toughness is characteristic of the ability of bone to absorb energy without fracturing, and bone with a low loss tangent, is less able to dampen incoming energies, and as such, bone that is less tough, is at a higher risk for fracture [61,65,66,67]. Following ovariectomy, bone has been commonly reported to possess a reduced viscous component [68], [69], and a significantly decreased loss tangent, elastic modulus, density, ultimate force, and stiffness [67], [69,70,71,72,73,74], thus microcrack formation and rapid propagation are more likely to occur. Notably and conversely, our findings revealed that although not significant, a strong trend was seen where the tibiae retrieved in the OVX group, displayed an increased elastic modulus, and storage modulus, indicating that on the macro-structural level, and on the micro-tissue level, respectively, bone was in fact, stiffer than the control, and OVX + E2 groups. Previous studies have reported a correlation between bone strength and stiffness [75,76,77], where bone will adaptively stiffen to prevent fracture [78]. In support of this, we measured no significant decline in ultimate, fracture, or yield strength in the OVX group when compared with the control, and OVX + E2 animals. Therefore, the results from this study suggest that the high fat diet rich in ω-9 fatty acids, may have delivered an advantageous response, in that the stiffer OVX bone structure may contribute to a superior structure for protection against fracture either despite of, or to compensate for, the substantial loss in OVX bone tissue observed.
Our findings also revealed an increased PYD and loss modulus in the OVX group when compared with control mice, indicating that on the macro-structural level and on the micro-tissue level, respectively, OVX bone possessed increased ductility, and viscous energy loss. This increase would deliver enhanced compliance, high toughness, and ultimately an OVX structure that retards fracture propagation, reducing the risk of monotonic fracture [61,65,66,67]. Together, these results suggest that a diet high in ω-9, promoted both a stiffer and a more fluid component within the OVX bone tissue structure. This may have in turn, increased its overarching structural strength; a concept supported by our results which showed no significant difference in ultimate, yield, and fracture stress when compared with control and OVX + E2 animals.
Unexpectedly, a significant decrease in cross-sectional moment of inertia was measured in the OVX group when compared to control animals. The loss of a substantial volume of trabecular bone tissue, and thinner cortices, as measured in the OVX group, has traditionally been reported to lead to structural adaptation. Typically, a countermeasure response, where bone adjusts, and increases its cross-sectional moment of inertia, is initiated in order to elicit a more mechanically robust structure [79,80,81]. This compensational response delivered via periosteal apposition (thereby delivering an increased outer cortical diameter), combined with endosteal resorption (which increases the inner cortical diameter), may be effective in positioning the tubular bone structure further from the neutral axis. This adaptive response would distribute forces over a larger area, thereby increasing resistance to stresses and strains, thus reducing the risk of fracture [79,80,81]. This would also theoretically be at the expense of ductility but in turn, offers greater resistance to fatigue. Such structural adaptations to PUFA, and high saturated fat diet-induced osteoporosis has previously been reported following 8 weeks of feeding [23]. However, and although substantial bone loss was measured in the OVX group, no adaptive, counter-response in tibial structure was observed, but conversely a significant decrease in cross-sectional moment of inertia was measured when compared with control animals. Despite this and remarkably, OVX ultimate, fracture, and yield strength were maintained at levels similar to the control tibiae. These results suggest that the significant changes in tibial geometric, cross-sectional structure, and tissue volume shown in the OVX group, did not appear to influence the overarching mechanical properties of OVX bone. In contrast to our findings, Jimenez-Palomar and colleagues [74] reported that OVX bone micro-beams displayed a lower elastic modulus, compared to healthy samples. However, and similar to our study, an increased strain to failure (ductility) was measured when compared to healthy control samples, with no differences in bone strength measured. The authors speculated that the increased ductility provided enhanced toughness, which maintained bone strength, and that degradation of the organic material in osteoporosis is responsible for the resultant changes in mechanical properties. To this end, alterations in bone macro-structure have traditionally been considered the primary driver of osteoporotic-induced bone fragility. However, more recently, advances in our understanding revealed bone mass, spatial distribution/shape, microarchitecture, and also the intrinsic and extrinsic characteristics of the organic and inorganic matrix are of high significance [82]. Thus, osteoporotic fracture risk is governed by the complex and likely synergistic interaction of these parameters, as well as progression of the disease.
Notably, alterations in the organic matrix alter the mechanical performance of bone. The mineral phase of bone is largely composed of HA, while type-I collagen constitutes over $90\%$ of the organic phase [58,59]. Water stabilizes the collagen triple helix via hydrogen bonding, binds to crystal surfaces for ion exchange, aiding apatite orientation and biomineralization [83], and binds extrafibrillar non-collagenous proteins, such as bone sialoprotein, osteocalcin (OCN), and osteopontin (OPN) [84,85]; thereby controlling the viscous properties of bone, delivering ductility or plasticity, and influencing the fracture properties of bone [59,86]. Further, protein networks store energy as well as dissipate large amounts of energy, thereby delivering cohesion and toughness properties to bone, and alterations in their chemistry have been reported in osteoporosis [87,88]. Changes in the mineral-organic interface due to osteoporosis have been shown to result in an increase in the stiffness and cross-linking characteristics of collagen fibers and protein chains [74]. Further, collagen fibril deformation at low strain rates, reduced fibril plasticity at high strain rates, and alterations to the intra/extrafibrillar structure have been reported in osteoporotic bone; properties that reduce the quality of the organic matrix [89]. Together, the accumulation of nonenzymatic glycation end-products, stiffening of the collagen network, as well as compositional changes in collagen, such as the α1 to α2 chains, have been reported to increase the fracture risk [90,91,92], and potentially ultimately failing via delamination of mineralized collagen [93]. We cautiously speculate that it is conceivable that the decrease in elastic modulus reported in osteoporotic bone may suggest that the stress transfer between protein molecules becomes inefficient, due to the previously reported changes in cross-linking density, thereby increasing deformation and failure within this organic component of bone [74]. If this is the case, in our present study, OVX bone was in fact stiffer. As such, the mineral portion of bone matrix likely increased, and this may have limited the interfibrillar motion of collagen, and in parallel, augmented the mechanical stability of OVX bone [68]. Paradoxically, our results also supported an increase in ductility and the viscous fraction within the organic component of bone, and the reasons for this remain elusive. High dietary levels of MUFAs have been associated with increased circulating OCN and OPN [50], which if in bone, may effect bone viscosity. Nevertheless, a high fat diet enriched with ω-9 fatty acids augmented bone strength and this may have occurred via augmentation of the organic component of bone, possibly by producing changes in cellular metabolic activity, structure, and/or cell function, in addition to potentially promoting osteoblast function [94].
Several study limitations are acknowledged. First, BMD, BMC, and BVTV were measured in only one animal per group. As the level of mineralization renders bone its stiffness, further investigations are needed to thoroughly assess alterations, and any correlations in bone mineral content, viscoelasticity, and bone fracture in response to ω-9, and following ovariectomy. Although an $$n = 1$$, our data showed that the OVX bone and control tibiae presented with similar levels of BMD and BMC. Second, this study did not assess levels of crystallinity, crystal properties, osteogenic, and non-collagenous protein levels, or collagen content and quality; all critical mediators of bone strength and risk of fracture. Finally, routine clinical laboratory data that would deliver further information on the biochemical changes that had occurred (e.g., glucose, triglycerides, creatine, OCN, OPN) were not investigated in this study.
## 5. Conclusions
In conclusion, and to the best of our knowledge, there are few studies that have investigated the effect of a diet high in ω-9 on bone health and osteoporosis. This study provides first evidence of the contribution of ω-9 in bone health and during post-menopausal osteoporosis. While studies in the literature report that osteoporotic bone exhibits decreased stiffness, storage modulus, loss modulus, and viscosity, increasing their cross-sectional moment of inertia to mechanically compensate for tissue loss, our study demonstrated that these attributes were not observed following administration of a diet high in ω-9 fatty acids, and over a post-ovariectomy 12-week study period. Notably, the diet did not prevent osteoporotic deterioration of the trabecular and cortical bone structure, nevertheless healthy overarching tibial strength and resistance to fracture was maintained. The ω-9 diet delivered subtle changes to the material properties of bone, and the unique skeletal properties bestowed by the ω-9 diet may be due to changes in metabolic activity within the organic component of bone. Many questions remain, and further investigation of ω-9 as a modifiable dietary intervention against osteoporotic deterioration is warranted.
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|
---
title: Macauba (Acrocomia aculeata) Pulp Oil Prevents Adipogenesis, Inflammation and
Oxidative Stress in Mice Fed a High-Fat Diet
authors:
- Cíntia Tomaz Sant’ Ana
- Thaísa Agrizzi Verediano
- Mariana Grancieri
- Renata Celi Lopes Toledo
- Elad Tako
- Neuza Maria Brunoro Costa
- Hércia Stampini Duarte Martino
- Frederico Augusto Ribeiro de Barros
journal: Nutrients
year: 2023
pmcid: PMC10005707
doi: 10.3390/nu15051252
license: CC BY 4.0
---
# Macauba (Acrocomia aculeata) Pulp Oil Prevents Adipogenesis, Inflammation and Oxidative Stress in Mice Fed a High-Fat Diet
## Abstract
Macauba is a palm tree native to Brazil, which fruits are rich in oil. Macauba pulp oil has high contents of oleic acid, carotenoids, and tocopherol, but its effect on health is unknown. We hypothesized that macauba pulp oil would prevent adipogenesis and inflammation in mice. Thus, the purpose of this study was to evaluate the effects of macauba pulp oil on the metabolic changes in C57Bl/6 mice fed a high-fat diet. Three experimental groups were used ($$n = 10$$): control diet (CD), high-fat diet (HFD), and high-fat diet with macauba pulp oil (HFM). The HFM reduced malondialdehyde and increased SOD activity and antioxidant capacity (TAC), showing high positive correlations between total tocopherol, oleic acid, and carotenoid intakes and SOD activity ($r = 0.9642$, $r = 0.8770$, and $r = 0.8585$, respectively). The animals fed the HFM had lower levels of PPAR-γ and NF-κB, which were negatively correlated with oleic acid intake (r = −0.7809 and r = −0.7831, respectively). Moreover, the consumption of macauba pulp oil reduced inflammatory infiltrate, adipocyte number and length, (mRNA) TNF-α, and (mRNA) SREBP-1c in the adipose tissue, and it increased (mRNA) Adiponectin. Therefore, macauba pulp oil prevents oxidative stress, inflammation, and adipogenesis and increases antioxidant capacity; these results highlight its potential against metabolic changes induced by an HFD.
## 1. Introduction
Current eating habits characterized by elevated consumption of saturated fats and simple carbohydrates and low vitamins are one of the most important causes for the emergence of chronic non-communicable diseases, such as obesity and nonalcoholic fatty liver disease [1,2]. Obesity is characterized by adipogenesis, which favors the induction of metabolic changes, including changes in cytokine concentrations, activation of inflammatory pathways, and lipotoxic effects in tissues such as the liver. As a consequence of these changes, reactive oxygen species (ROS) production may increase and may, thus, result in an exacerbation of inflammation, oxidative stress, and cell alterations [3].
Regulation and control of adipogenesis and metabolic changes are performed by specific transcriptional regulators, such as peroxisome proliferator-activated receptor gamma (PPAR-γ), sterol regulatory element-binding protein 1 (SREBP-1), and nuclear factor kappa B (NF-κB) [4]. SREBP-1 controls fatty acid biosynthesis by favoring the transcription of specific enzymes and activating PPAR-γ, which controls the expression of genes that regulate adipocyte differentiation. NF-κB controls the expression of inflammatory genes. In obesity, there is an increase in these transcription factors, resulting in increased lipogenesis, which leads to an increase in triacylglycerol and a reduction in lipolysis, thereby favoring the development of inflammation and oxidative stress [3,4]. As such, research studies that demonstrate new dietary strategies with the purpose of preventing or controlling obesity and metabolic alterations become very important, and dietary fatty acid composition demonstrates a significant impact on disease development [5]. Thus, nutritional strategies that aim to treat or prevent these metabolic alterations are of great importance.
Macauba (Acrocomia aculeata) is a palm tree that is naturally present in almost all Brazilian territories, and it is considered a promising alternative to vegetable oil for fuel and for the cosmetic and food industries due to its high oil production and specific characteristics [6]. Two types of oils are obtained from macauba: pulp and kernel oils. Both have important chemical and economical characteristics, highlighting their nutritional action and applications in the food industry [7]. Similar to olive oil, macauba pulp oil is rich in oleic acid [6]. Oleic acid has been shown to reduce the expression of transcription factors related to the adipogenesis signaling pathway, such as PPAR-γ, and reduce oxidative stress markers [8]. Moreover, macauba pulp oil has a high content of carotenoids, which can act to reduce inflammation through NF-κB modulation [6,9]. Additionally, this oil contains tocopherol, which is an important antioxidant that has been shown to improve inflammation and oxidative stress [10]. Thus, macauba pulp oil consumption may result in an improvement in metabolic changes, which is associated with the bioactive compounds in its composition [6].
We hypothesized that macauba pulp oil would prevent adipogenesis and inflammation in mice. However, to the best of our knowledge, no research has been performed to provide evidence of the health benefits of macauba pulp oil. Thus, the objective of this work was to evaluate the effect of macauba pulp oil on the adipogenesis pathways and metabolic changes in mice fed a high-fat diet. This study is the first to explore the health benefits of this promising vegetable oil.
## 2.1. Materials
Macauba fruits were harvested in Araponga, Minas Gerais (Brazil), in the mature stage, and then they were peeled and pulped to obtain the macauba pulp. The pulp was dried at 65 °C (CIENLAB CE220, Brazil) for 15 h. Oil was extracted using a manual hydraulic press (Laboratory Press, Fred S. Carver Inc., Summit, NJ, USA), centrifuged (5000 rpm/20 min), and then placed in a freezer (−80 °C).
## 2.2. Chemical Characterization of Macauba Pulp Oil
The fatty acid profile of the macauba pulp oil was determined using a gas chromatography equipped with a flame ionization detector (GC-FID) (Shimadzu, GC-2010, Kyoto, Japan) and a capillary column of 100 m × 0.25 mm (SP-2560, Sigma-Aldrich, San Luis, MO, USA) [11]. Helium gas was used as the dragging gas and maintained at a constant flow rate of 363 kPa. Fatty acid methyl esters (FAMEs) were separated using a linear heating ramp from 100 °C to 270 °C, at a heating rate of 20 °C mim−1 and with a high linear velocity for better peak resolution. Peak identification was confirmed by comparison with the standard FAME mix (Supelco 37 FAME mix, Sigma-Aldrich, San Luis, MO, USA). Moreover, the oleic acid content (mg/g) of the oil was also determined using a standard (Sigma-Aldrich).
Carotenoid analysis was carried out by a high-performance liquid chromatography (HPLC) with detection at 450 nm, using the following chromatographic conditions: a HPLC system (Shimadzu, SCL 10AT VP, Kyoto, Japan) and a chromatographic column Phenomenex Gemini RP-18 (250 mm × 4.6 mm, 5 mm) fitted with a guard column RP-18 Phenomenex ODS column (4 mm × 3 mm). The mobile phase consisted of methanol:ethylacetate:acetonitrile (70:20:10, v/v/v) with a flow rate of 2.0 mL·min−1 and a run time of 15 min. Total carotenoid content (μg/g) was expressed as the sum of the major carotenoids present in the macauba pulp oil [12].
Total tocopherol content was determined following the AOCS method, using a HPLC with fluorescence detection at 450 nm and the following chromatographic conditions: a silica column of 4.6 × 250 mm with a pore of 5 μm, a flow rate of 1.0 mL min−1, and as the mobile phase, a mixture of $99.5\%$ of n-hexane and $0.5\%$ of isopropanol. The concentration of total tocopherols (μg/g) was expressed as the sum of the major tocopherols present in the macauba pulp oil [13].
## 2.3. Animals and Experimental Design
Black male mice C57Bl/6 (30 animals), which were 8 weeks old and had an average weight of 24.34 ± 0.18 g, were allocated into 3 groups, with 10 animals in each group, based on the homogeneity of body weight. The sample calculation equation determined how many animals should be in each group, using the following variables: α-error type $I = 1.96$, α-level = $5\%$, and data of fat mass mean reported by Schoemaker et al. in 2017 [14,15]. Individual stainless steel cages were used to keep the animals in a temperature-controlled room (light–dark cycles of 12 h and temperature of 22 ± 2 °C). Water and the respective experimental diets were supplied ad libitum.
The experimental diets were formulated according to AIN-93M and high-fat diet, using lard in the high-fat diet [16]. Each experimental group consumed the following diet: control diet—AIN93M (CD); high-fat diet (HFD); high-fat diet with macauba pulp oil (HFM). In the HFM, macauba pulp oil was added in a proportion of 40 g/kg ($4\%$), replacing the soybean oil used in the AIN-93M diet (Table 1). The objective was to verify the effect of macauba pulp oil as a replacement of soybean oil, which is commonly used in control diets, and not as a supplementation. The formulated diets were stored at a low temperature (−20 °C) and offered to the animals every day.
At the end of the 8 weeks, the animals were anesthetized after 12 h of fasting using isoflurane (Isoforine, Cristália), in accordance with the bodyweight of the mice. Using the methodology of cardiac puncture, blood was collected and centrifuged (4 °C at 800× g for 10 min using Fanem-204, São Paulo, Brazil), and the serum was collected and stored at −80 °C. The liver and adipose (epididymal and subcutaneous) tissues were extracted and stored at (−80 °C) until analysis, and another part was fixed in formaldehyde ($10\%$) for the analysis of histological markers. Bodyweight gain and feed consumption were measured on a weekly basis throughout the experiment to calculate the feed efficiency ratio (weight gain/consumption × 100), and the percentage of adiposity was measured based on the weight of the adipose tissue (g) in relation to the total body weight. Body mass index (BMI) was measured using the ratio between weight and naso-anal length (cm) squared [17]. The hepatosomatic index was also determined (liver weight/body weight × 100) [18]. Carotenoid, oleic acid, and tocopherol intakes were determined by the total amount of diet consumed by the mice. Ethical principles for animal experimentation were implemented for all processes performed on the animals [19]. The Ethics Committee of the Federal University of Viçosa approved this research (Protocol $\frac{09}{2019}$; date of approval: 28 May 2019).
## 2.4. Biochemical Analysis
The biochemical parameters were determined using the serum. Glucose concentration, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triacylglycerides (TGL), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were determined based on the colorimetric method using commercial kits (Bioclin®, Belo Horizonte, Brazil).
## 2.5. Homogenate Preparation and Oxidative Stress Levels
Liver homogenate was prepared with 200 mg of the liver. The liver was mixed with 1 mM of EDTA (pH 7.4) and 1000 μL of phosphate buffer (50 mM). The content was macerated and centrifuged (1200× g/8 min/4 °C), and the supernatant was collected for the analysis of antioxidant enzymes.
For the quantification of the enzyme superoxide dismutase (SOD), 249 μL of 50 mM of Tris-HCl buffer (pH 8.2) (1 mM of EDTA, 6 μL of MTT (1.25 mM), 15 μL of pyrogallol (10 mM), and 279 μL of buffer) was mixed into the aliquoted homogenate. To determine the blank, 6 μL of MTT and 294 μL of buffer were added to the wells, which were incubated for 5 min at 37 °C, and the reading was performed on a spectrophotometer at 570 nm (Thermo Scientific Multiskan GO, Waltham, MA, USA). The SOD quantification was expressed as units of SOD/mg protein [20].
Malondialdehyde (MDA) was determined using the samples of the homogenate. A total of 400 μL of trichloroacetic acid solution ($15\%$) and thiobarbituric acid ($0.375\%$) was added into 400 μL of the sample. It was placed in a water bath (90 °C/40 min) and 600 μL of n-Butanol was added; then, the mixture was centrifuged (3500 rpm/ 5 min). The supernatant was removed, and the absorbance was read at 535 nm (Multiskan GO—Thermo Scientific). The MDA level was expressed as MDA/mg protein [21].
Catalase was performed on the samples of the homogenate as described above. At 0, 30, and 60 s after the reaction was initiated, the absorbance was determined at 240 nm (T70 + UV/VIS Spectrometer). Enzyme activity was reported as μmol per mL of sample, and the data were expressed in U of catalase/mg protein. Catalase activity was calculated according to the Beer–Lambert law [22].
For the quantification of nitric oxide, 50 μL of the homogenate was used. Then, $1\%$ sulfanilamide solution and $0.1\%$ nafityl ethylene amide dihydrochloride were added. A 0.025 M sodium nitrite standard curve was used, and the absorbance was determined at 570 nm (Multiskan GO—Thermo Scientific) [23].
## 2.6. Total Antioxidant Capacity of Serum and Liver
The total antioxidant capacity (TAC) of the serum and the liver was determined with an antioxidant assay kit (Cayman Chem Corp, Ann Arbor, MI, USA) Sigma Aldrich®. The absorbance reading was performed at 405 nm (Multiskan GO—Thermo Scientific).
## 2.7. PPAR-γ, PPAR-α, NF-κB, and TLR-4 Quantification
The adipose tissue and liver samples were homogenized using the NE-PER™ Nuclear and Cytoplasmic Extraction Kit reagents (Thermo Scientific Fisher, Waltham, MA, USA). The nuclear fractions were analyzed using an immunoassay with the Mouse PPAR-γ (Peroxisome Proliferator-Activated Receptor Gamma—E-EL-M0893, Elabscience, Houston, TX, USA), Mouse NF-κB p65 (Factor Nuclear Kappa B—E-EL-M0838, Elabscience, Houston, TX, USA), Rat PPAR-α (Peroxisome Proliferator-Activated Receptor Alfa—E-EL-R0725, Elabscience, Houston, TX, USA), Rat NF-κB p65 (Factor Nuclear Kappa B—E-EL-R0674, Elabscience, USA) and Rat TLR-4 (Toll-like Receptor 4—E-EL-R0990, Elabscience, USA) ELISA kits, respectively. The microplates were, respectively, precoated with anti-PPAR-γ, anti-NF-κB p65, anti-PPAR-α, and anti-TLR-4 antibodies. The concentrations were calculated by comparison to the corresponding standard curves.
## 2.8. Determination of Gene Expression in Adipose Tissue and Liver by Reverse Transcriptase Quantitative Polymerase Chain Reaction (RT-qPCR)
TRIzol reagent (Invitrogen, CA, USA) was used to extract total RNA from the liver, and a specific kit (mirVana™ miRNA Isolation Kit, Life Technologies, Carlsbad, CA, USA) was used to extract RNA from the adipose tissue, according to the manufacturer’s protocols. RNA concentration and purity were evaluated using a Microdrop plate spectrophotometer Multiskan™ GO (Thermo Scientific, Waltham, MA, USA). To create cDNA synthesis, the M-MLV Reverse Transcriptase Kit (Invitrogen, CA, USA) was used. RT-qPCR was used for the gene expression relative quantification using the AB StepOne Real-Time PCR System equipment and Fast SYBR Green Master Mix (Applied Biosystems, Carlsbad, CA, USA) reagent. The initial parameters used were 20 s at 95 °C and then 40 cycles at 95 °C (3 s), 60 °C (30 s), followed by the melting curve analysis. A melting point analysis was performed to improve the specificity and sensitivity of the amplification reactions detected. All primers were designed by using the Primer 3 Plus program and obtained from Sigma-Aldrich Brazil Ltda. ( Table 2). The 2-Delta-Delta C (T) method was used to calculate the gene expression, by using GAPDH and β-actin as the references and the high-fat diet group as the control, which was normalized to 1 [24].
## 2.9. Histomorphometric Analysis of Adipose and Liver Tissues
Paraffin was used to fix the samples of adipose tissue and liver. Ten cuts per animal were performed (3 μm thick), and the samples were mounted on glass slides and stained with hematoxylin and eosin. Analyses were performed under a light microscope (Leica DM750®). The histological sections of the images were captured in a 20× objective. Inflammatory infiltrate number and length of adipocytes were evaluated using the adipose tissue (Image-Pro Plus® 4.5). Liver cellular components (fat vesicles, inflammatory infiltrate, cytoplasm, and nucleus), for 10 histological fields per animal, were analyzed using a test system with 266 points, obtaining 2660 total points for each animal analyzed (Image J®, Wayne Rasband). The following formula was used to calculate the parameters: Vv = Pp/PT (Pp = number of points located on the structure of interest, and PT = total test points in the histological area) [25]. The steatosis degree was determined semi-quantitatively according to a 5° scale and the fat percentage: degree 0 (<$5\%$), grade 1 (≥$5\%$ and <$25\%$), grade 2 (≥$25\%$ and <$50\%$), grade 3 (≥$50\%$ and <$75\%$), grade 4 (≥$75\%$) [26].
## 2.10. Statistical Analyses
Kolmogorov–Smirnov normality test was initially applied, and then an analysis of variance (ANOVA) test was performed, followed by the Newman–Keuls test for parametric variables. For the correlation analysis, Pearson’s correlation was used. The results with a p-value ≤ 0.05 were considered statistically significant. The statistical analyses were performed using the GraphPad Prism® version 8.0 (GraphPad Software, San Diego, CA, USA).
## 3.1. Chemical Characterization of Macauba Pulp Oil
The macauba pulp oil shows a high content of monounsaturated fatty acids ($55\%$), with significant oleic acid content ($49.32\%$), as shown in Table 3. In addition, it has high contents of carotenoids and tocopherol (Table 3).
## 3.2. Effects of Macauba Pulp Oil on Biometric Measures, Food Intake, and Lipid Profile
Weight gain, body mass index (BMI), and food efficiency ratio (FER) did not differ among the experimental groups ($p \leq 0.05$; Table 4). The CD group had higher food consumption compared to the HFD and HFM groups, which was associated with the reduced caloric density of the AIN93M diet ($p \leq 0.0001$; Table 4). The CD group had a lower percentage of adiposity compared to the HFD and HFM groups ($$p \leq 0.0018$$; Table 4).
The group that consumed macauba pulp oil (HFM) did not differ from the HFD group in terms of glucose, triglyceride, TC, LDL, and HDL values, as well as hepatic enzymes AST and ALT, and hepatosomatic index ($p \leq 0.05$; Table 4).
## 3.3. Total Antioxidant Capacity and Oxidative Stress Marker Levels in Mice
The HFM group had a high SOD activity ($$p \leq 0.0078$$; Figure 1A) and showed a positive correlation with carotenoid ($r = 0.8585$, $$p \leq 0.004$$), oleic acid ($r = 0.8770$, $$p \leq 0.009$$) and tocopherol ($r = 0.9642$, $p \leq 0.0001$) intakes (Figure 1B–D). Macauba pulp oil decreased malondialdehyde ($$p \leq 0.0057$$; Figure 1E), showing a negative correlation between this parameter and oleic acid (r = −0.9401, $p \leq 0.001$) and tocopherol (r = −0.9021, $$p \leq 0.0004$$) (Figure 1G,H). Catalase and nitric oxide did not differ among the groups ($p \leq 0.05$; Figure 1M,N).
The HFM group had a higher serum TAC compared to the HFD and CD groups ($$p \leq 0.0058$$; Figure 1I), showing a positive correlation between serum TAC and oleic acid ($r = 0.8967$, $$p \leq 0.005$$) intake from macauba pulp oil (Figure 1K). Liver TAC did not differ among the experimental groups ($p \leq 0.05$; Figure 1O).
## 3.4. Effects of Macauba Pulp Oil on NF-κB, TLR-4, and PPAR-(α, γ) Quantification
The HFM group had a lower nuclear quantification of NF-κB in the adipose tissue compared to the HFD and CD groups ($$p \leq 0.0179$$; Figure 2A), showing a negative correlation with oleic acid (r = −0.7831, $$p \leq 0.037$$) and tocopherol (r = −0.8134, $$p \leq 0.0261$$) intakes (Figure 2C,D). Macauba pulp oil reduced the PPAR-γ quantification ($$p \leq 0.056$$; Figure 2E), showing a negative correlation with carotenoid (r = −0.7301, $$p \leq 0.021$$) and oleic acid (r = −0.7809, $$p \leq 0.022$$) (Figure 2F,G).
NF-κB, PPAR-α, and TLR-4, as present in the nuclear fraction in the liver, did not differ among the experimental groups ($p \leq 0.05$; Figure 2I–K).
## 3.5. Effects of Macauba Pulp Oil on Gene Expression in Adipose and Hepatic Tissues
In the liver, in the HFM group, the mRNA expression of SREBP-1c was significantly increased compared to the control and HFD groups ($p \leq 0.0001$; Figure 3A), whereas (mRNA) CPT-1α was decreased ($$p \leq 0.0031$$; Figure 3B). The mRNA expression of ACC-1α and AdipoR2 did not differ from the HFD group ($p \leq 0.05$; Figure 3C,D).
In the adipose tissue, in the HFM group, the mRNA expression of SREBP-1c ($p \leq 0.0001$; Figure 3E) and (mRNA) TNF-α ($p \leq 0.0001$; Figure 3H) were significantly decreased compared to the HFD group, and the mRNA expression of Adiponectin was similar between the HFM and CD groups ($p \leq 0.05$; Figure 3G). The mRNA expression of LPL was similar among the groups ($p \leq 0.05$; Figure 3F). The correlation analysis showed a negative correlation between mRNA SREBP-1c and carotenoid intake (r = −0.8991, $$p \leq 0.012$$), a positive correlation between mRNA Adiponectin and carotenoid intake ($r = 0.9130$, $p \leq 0.001$), and negative correlation between mRNA TNF-α and oleic acid intake (r = −0.9057, $$p \leq 0.0009$$).
## 3.6. Effects of Macauba Pulp Oil on Histological Morphometrics of Liver and Adipose Tissues
The percentage of the nucleus, cytoplasm, inflammatory infiltrate, and fat deposition in the hepatocytes did not differ among the groups ($p \leq 0.05$, Figure 4A). The control group was identified as steatosis grade 0, whereas the HFD and HFM groups increased the steatosis to grade 1 and had similar values between them (Figure 4B). The HFM group had lower inflammatory infiltrate ($p \leq 0.0001$) and adipocyte number ($$p \leq 0.0027$$) and length ($$p \leq 0.0088$$) in the adipose tissue compared to the HFD group, but its values were similar to the CD group (Figure 4C,D).
## 4. Discussion
This is the first work that evaluated the influence of macauba pulp oil on undesirable metabolic changes in mice fed a high-fat diet. The present research focused on the effects of macauba pulp oil since there is evidence that oleic acid, carotenoid, and tocopherol present in this oil would trigger anti-inflammatory, anti-obesity, and antioxidant effects [27,28]. In this study, macauba pulp oil intake prevented the adipogenesis pathway, inflammation, and oxidative stress in mice fed a high-fat diet. In order to stimulate metabolic changes in animals, high-saturated fat diet consumption is extensively applied. The time to verify the effect of a specific food or compound on metabolic changes usually begins after seven or eight weeks of receiving the diet. In a different way, in our study, to determine the effects of macauba pulp oil as a preventive treatment, macauba pulp oil was added in the diet since the beginning of the experiment, along with the high-fat diet, to examine its mechanism of action and metabolic alterations.
In the present study, the consumption of macauba pulp oil reflected a higher total antioxidant capacity (TAC), which might be associated with the oleic acid content, and this was confirmed by the correlation analysis, which demonstrated a significant positive correlation between this compound consumption and TAC. Oleic acid is well documented for its anti-inflammatory properties, possibly associated with its chemical configuration with a double bond, thereby causing less chance of oxidation and resulting in the antioxidant property against a high oxidative load [10,29]. In addition, higher SOD activity and lower malondialdehyde levels were observed with the macauba pulp oil consumption. SODs are oxidoreductase enzymes that have a role in protecting cells against superoxide anions, performing the dismutation of O2•− into oxygen and H2O2, and providing antioxidant defense for the organism, while malondialdehyde is an important marker of lipid peroxidation [30,31]. it is shown that macauba pulp oil consumption can improve antioxidant defenses, with these results being attributed to the oleic acid, carotenoids, and tocopherol present in macauba pulp oil, as demonstrated in other studies that examined the relationship between these components and the improvement of the body’s antioxidant defenses [32,33,34]. Additionally, there was a positive correlation between SOD and these compounds and a negative correlation between MDA and oleic acid and tocopherol.
The consumption of macauba pulp oil prevented the adipogenesis pathway by decreasing the expression of PPAR-γ and (mRNA) SREBP-1c and increasing the expression of (mRNA) Adiponectin in the adipose tissue. This effect was confirmed by the result of the histomorphometric analysis, which demonstrated that the animals that consumed the macauba pulp oil had smaller adipocyte number and length even with a high-fat diet consumption, that is, the macauba pulp oil caused less hypertrophy and hyperplasia of the adipocytes. Thus, the lower translocation of PPAR-γ in the present research could be associated with the high content of oleic acid and carotenoids in the macauba pulp oil and was confirmed by the significant negative correlation between the consumption of these compounds and PPAR-γ quantification. Oleic acid has been shown to act in PPAR-γ repression, resulting in less differentiation of pre-adipocytes into mature adipocytes and reducing adipogenesis [3,35]. Similar to our results, a previous study found a relationship between oleic acid consumption and reduction in PPAR-γ and (mRNA) SREBP-1c in an obese animal model [35]. Research shows that carotenoids can affect adipocyte function through the interaction with PPAR-γ, thereby interfering with adipocyte differentiation, as demonstrated in a study using experimental animals, which found an association between carotenoids and lower adipose tissue gain related to lower PPAR-γ expression [36]. Still, this result is related to the increased expression of adiponectin since PPAR-γ is tightly regulated by adiponectin [37]. Moreover, the observed results of a reduction in the genes related to the adipogenesis pathway, with a concomitant reduction in the histological markers of adipose tissue, could be related to the presence of β-carotene, which was the main carotenoid found in the macauba pulp oil that could suppress PPAR-γ, resulting in lower total lipid in adipocytes [38,39].
Related to this, macauba pulp oil was efficient in reducing inflammation in the adipose tissue since it reduced NF-κB in the nuclear fraction, and this indicates a reduction in the inflammation cascade, leading to a significant reduction in (mRNA) TNF-α gene expression. Corroborating this result, the histomorphometric analysis of the adipose tissue showed less inflammatory infiltrate with the consumption of macauba pulp oil. A hypertrophy of adipose tissue initiates the emission of chemotactic signals that recruit immune cells and lead to the infiltration of macrophages into the adipose tissue, contributing to systemic subclinical inflammation [3]. This result may be associated with a lower amount of PPAR-γ and higher adiponectin since PPAR-γ interferes with the differentiation of adipocytes and is consequently related to the inflammatory process. Obesity is an inflammatory condition: one of the complications related to obesity is the development of reactive oxygen species (ROS), and adiponectin is an anti-inflammatory adipokine with a negative correlation between the degree of obesity and the level of this adipokine [40,41]. These results were supported by the present study since there was a positive correlation between carotenoid consumption and an increase in the expression of adiponectin, indicating that the macauba pulp oil, which is high in carotenoids, may contribute to the reduction of inflammation. Additionally, there was a significant negative correlation between oleic acid and tocopherol and NF-κB, that is, an increase in oleic acid and tocopherol consumption was correlated with a decrease in the quantification of NF-κB. The study by Rosillo et al., with a mouse model, also demonstrated that the administration of oleic acid is able to suppress NF-κB activation [42]. Oleic acid is able to activate PGC-1α by forming a dimer with the protein called c-MAF, migrate to the nucleus, and then transcribe the gene responsible for IL-10, which dismantles the activation signaling of NF-κB due to its potent anti-inflammatory action [43]. Tocopherol can block NF-kB activation through its action on enzymes that regulate the NF-kB signaling pathway [44]. Despite the lack of a correlation between the reduction in NF-κB and the consumption of carotenoids in the present study, this compound presents interference with the NF-κB pathway, resulting in the modulation of their interacting proteins and interacting with the cysteine residues of IκB kinase, thereby suppressing NF-κB activation or inhibiting IκBα degradation [45,46].
Although macauba pulp oil prevented the adipogenesis pathway and inflammation in the adipose tissue, significant effects in the hepatic markers were not observed after eight weeks of the high-fat diet. The current study was carried out as a prevention model, and for this reason, it might not be able to verify alterations in the liver. Thus, in the current research, the consumption of the diets for eight weeks, even with a high concentration of saturated fats, was not able to cause metabolic changes in the liver. These results were confirmed by histomorphometric analyses, which showed that there was no alteration of the cellular components evaluated, such as fat and inflammation in the liver.
Despite the decreased expression of (mRNA) CPT-1α gene, the quantification of PPAR-α did not change with the consumption of macauba pulp oil, which might be because ADIPOR2 did not change either. The increase in the sensitization of ADIPOR2 receptor triggers the activation of PPAR-α, which regulates fatty acid oxidation [47]. Moreover, the high traffic of free fatty acids due to a high-fat diet has the ability to trigger SREBP-1c, which controls the expression of enzymes essential in triacylglycerol synthesis and storage, and restricts lipogenic genes, such as ACC-1, that are responsible for the transformation of ACC-1 to malonyl CoA [48]. However, despite the overexpression of this gene in the fatty acid synthesis pathway, there was no change in the proportion of fat and steatosis degree in the liver. This might be due to the increased antioxidant capacity, which decreased the expression of this gene in relation to fatty acid synthesis.
The strain of mice used in this study was chosen since they are prone to metabolic disturbances generated by a high-fat diet. However, it is known that experiments with mice do not fully reflect the effects in humans due to differences in the organs and metabolism of these two species. However, taking into account the macauba pulp oil intake per animal weight, a human with 70 kg needs a consumption of a small amount per day (approximately 8 g/day of macauba pulp oil—similar to one teaspoon) to have the same improvements observed in this research in the prevention of metabolic changes. Thus, further studies are needed to verify the real effects of macauba pulp oil in human.
The influence of a high-fat diet on the body and the mechanism of macauba pulp oil, which was demonstrated in our study, are summarized in Figure 5. The consumption of macauba pulp oil prevents inflammation and adipogenesis, as demonstrated by a reduction in the expression of PPAR-γ, (mRNA) SREBP-1c, NF-κB, and (mRNA) TNF-α, and an increase in adiponectin in adipose tissue. In the liver, despite triggering the SREBP-1c expression and a lower (mRNA) CPT-1α level, it does not lead to liver changes, according to the histomorphometric analysis, due to an increased antioxidant capacity. These modes of action may be related to macauba pulp oil, which has a good composition of carotenoids, oleic acid, and tocopherol and improves the total antioxidant capacity, resulting in adipogenesis even with a high level of saturated fat consumption.
## 5. Conclusions
Consumption of macauba pulp oil increases antioxidant capacity and prevents oxidative stress, inflammation, and the adipogenesis pathway. Therefore, macauba pulp oil has a great potential for inclusion in human foods to improve health, assisting in the prevention of risk factors for chronic non-communicable diseases.
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|
---
title: Presence of Parabens in Different Children Biological Matrices and Its Relationship
with Body Mass Index
authors:
- Inmaculada Moscoso-Ruiz
- Yolanda Gálvez-Ontiveros
- Cristina Samaniego-Sánchez
- Vega Almazán Fernández de Bobadilla
- Celia Monteagudo
- Alberto Zafra-Gómez
- Ana Rivas
journal: Nutrients
year: 2023
pmcid: PMC10005709
doi: 10.3390/nu15051154
license: CC BY 4.0
---
# Presence of Parabens in Different Children Biological Matrices and Its Relationship with Body Mass Index
## Abstract
Parabens have been accepted almost worldwide as preservatives by the cosmetic, food, and pharmaceutical industries. Since epidemiological evidence of the obesogenic activity of parabens is weak, the aim of this study was to investigate the association between parabens exposure and childhood obesity. Four parabens (methylparaben/MetPB, ethylparaben/EthPB, propylparaben/PropPB, and butylparaben/ButPB) were measured in 160 children’s bodies between 6 and 12 years of age. Parabens measurements were performed with ultrahigh-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). Logistic regression was used to evaluate risk factors for elevated body weight associated with paraben exposure. No significant relation was detected between children’s body weight and the presence of parabens in the samples. This study confirmed the omnipresence of parabens in children’s bodies. Our results could be a basis for future research about the effect of parabens on childhood body weight using nails as a biomarker due to the ease of its collection and its non-invasive character.
## 1. Introduction
Overweight and obesity are defined as abnormal or excessive fat accumulation that may cause health disorders such as cardiovascular diseases, musculoskeletal conditions, alterations in spinal posture and mobility, metabolic syndrome, and gastrointestinal and pulmonary conditions [1,2]. Recent data from World Health Organization showed that 39 million children under the age of 5 were overweight or obese in 2020, and nearly tripled since 1975 [3]. Overweight/obesity can be attributed to several factors, but traditional risk factors such as diet, physical activity, and genetics cannot completely explain the increase [4]. In 2006 Grün and Blumberg postulated the obesogens hypothesis, where certain environmental pollutants could induce adipogenesis [5]. Some endocrine-disrupting chemicals (EDCs) have been catalogued as obesogens due to the effect they induce in adipose tissue deposition via mimicking endogenous endocrine hormones or activating lipidogenesis-related nuclear receptors [6].
Parabens are EDCs widely used as preservatives in the cosmetic, pharmaceutical, and food industries because of their antimicrobial properties and low allergenic potential [7,8,9]. Additionally, their chemical stability, low toxicity, and allergenicity lead parabens to almost worldwide acceptance [7,8,9,10]. The most commonly used parabens are methylparaben (MetPB), ethylparaben (EthPB), propylparaben (PropPB) and butylparaben (ButPB) [11]. All four of these parabens are allowed in personal care products (PCPs), yet both PropPB and ButPB are prohibited in food products due to scientific evidence of their toxicity [12]. The main entry routes of parabens to the body are dermal (by cosmetics), oral (by foods and medicines), and by inhalation [9,13].
It has been shown that parabens may modulate or alter the endocrine system and, therefore, may have negative effects on health due to their cumulative nature in tissues [11,14,15]. In addition, the obesogenic effects of parabens have recently been demonstrated [16,17]. Fat accumulation in the body may occur by increasing the number of adipocytes and their volume or altering the pathways of adipocyte tissue control. Obesogens such as parabens can act by modifying the number or volume of adipocytes by interfering with transcriptional regulators that control lipid flux, adipocyte proliferation, and adipocyte differentiation, particularly through the peroxisome proliferator (PPAR) [18]. In addition, parabens have been shown to promote adipocyte differentiation in 3T3-L1 cells, whose action increased in conjunction with the linear length of the alkyl chain associated with PPAR γ activation [17].
Available literature has shown the obesogenic effect of parabens in vitro and in vivo, yet there still is no consensus on their obesogen effect on the human body [19]. Recently we demonstrated an association between dietary exposure to parabens and overweight/obesity in Spanish adolescent girls using dietary records [16]. However, the use of biological matrices could give us more detailed information on the subjects’ exposure to obesogens.
Urine has been the traditional matrix of choice to study paraben concentrations in the human body. Several studies have analysed the possible relationship between parabens in urine and overweight/obesity in different populations of both children [20,21] and adults [22,23,24], finding a direct statistically significant relationship between MetPB, EthPB, and PropPB and obesity. However, other authors do not confirm those results [25,26,27,28]. The majority of the studies were carried out in Asia, and only one was carried out in a Spanish child population with an average age of 11, showing non-statistically significant results [29]. Children are particularly vulnerable to obesogens because of the critical stage they’re in for the correct functioning and development of the endocrine system [30].
Since urine only provides information on fairly recent exposures, other matrices should be studied as bioindicators of body contamination. Blood might be a good matrix if it were not so invasive. Several studies have considered other non-invasive matrices to study the presence of parabens, such as saliva [31] or nail [32]. The nail can provide information on bioaccumulation over time. On the other hand, Barbosa et al. have made comparisons between the concentration of some contaminants in blood and saliva [33].
Due to the lack of consensus as well as the ubiquity of parabens in foods and cosmetics [13,34,35,36], it is necessary to study alternative matrices to better chart their bioaccumulation in the body, as well as examine more population groups to better understand their obesogen effects, especially in vulnerable populations such as children. The present work aims to study the presence of parabens in a range of biological matrices (nails, urine, and saliva) in the child population of Granada (Spain), as well as the possible association of these parabens with overweight and/or obesity.
## 2.1. Study Design and Settings
The present research is a case-control study designed to assess the influence of environmental factors on the development of overweight and obesity among Spanish children and adolescents. Study participants were recruited from January 2020 to January 2022 from different primary care centres and schools in the province of Granada, Spain. Protocol was approved by the Ethics Committee of Provincial Biomedical Research of Granada (CEI). All parents or legal tutors of study participants were fully informed about the study objectives, and they signed informed consent. Personal identifiers in the dataset were removed to guarantee data confidentiality.
## 2.2. Study Participants
Eligible cases met the following inclusion criteria: [1] diagnosis of overweight or obesity; [2] children aged 6 to 12 years old; [3] having resided within the study area continuously for at least 6 months. The controls had to meet the same inclusion criteria, except for the diagnosis of overweight and obesity. Individuals with obesity associated with a pathology or pharmacological treatment were excluded from the study. The sample included a total of 231 subjects.
For this study, the aim of which was to investigate the relationship between paraben exposure and obesity, only 160 participants ($53.5\%$ boys) of the 231 who agreed to participate were included and displayed concentrations of these compounds in urine, saliva and/or nails (in one, two or the three matrices). No statistically significant differences between controls and cases included/not included were observed, except for the marital status of parent’s cases among controls (Supplementary Table S1).
## 2.3. Data Collection
To achieve the objective of this study, data were collected and used on variables related to anthropometry (weight and height), sociodemographics (gender, age, occupational rank, and marital status of parents or legal guardians), lifestyle (energy intake, physical activity, and smoking habits), urine creatinine levels and detected levels of parabens in the three biological matrices.
Height data were obtained with a tallimeter (model SECA 214 (20–207 cm) and weight with a portable Tanita floor scale (model MC 780-S MA). Body mass index (BMI) was calculated as weight in kilograms divided by height squared in meters. The sample population was classified as underweight, normal weight, and overweight or obese, as described by Cole et al. [ 2000, 2007] [37,38]. According to this classification, we divided our population into two groups: cases which included overweight and obese children, and controls which included normal-weight children. The cut-off point was established in the equivalent value of 25 kg m−2 for adults.
Data on sociodemographic variables and lifestyle were obtained after completing a questionnaire supervised by a nutritionist. The occupational rank of the parents was determined based on the occupational classifications of the international standard [39]. Energy intake was obtained from three 24 h recalls. The questionnaire was addressed to the parents or legal guardians of the population and completed by nutrition professionals.
Urine creatinine levels were analysed by the Ángel Mendez Soto Clinical Analysis Laboratory. The classical Jaffé method [40,41] based on the photometric measurement of the kinetics of creatinine reaction with picric acid at 37 °C was employed. A kit of reagents was provided by Biosystems (Barcelona, Spain).
## 2.4. Determination of Parabens in Biological Samples
A total of 4 parabens (MetPB, EthPB, PropPB, and ButPB) were determined in nails, saliva, and urine samples. Saliva and urine samples were stored at −80 °C until laboratory treatment; nails were stored at room temperature. All samples were taken between 1 and 4 months since the questionnaire was completed. Parameters of validation, LOD, LOQ, recovery, calibration range, etc., can be checked in previously published works of our group research [31,32,42]. For paraben determinations in biological samples, three replicas/samples were analysed and the relative standard deviation was <$15\%$ in all cases.
## 2.4.1. Determination of Parabens in Saliva
Saliva ($$n = 89$$) was collected in wide-mouthed glass jars. The collection technique was performed by passively collecting saliva in the mouth and prior fasting by the subjects (overnight). Passive drool collection from children was carried out over several weekends until at least 4 g of sample was collected.
The methodology followed for the extraction is described in the article Moscoso-Ruiz et al., 2022 [31]. Briefly, for saliva treatment, 1 g of each saliva sample was weighed into a 10 mL glass tube to which 2 mL of acetonitrile was added. Additionally, 150 µL of acetic acid solution (0.1 M) was added. The mixture was vortexed for 30 s and centrifuged for 5 min. The supernatant was transferred to a 10 mL glass tube and evaporated to dryness. A volume of 1.5 mL of acetone as extraction solvent was added to the resulting residue, and ultrasound-assisted extraction was performed for 30 min at $35\%$ power. The mixture was centrifuged, and the supernatant was transferred to another 10 mL tube. The extraction was then repeated with 1.5 mL of ethanol (EtOH) using the same conditions. The supernatant was evaporated to dryness, reconstituted with 20 µL of methanol (MeOH) and 80 µL of ultrapure water, and then centrifuged. It was analysed with ultra-high-performance liquid chromatography coupled with a triple quadrupole tandem mass spectrometry (UHPLC-MS/MS) system.
## 2.4.2. Determination of Parabens in Urine
Polypropylene beakers were previously analysed to ensure they did not contain parabens and were used for urine collection ($$n = 149$$). Urine was collected first in the morning, prior to fasting subjects.
The methodology for the determination of total parabens in the urine samples followed the procedure outlined by Moscoso-Ruiz et al., 2022 [42]. First, enzymatic treatment was carried out by adding 25 µL of β-glucuronidase/sulfatase and 100 µL of β-glucuronidase to a 4 mL of sample to know the total content in parabens. Incubation time was 24 h at 37 °C. After that, it was added 4 mL of a $10\%$ (w/v) of sodium chloride aqueous solution and 100 μL of hydrochloric acid (6 N) until pH 2 was reached. Then dispersive liquid-liquid microextraction was performed. A mixture of 400 μL of acetone and 600 μL of chloroform was rapidly injected into the urine sample with a Hamilton syringe. Subsequently, the samples were gently vortexed and centrifuged for 5 min. The low phase was collected and transferred to another 10 mL glass tube. The extraction was repeated 4 times in total, and the organic phase was evaporated to dryness. The solid residue was reconstituted with 20 µL of MeOH and 80 µL of ultrapure water and, following centrifugation, was injected into the UHPLC-MS/MS system.
## 2.4.3. Determination of Parabens in Nails
Fingernails and toenails were collected over a 3-month period ($$n = 74$$). For the extraction and determination of parabens in nails, the methodology described by Martín-Pozo et al., 2020 [32] was followed. Individual nails were first cleaned, as per Martín-Pozo et al., 2020 to remove any external contamination [32]. Both fingers and toenails were lyophilized and crushed in a ball mill until the powder was obtained. Aliquots of 0.1 g of lyophilized nails were prepared with 1 mL of sodium hydroxide/MeOH (0.04 mol/L) and incubated at 30 °C for 15 h. The mixture was previously vortexed for 2 min. After incubation, the digested nails were cooled to room temperature and, following centrifugation for 10 min, were evaporated to dryness under a stream of nitrogen gas. The residue was reconstituted with 20 µL of MeOH and 80 µL of ultrapure water, centrifuged, and analysed in the UHPLC-MS/MS system
## 2.5. Statistical Analysis
Continuous and parametric variables (height and energy intake) were described as mean and standard deviation (SD), while continuous and non-parametric variables (weight, urinary creatinine levels, and paraben concentration in biological samples) were described as the median and interquartile range (IQR). Absolute and relative frequency distributions were calculated for categorical variables (gender, age, physical activity, smoking habits, educational level, and marital status of parents or legal guardians).
The Student’s t-test, Mann-Whitney U test, and Pearson’s chi-square test were used to assess the differences between cases and controls for parametric, non-parametric, and categorical variables, respectively.
A logistic regression analysis was used to determine the association between overweight and obesity (cut-off point equivalent to 25 kg m−2 in adults, according to Cole et al. [ 37,38]) as a dependent variable, and concentrations of parabens (ng g−1 or ng mL−1) in the three biological samples (nails, urine, and saliva) as independent variables. For all analyses performed, independent variables were dichotomized according to the median value (reference category: concentration ≤ median value) or according to [limit of detection (LOD)/√2] values for the analytes that were not detected in more than $30\%$ of samples [43]. Odds ratio (OR) and $95\%$ confidence intervals (CI) were calculated for crude and adjusted models. For the crude model, independent variables were included one by one separately, while for the adjusted model, these variables were included together with confounder factors using the ented method. Energy intake, physical activity, parents’ level of education, smoking habits, and marital status were all considered as confounder factors in the adjusted model when they modified the OR value by over $10\%$ in crude analysis. Regarding creatinine in urine samples, it was decided to use creatinine levels as an independent variable in all regression models rather than urine standardization. This approach is less likely to produce a skewed estimate of the effect [44]. Given that ButPB was detected in a very low percentage of samples analysed, this contaminant was not included in the logistic regression analysis.
SPSS v.23 (version 23, IBM® SPSS® Statistics, Armonk, NY, USA) was used for all statistical analyses; significance was set at $p \leq 0.05.$
## 3. Results
Characteristics of the study population are shown in Table 1. Weight and height were significantly higher in the case group ($p \leq 0.001$), while the parents’ level of education was significantly lower ($p \leq 0.001$). More children with married parents were found in the control group and more divorcee parents in the case group ($$p \leq 0.010$$). Regarding gender, age, energy intake, physical activity, and smoking habits, non-significant differences were observed between cases and controls.
After determining paraben concentrations in the three biological samples (Table 2), nails were found to be the matrix with the highest total paraben values ($p \leq 0.001$), EthPB ($p \leq 0.001$), PropPB ($p \leq 0.001$) and ButPB ($p \leq 0.001$) (data not showed). EthPB was higher in controls, but non-significant differences were observed with respect to the cases. PropPB and total parabens in urine were significantly higher among cases. Analytic methods used to determine parabens in nails did not show results for MetPB due to the high concentration of this compound in this matrix.
Table 3, Table 4 and Table 5 show the influence of exposure to MethPB, EthPB, and PropPB, and the total concentration of Parabens as determined by paraben presence in nails, urine, and saliva, respectively, on the overweight and obesity of the study population. The association of all of them (individually and in summation) with overweight and obesity was not significant. After adjusting for gender, age, creatinine level (for urine), and other confounding factors, OR values for PropPB, paraben totals in nails (Table 3), and all analytes, including total parabens in urine (Table 4) showed that for those values which were lower than median or LOD values, the study population showed lower body weight. However, this relationship was not significant.
## 4. Discussion
The objective of this work was to analyse the association between paraben concentrations in three biological matrices (nails, urine, and saliva) and overweight/obesity in children. The results found at least one of the four studied parabens in each biological sample. Data showed that those children with a BMI ≥ 25 kg m−2 have higher paraben concentrations in biological samples versus the control group (BMI < 25 kg m−2), although these findings are not statistically significant.
The effect of parabens on obesity has been analysed in different in vitro, in vivo, and epidemiological studies [22,45,46]. In vitro studies analysed the influence of parabens on adipocyte differentiation. Hu et al. [ 2013] [45] studied the effects of parabens on adipocyte differentiation, finding that the distinction is higher when the linear alkyl chain increases, with ButPB being the compound with more potency to modulate and promote the early phases of the differentiation. Additionally, the study tested the transactivation of the glucocorticoid receptor and/or the peroxisome proliferator-activated receptor (PPARγ) by parabens, obtaining an increment of potency when the radical chain is larger [45]. Both mechanisms are established as signalling pathways in adipocyte differentiation. Another study suggests that parabens may modulate stem cell fate by favouring the differentiation of adipocytes at the expense of osteoblast or chondrocytes since these cell types are known to share the same stem cell population [47]. In vivo studies directly analysed the influence of one or more parabens in a living organism. Boberg et al. [ 2008] [48] evidenced in rat foetuses that ButPB reduced levels of leptin, a hormone involved in body weight regulation. On the other hand, Leppert et al. [ 2020] [46] demonstrated in an in vivo mouse model that maternal exposure to ButPB increases food intake and weight gain in female offspring by neuronal dysregulation of satiety, involving the appetite-regulating gene Proopiomelanocortin.
Most of the epidemiological studies that analysed the effect of parabens on BMI used urine as a matrix. Kang et al. [ 2016] [22] studied the urine of 2541 Korean individuals aged 3 to 69 years old, finding that MetPB and PropPB concentrations were correlated in this matrix and were positively associated with BMI [22]. Li et al. [ 2019] [23] also found a moderate correlation between PropPB and BMI in the urine of pregnant women with gestational diabetes mellitus [23]. In 2020, another study in Canada used a population of 2564 children and adults aged 3 to 79 years old to evaluate, among other things, the relationship between the presence of parabens in urine and BMI and metabolic syndrome. As opposed to the previous study, Kim and Chevrier, 2020 found an inverse association between EthPB and BMI in women, and there were no associations in children’s populations [27]. However, the results of this investigation showed that EthPB in children’s urine had higher OR compared with the rest of the PBs and lower p, though still over 0.05. Lee et al. [ 2021] [24] found the same positive urinary correlation trend between EthPB and BMI in a population of 3779 adults aged 19 to 86 years old. Feizabadi et al. [ 2020] [26] studied parabens in 178 urine samples of Iranian adults, also finding a correlation between MetPB and PropPB levels; however, they did not find a correlation between this family of chemicals and BMI or waist circumference, except for MetPB displaying an inverse correlation [26]. Yet another study showed that urinary levels of parabens and BMI have no correlation whatsoever [49], the study population being 52 young Indian women between 18 and 31 years old. Another two studies obtained inverse correlations between parabens in urine and BMI [28,50]. Xu et al. [ 2022] [28] studied several EDCs in the urine of 300 Chinese people aged 2 to 80 years old, and Hajizadeh et al. [ 2020] [50] evaluated 95 Iranian pregnant women as the study population. Results from both papers showed that EthPB and BMI were inversely proportional to each other, while Hajizadeh et al. [ 2020] [50] found a possible relation between EthPB and PropPB levels.
To the best of our knowledge, only a few articles have studied the relationship between BMI and levels of PBs in children, using urine as a matrix. Berger et al. [ 2021] [21] studied a population of 309 children aged five using two different statistical models to study several phenols, both showing a correlation between PropPB in urine and BMI [21]. Guo et al. [ 2017] [20] studied a population of 436 children aged three, finding a direct correlation between levels of EthPB and BMI. Conversely, Quirós-Alcalá et al. [ 2018] [25] studied a 1324 children and adolescent population aged 6 to 19 years old inverse, finding a correlation between MetPB and BMI [25]. In the USA, Deierlein et al. [ 2017] [51] did not find a correlation between parabens and BMI in a population of 1017 girls between six and eight years old. Similarly, Güil-Oumrait et al. [ 2022] [29] did not observe a correlation in a population of 1015 Spanish children with a mean age of 11. The results of this study showed that MetPB and PropPB concentrations in urine did not show any significant statistical correlation with overweight/obesity.
Besides urine, three authors have used alternative biological matrices to analyse the relationship between paraben concentrations in biological samples and body weight. In 2017, van der Meer et al. [ 52] studied two different regions of the human brain in 24 subjects. Only MetPB was detected, and found no correlation with BMI [52]. Reimann et al. [ 2021] [53] studied the association between EthPB in the placenta and BMI, concluding that prenatal EthPB exposure may affect early childhood BMI [53]. Artacho-Cordón et al. [ 2018] [54] directly analysed adipose tissue samples in a population of 144 adults, given that $2\%$ of total parabens remain in body tissues after subcutaneous administration [55]. Results showed that almost $90\%$ of the samples were positive for ≥4 contaminants, MetPB and PropPB being the most prevalent parabens. However, they did not find a statistically significant correlation between these parabens and the weight of the individuals.
In this current research, nails and saliva were used due to the ease and non-invasiveness of their collection. Parabens in nails obtained a higher OR (2.18), but the results were not statistically significant. In saliva, the lower p-value corresponded to EthPB in an inverse relationship with overweight/obesity, although still not significant (>0.05). There are no other epidemiological studies involving these biological matrices. Table 6 shows a summary of consulted papers that study the relationship between BMI and the levels of parabens in a range of biological matrices.
To the best of our knowledge, this is the first study simultaneously involving parabens in three different biological matrices and their relationship with overweight/obesity. The available literature is scarce, and the majority of the works just use urine levels to study their correlation with overweight/obesity. Urine is the biological matrix traditionally used due to the ease of its collection and the high quantity of samples that can be obtained in a short period of time. Moos et al. [ 56] studied the hazard index and daily intake of parabens based on 24 h urinary levels, obtaining that MetPB and EthPB are below levels of health risk, but in $8.4\%$ of the population studied (660 people), levels of PropPB and ButPB were over the hazard intake. However, urine samples have some deficiencies, primarily that the exogenous toxins contained they contain primarily represent the diet of the previous $\frac{24}{48}$ h. In the case of the parabens in this research, the larger the radical chain of the paraben, the larger the persistence in the body. A study in 2016 analysed the metabolism and elimination of certain parabens via urine [57]. In the first 24 h, $80\%$ of the paraben dose introduced into the body was recovered; in the first 48 h, almost $85\%$ of MetPB was recovered, while only $80.6\%$ of ButPB was recovered. It can therefore be concluded that PBs bioaccumulate, and hence the information that urine provides us is limited.
In this study, children’s nails and saliva were also analysed. The mouth represents the first step in digestion, being the point of entry for external dietary contamination. Saliva is secreted by the salivary glands, which have high blood flow, and chemicals and their metabolites pass into the saliva via different mechanisms. Saliva has been widely employed for biomonitoring medicines or drugs, although its use for environmental exposure is not yet thoroughly studied [58]. Some studies exist comparing salivary and blood contamination, obtaining comparable results [59]. On the other hand, nails provide information about long-term exposure to contaminants, their collection is easy, and their storage at room temperature is straightforward. Nails have traditionally been used as a biomarker for metals, specifically arsenic [60,61]. However, they have not been explored sufficiently as a biomarker for emerging contaminants, despite their simple composition making them a good matrix to analyse body pollutants.
## Strengths and Weaknesses
This is the first study involving several child biological matrices that explores an association between parabens and body weight. To the best of our knowledge, there are no other epidemiological studies that analyse the relationship between parabens in more than one matrix, as well as with obesity/overweight. Childhood is a critical window of exposure to obesogens, and it has been demonstrated that obese children have more risk of suffering adulthood obesity, not to mention a series of other pathologies related to overweight/obesity. It is, therefore, essential to investigate external factors that promote this health impairment. It is also essential to explore options of matrices, especially non-invasive ones, to better understand the bioaccumulation of these contaminants.
The principal weakness of this study was the sample size. One hundred sixty children participated, and, as per the literature, similar numbers of the individual have been included in studies that analyse the relationship between parabens in biological matrices and obesity [26,49,52,54]. The difficulty of access to children compared to an adult population, exacerbated by the COVID-19 situation, should be taken into account.
## 5. Conclusions
This is the first study to report on paraben concentrations in a range of biological matrices from children and the relationship of these parabens with obesity/overweight. This work explores biological samples to measure the accumulation of obesogens which have not been widely studied yet. Our results show that there are no statistically significant relationships between the presence of parabens in the studied biological samples and body weight. Other epidemiological articles relating PBs to obesity were consulted, and as yet, there is no consensus on the effect of these compounds on body weight. However, as this study confirms, given the omnipresence of parabens in children’s bodies, coupled with increasing childhood obesity worldwide, more investigation is necessary to clarify a possible relationship. These findings could be a basis for future research on the effect of parabens on childhood body weight using nails as a biological matrix due to the ease and non-invasiveness of their collection and the fact that pollutants bioaccumulate in this matrix, thus revealing data on long-term exposure.
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|
---
title: Water Extract of Chrysanthemum indicum L. Flower Inhibits Capsaicin-Induced
Systemic Low-Grade Inflammation by Modulating Gut Microbiota and Short-Chain Fatty
Acids
authors:
- Bing Yang
- Dongfang Sun
- Lijun Sun
- Yaokun Cheng
- Chen Wang
- Lianhua Hu
- Zhijia Fang
- Qi Deng
- Jian Zhao
journal: Nutrients
year: 2023
pmcid: PMC10005712
doi: 10.3390/nu15051069
license: CC BY 4.0
---
# Water Extract of Chrysanthemum indicum L. Flower Inhibits Capsaicin-Induced Systemic Low-Grade Inflammation by Modulating Gut Microbiota and Short-Chain Fatty Acids
## Abstract
Systemic low-grade inflammation induced by unhealthy diet has become a common health concern as it contributes to immune imbalance and induces chronic diseases, yet effective preventions and interventions are currently unavailable. The *Chrysanthemum indicum* L. flower (CIF) is a common herb with a strong anti-inflammatory effect in drug-induced models, based on the theory of “medicine and food homology”. However, its effects and mechanisms in reducing food-induced systemic low-grade inflammation (FSLI) remain unclear. This study showed that CIF can reduce FSLI and represents a new strategy to intervene in chronic inflammatory diseases. In this study, we administered capsaicin to mice by gavage to establish a FSLI model. Then, three doses of CIF (7, 14, 28 g·kg−1·day−1) were tested as the intervention. Capsaicin was found to increase serum TNF-α levels, demonstrating a successful model induction. After a high dose of CIF intervention, serum levels of TNF-α and LPS were reduced by $62.8\%$ and $77.44\%$. In addition, CIF increased the α diversity and number of OTUs in the gut microbiota, restored the abundance of Lactobacillus and increased the total content of SCFAs in the feces. In summary, CIF inhibits FSLI by modulating the gut microbiota, increasing SCFAs levels and inhibiting excessive LPS translocation into the blood. Our findings provided a theoretical support for using CIF in FSLI intervention.
## 1. Introduction
Certain foods, such as chili, litchi, pepper, etc., are known as “heating” foods in traditional Chinese medicine. The excessive consumption of “heating” foods may cause a number of disorders, such as red and swollen eyes, acne, sores and ulcers in the mouth and tongue, swollen gums, sore throat, yellow urine, constipation and other symptoms; these symptoms are known as “shanghuo” (heating-up) in Chinese medicine [1,2,3]. Modern medicine defines “shanghuo“ as a kind of systemic and chronic low-grade inflammation characterized by a significant increase in inflammatory factors such as tumor necrosis factor-α (TNF-α) [4]. Prolonged systemic low-grade inflammation can cause substantial damage to the body and induce chronic diseases such as obesity, diabetes and depression [5,6,7]. The mechanism of food-induced systemic low-grade inflammation (FSLI) has yet to be established. Generally, nonsteroidal anti-inflammatory drugs (NSAIDs) are used to treat high-grade inflammation clinically. However, FSLI is a long-term condition that requires prolonged periods of medication, but such long-term treatment with NSAIDs can induce a number of side effects in the gastrointestinal tract, liver, nervous system and other organs of the body [8]. Therefore, persistent FSLI requires more appropriate prevention and intervention strategies with fewer side effects. Traditional Chinese medicine believes that medicine and food have a homologous relationship, and food components can also act as medicine to prevent and treat various disorders. Thus, according to this tradition, some foods are commonly used to replace anti-inflammatory drugs to inhibit inflammatory “shanghuo”. The *Chrysanthemum indicum* L. flower (CIF) is one such herb that is often added to tea to relieve sore throats. Several reports have shown that CIF has antioxidant activity in vitro [9,10,11,12]. However, the mechanism of action of CIF in FSLI has yet to be elucidated to provide a solid scientific foundation for its applications as a herbal medicine in preventing and treating FSLI.
Previous studies have shown that FSLI is associated with a diet-induced disorder in the intestinal microbiota [13,14]. However, in most of the existing studies, the direct injection of LPS was used to establish the inflammatory model, which may not represent the situation of low-grade inflammation induced by unhealthy diet. To overcome this problem, a food-induced model of systemic low-grade inflammation needs to be established. Studies have reported that the excessive consumption of chili in some people can induce inflammatory “shanghuo” [1,15,16]. However, the role of gut microbiota in the process of inflammation induced by chili has not been reported in the literature.
In this study, capsaicin, a major component of chili (a representative “heating” food), was used to establish a FSLI model by the oral gavage of mice for 7 days. The CIF were then orally administered to the mice at different doses as the treatment. Inflammatory factors, gut microbiota and SCFAs were analyzed to determine their correlations. The objective of the study was to investigate the anti-inflammatory effect of CIF on capsaicin-induced FSLI and elucidate its mechanism of action, so as to provide a theoretical basis for the development of functional foods and nutritional supplements with efficient anti-systemic inflammation effects.
## 2.1. Preparation of CIF Extract
Dry CIF were purchased from Antai Biotechnology Co., Ltd., (Shenzhen, China). They were mixed with distilled water in 1:8 ratio (w/w), heated and simmered for 1 h for three sessions, followed by filtration with busher funnel. The filtrate was concentrated to 400 mL at (47 ± 1 °C) by rotary evaporator (N-1100V-WB, Tokyo Physicochemical Equipment Co., Tokyo, Japan). The concentrate was desiccated in a laboratory oven for 62 h at 60 °C to produce the CIF powder. The CIF powder was stored at −80 °C in a refrigerator and diluted to 0.4, 0.2, 0.1 g/mL with pure water before use. The main components of CIF extract were 3,4-dihydroxybenzoic acid, chlorogenic acid, luteoloside and linarin. The extraction methods and the main components are reported in the literature [17,18].
## 2.2. Preparation of Laboratory Animals
Six-week-old female C57BL/6 J mice, each weighing 20 ± 2 g, were purchased from Beijing HuaFukang Biotechnology Co., Ltd., (Beijing, China) and raised in specific pathogen-free (SPF) conditions, with the license number SCXK (Beijing) 2014007. The mice were first acclimated to an environment of 25 ± 2 °C, humidity $55\%$ ± $5\%$ and a 12 h light/dark cycle. During the experiment, adequate feed and water were provided. After 3 days of adaptive feeding, the mice were randomly divided into 5 treatment groups: model group (CHCB group), low-dose group (CHL group), medium-dose group (CHM group) and high-dose group (CHH group), along with a blank control group (CHCA group). Mice in treatment groups were treated with 14.4 g·kg−1·day−1 capsaicin (purity > $99\%$, Guangzhou Bosen Pharmaceutical Co., Ltd., Guangzhou, China) solution orally for 7 days. Starting from day 10, mice in the CHL, CHM and CHH groups were treated with 7, 14 and 28 g·kg−1·day−1 of CIF for 3 days. The lowest dose used in this study was 567 mg/kg as human dose, which was within the range of 278.17 ± 358.0 mg/kg reported in the literature [19]. To convert human dose to mice dose, multiply human dose by 12.3 to get 7 g·kg−1 according to the literature [20]. The medium and high doses were 2 times and 4 times those of low doses, respectively.
## 2.3. Determination of LPS and Inflammatory Factors in the Serum of Mice
At the end of treatment, the mouse blood was collected into a pyrogen free centrifuge tube. The blood was maintained at 25 °C for 30 min and centrifuged in a refrigerated centrifuge at 8000 rpm and 4 °C for 5 min and the serum was collected. Lipopolysaccharide (LPS) in the serum was assessed using the limulus amebocyte lysate kit (Xiamen Limulus Reagent Biotechnology Co., Ltd., Xiamen, China). Serum levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6) and interleukin-10 (IL-10) were determined by respective ELISA kits (Shenzhen Xinbosheng Biotechnology Co., LTD., Shenzhen, China).
## 2.4. Analysis of Mouse Gut Microbiota
Mice feces were directly collected using a 1.5 mL sterile centrifuge tube on the last day of the experiment. Fecal samples were analyzed by 16SrDNA high-throughput sequencing method. Then, sample species information was obtained by comparing with database to obtain species category of gut bacteria. The methods of analyzing gut microbiota followed the methods described in [21].
## 2.5. Determination of SCFAs Content
A 50 mg fecal sample (collected in 2.4) was added to 100 μL of $15\%$ phosphoric acid, 100 μL of 125 μg/mL isohexic acid solution as internal standard and 900 μL of ether and the mixture was homogenated for 1 min. The suspension was centrifuged at 12,000 RPM and 4 °C for 10 min and the supernatant was filtered through a 0.22 μm organic micropore membrane. Acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid and isovaleric acid in the filtrate were analyzed by gas chromatography mass spectrometry (GDC/GC-MS-QP2010, Shimadsu, Japan). A total of 1 μL of sample was injected into GC-MS, which was equipped with a VF-Wax column. Helium was the carrier gas at a flow rate of 1.0 mL/min. The injection temperature was 250 °C and the ion source temperature was 230 °C.
## 2.6. Statistical Analysis
SPSS 24.0 was used to analyze all the results, and each analysis was replicated 5 times ($$n = 5$$). All experimental data were expressed as mean ± SD. One-way Analysis of Variance (ANOVA) was performed to compare means in different groups, and a value of $p \leq 0.05$ was considered statistically significant.
## 3.1. Effects of Capsaicin and CIF on the Levels of Serum Inflammatory Factor in Mice
To determine whether capsaicin treatment can induce inflammation in mice and the effects of CIF on capsaicin-induced inflammation, the typical inflammatory factors TNF-α, IL-1β, IL-6 and IL-10 in the mouse serum were measured (Figure 1). Compared with the CHCA group, the serum TNF-α level in the CHCB group was increased by $105.8\%$ ($p \leq 0.05$), while IL-6 and IL-10 were decreased by $70.4\%$ and $30.4\%$ ($p \leq 0.05$), respectively. These results demonstrated that capsaicin gavage successfully induced FSLI in the mice. On the other hand, compared with the CHCB group, the TNF-α level of mice in the CHM and CHH groups decreased by $54.9\%$ and $62.8\%$ ($p \leq 0.05$), respectively. Furthermore, treatment with high-dose CIF increased the levels of IL-6 in the blood of mice by $150.4\%$ ($p \leq 0.05$), while the levels of IL-10 in serum significantly increased ($p \leq 0.05$) in all groups. In sum, CIF administration led to decreases in the proinflammation factors (TNF-α) and increases in the level of anti-inflammation factors (IL-6 and IL-10) in the serum of FSLI mice.
## 3.2. Effects of Capsaicin and CIF on Serum LPS Levels in Mice
Lipopolysaccharide(LPS) is a major component of the cell wall of gram-negative bacteria, and can induce inflammation in vivo when circulating in the blood of the organism [22]. The LPS in the mouse serum was measured in this study (Figure 2). Compared with the CHCA group, LPS concentration in the serum of mice in the CHCB group increased 2.33-fold ($p \leq 0.05$). After intervention with medium and high doses of CIF, the LPS levels in the treatment groups decreased by $75.56\%$ and $77.44\%$, respectively ($p \leq 0.05$). These results further confirmed that capsaicin gavage induced FSLI in the mice, and treatment with medium and high doses of CIF effectively reduced LPS in the FSLI mice.
## 3.3. Effects of Capsaicin and CIF on the Diversity of Intestinal Microflora in Mice
Alpha (α) diversity is one of the important indices reflecting the abundance, evenness and diversity of gut microbiota. As shown in Table 1, the Shannon, Simpson, Chao1 and Ace indices of the CHCB group were significantly lower ($p \leq 0.05$) compared with the CHCA group, demonstrating that feeding mice with capsaicin significantly reduced the abundance and diversity of gut microbiota in the mice. On the other hand, all of the indices in the CHL and CHH groups were significantly higher than in the CHCB group ($p \leq 0.05$), suggesting that the intervention with low and high doses of CIF enhanced and restored the richness and diversity of gut microbiota that had been affected by feeding the mice with capsaicin.
The Venn diagram presents a breakdown of the common and unique operational taxonomic unit (OTU) numbers among each group (Figure 3). Compared with the 287 OTUs in the CHCA group, a markedly reduced number of 101 OTUs were found to be unique to the CHCB group. The number of OTUs endemic to the CHCB group was 37, which was considerably lower than the 89 OTUs endemic to the CHL group and 68 OTUs endemic to the CHH group (Figure 3A). The distances between the samples were determined in the principal coordinate analysis (PCoA), which reflects the difference in β diversity of gut microbiota (Figure 3B). The results were not significant, suggesting similar structural characteristics of gut microbiota in each group. In sum, the feeding of mice with capsaicin reduced the richness and diversity of their gut microbiota, while treatment with CIF restored and enhanced the richness and diversity.
## 3.4. Effects of Capsaicin and CIF on Gut Microbiota at Phylum and Genus Levels in Mice
Figure 4 shows the effects of capsaicin and CIF on the gut microbiota composition of mice. At the phylum level, the abundance of Bacteroidetes and TM7 in the CHCB group decreased by $79.63\%$ and $97.99\%$, respectively, while the abundance of Firmicutes increased by $86.15\%$ ($p \leq 0.05$) compared with the CHCA group, suggesting that the structure of the gut microbiota was significantly altered by feeding the mice with capsaicin. After CIF intervention, however, the abundance of TM7 was significantly increased 7.73-, 2.90- and 7.90-fold in the CHL, CHM and CHH groups, respectively, compared with the CHCB group, while the abundance of Bacteroidetes did not change significantly ($p \leq 0.05$) (Figure 4A). These results suggest that the CIF restored the structural changes in gut microbiota caused by capsaicin.
At the genus level, feeding the mice with capsaicin caused the abundance of Xanthomonas, Stenotrophomonas, Anaerofustis and Lactobacillus in the CHCB group to increase 340.87-, 730.97-, 6.38- and 6.49-fold ($p \leq 0.05$), while the abundance of Ruminococcus was decreased by $65.5\%$, compared with the CHCA group. However, CIF intervention resulted in the levels of Xanthomonas and Stenotrophomonas in inflamed mice decreasing by more than $99.7\%$ compared with the CHCB group. The abundance of Anaerofustis in the CHL and CHM groups decreased by $76.62\%$ and $83.25\%$ ($p \leq 0.05$), respectively, compared with the CHCB group. The levels of Lactobacillus in the CHL, CHM and CHH groups decreased by $28.57\%$, $84.94\%$ and $71.40\%$, respectively ($p \leq 0.05$), while the abundance of Ruminococcus species increased by $137.74\%$, $61.50\%$ and $263.90\%$, respectively, compared with the CHCB group.
In sum, feeding the mice with capsaicin caused a significant decrease in the species diversity of their gut microbiota, and changes in the dominant species, while CIF treatment restored the species diversity reduced by capsaicin.
## 3.5. Correlation between Inflammatory Factors and Gut Microbiota
To find the relationship between capsaicin-induced inflammation and the gut microbiota in the mice as well as the mechanism of the CIF treatment, the Pearson correlation coefficient was used to analyze the correlation between inflammatory factors and gut microbiota (Figure 5). The change in the proportion of Verrucomicrobia was positively correlated with LPS. There was a negative correlation between Tenericutes and IL−10 level. The number of Lactobacillus had a significant positive correlation with LPS and TNF−α ($p \leq 0.05$), while the number of Akkermansia was correlated with the level of LPS ($p \leq 0.05$). In addition, RF−39 was negatively correlated with the level of IL−10 ($p \leq 0.05$).
## 3.6. Effects of Capsaicin and CIF on SCFAs Content of Feces in Mice
Feeding the mice with capsaicin led to significant reductions in the level of SCFAs in the feces of the mice (Figure 6). The levels of acetic acid, propionic acid, butyric acid, isobutyric acid and total SCFAs in the CHCB group were reduced ($p \leq 0.05$) by $75.5\%$, $73.1\%$, $67.0\%$, $58.5\%$ and $74.3\%$, respectively, compared with those in the CHCA group, while the concentrations of valeric acid and isovaleric acid did not differ significantly. On the other hand, the treatment of the capsaicin-fed mice with CIF resulted in restoration and increases in the SCFA levels in the mice. Compared with the CHCB group, the total SCFA production in the CHL, CHM and CHH groups increased by $357.0\%$, $751.6\%$ and $549.5\%$, respectively ($p \leq 0.05$), while butyric acid in these groups increased ($p \leq 0.05$) by $23.1\%$, $287.3\%$ and $25.9\%$, respectively. Total SCFAs and acetic, propionic, isobutyric, isovaleric and valeric acids were increased ($p \leq 0.05$) by $843.1\%$, $650.2\%$, $242.0\%$, $227.9\%$ and $746.6\%$ in the CHM group, respectively, compared with the CHCB group. The levels of these acids in the CHH group increased by $632.5\%$, $565.7\%$, $127.7\%$,$84.5\%$ and $167.5\%$, respectively, compared with the CHCB group. Overall, these results indicate that capsaicin intervention significantly reduced the production of SCFAs in mice, while treatment with CIF significantly increased the production of SCFAs. Medium and high doses of CIF effectively increased the synthesis of SCFAs in mice.
## 4. Discussion
Previous studies use direct LPS injection to induce systemic low-grade inflammation in laboratory animals such as mice [23]. The drawback of this practice is that the inflammatory effects induced by LPS may not be the same as or accurately represent those induced by unhealthy diet. To overcome this drawback, an inflammation model induced by food components is needed. Capsaicin is the component in chili that gives the “hot” sensation and is widely believed in Chinese medicine to cause “shanghuo” or heating up symptoms in the body. Some studies have reported the beneficial effects of low-dose capsaicin on the health of mice [24,25,26], while others have shown that high doses of capsaicin can cause intestinal inflammation and physiological disorders [27,28,29,30]. In the present study, a FSLI model via the oral administration of high doses of capsaicin was established. Our results showed that high doses of oral capsaicin significantly increased the serum TNF-α levels in mice to almost twice those of the control group, indicating that excessive capsaicin can induce FSLI in mice. Our findings demonstrate the feasibility of establishing a model of FSLI using capsaicin, which was reported here for the first time.
Chrysanthemum indicum L. is a herb that is commonly added to tea or infused directly as a tea analogue in China to prevent or alleviate “hotting up” symptoms, especially in summer time. People usually add 25–50 g of CIF to 250–500 mL of water, so many studies use the water extract of CIF with concentrations in the range of 0.1–0.2 g/mL [31,32]. The lowest concentration in this study was 0.1 g/mL, which was in line with people’s habitual intake. As a Chinese herbal medicine, the average human equivalent dose value is 278.1 ± 358.0 mg/kg, and the values for single-herb are 322.7 ± 488.4 mg/kg [19]. The lowest dose used in this study was 567 mg/kg as a human dose, which was within the range reported in the literature. Medium and high doses were 2 times and 4 times those of low doses, respectively. Chrysanthemum indicum L. is rich in polyphenols such as luteolin, caryolane, acacetin, apigenin, which have been shown to have anti-inflammatory activity [33]. However, there is relatively scant information on the effect of CIF as a whole in treating systemic low-grade inflammation. In this study, we found that exposure to medium and high doses of CIF significantly reduced the levels of the inflammatory factor TNF-α, which was significantly increased by feeding the mice with capsaicin, implying that CIF was able to alleviate capsaicin-induced systemic inflammation. Also, we found that the serum IL-6 level decreased significantly in capsaicin-induced systemic inflammation, contrary to previous reports that cancer-related chronic inflammation is accompanied by an increase in IL-6 [34]. Treatment with high-dose CIF led to significantly elevated levels of IL-6, restoring it to the levels in the control group. Furthermore, feeding capsaicin to mice significantly reduced their level of serum IL-10. Previous reports have shown that IL-10 is an anti-inflammatory factor that inhibits the synthesis of pro-inflammatory factors [35]. The IL-10 levels in the mice significantly increased after the CIF intervention. Taken together, these results demonstrated that CIF was able to reduce systemic low-grade inflammation induced by capsaicin by lowering the proinflammation factors such as TNF-α and increasing the level of anti-inflammation factors including IL-6 and IL-10.
Previous studies have shown that excessive LPS translocation into the blood is one of the main causes of systemic inflammation [36,37]. However, it is not clear whether food-induced FSLI is mediated via a similar mechanism. The results obtained in this study showed that the concentration of LPS in the inflamed mice was more than twice that of the control group, suggesting that high-dose capsaicin promoted excessive LPS translocation into the blood and induced systemic inflammation. On the other hand, CIF treatments significantly decreased the levels of LPS in serum, indicating that CIF effectively prevented the release of a large amount of LPS into the blood, and thereby reduced the level of inflammation. This agreed with previous in vitro cell culture studies that showed that CIF treatments reduced LPS-induced inflammation. As the concentration of LPS in blood is implicated in intestinal mucosal permeability, it can be speculated that CIF might reduce the intestinal barrier permeability, thus preventing the release of LPS into the blood and reducing the level of inflammation. However, further studies are needed to confirm the effects of enhancing intestinal barrier integrity.
In the past 10 years, there have been numerous studies investigating the role of dietary intake in altering gut microbiota. However, these studies are focused on the effects of major food components such as fat, sugar and dietary fiber on the gut microbiota, while relatively few studies have examined the effect of minor components such as capsaicin on gut microbiota and their relationship with systemic inflammation, especially induced by unhealthy diet. An appropriate amount of capsaicin is found to have a positive regulatory effect on the structure and function of gut microbiota [25]. In the present study, high-dose capsaicin caused significant decreases in both the abundance and diversity of the gut microbiota of mice. Moreover, the structure of microflora also changed significantly. Gut microbiota have been reported to play a key role in the development of inflammation [38], and alterations of gut microbiota caused by capsaicin are a potential pathway for its inflammatory effects. On the other hand, intervention with medium and high doses of CIF restored the abundance and diversity of gut microbiota, indicating that CIF probably inhibited FSLI by modulating the structure of gut microbiota. One unanticipated finding was that the abundance of Lactobacillus increased significantly after the oral administration of high-dose capsaicin, while the Pearson correlation analysis indicated that the number of Lactobacillus were significantly related to the Serum LPS and TNF-α content. This result contradicted the common belief that an increased abundance of Lactobacillus can reduce inflammation levels [39,40,41], indicating that large increases in Lactobacillus may also contribute to systemic inflammation. Wang et al. found that CIF can decrease the abundance of Lactobacillus in metabolic hypertensive rats [42], but no studies have shown whether CIF can decease the abundance of Lactobacillus in a FSLI model. This study found that intervention with medium and high doses of CIF can decrease the amount of Lactobacillus increased by high-dose capsaicin, and the abundance of Lactobacillus was not significantly different with the control group, which partially agreed with our results. One of the main phenolic components of CIF, chlorogenic acid, has been reported to improve the relative abundance of Lactobacillus in a mouse model of dextran sulfate sodium-induced colitis [43]. However, the results of this study found that CIF decreased the abundance of Lactobacillus. The effect of CIF may be to restore the abnormal abundance of Lactobacillus induced by high-dose capsaicin, rather than simply affecting its rise or fall. Pearson correlation analysis indicated that the number of Lactobacillus were significantly related to the Serum LPS and TNF-α content, which is closely related to the level of FSLI. The large increase in Lactobacillus may potentially contribute to capsaicin-induced gut microbiota disorder and inflammation, but further studies are needed to confirm the hypothesis. After the intervention with medium and high doses of CIF, the abundance of Lactobacillus became comparable to that of the control group, indicating that CIF can effectively rebalance the structure of gut microbiota by restoring the abundance of Lactobacillus. The main components of CIF are polyphenols; most polyphenols (90–$95\%$) cannot be absorbed by the gastrointestinal tract directly, but are transported to the colon and synthesized by specific bacteria to synthesize SCFAs [44].
Short-chain fatty acids play a key regulatory role in a variety of metabolic functions of the host. Butyric acid can adjust tight junction proteins to enhance the function of the intestinal barrier, while acetic acid and butyric acid inhibit intestinal inflammation [45,46,47]. Medium and high doses of CIF significantly increase the concentrations of acetic acid and butyric acid in mouse feces. Further, CIF treatment significantly increased the abundance of Ruminococcaceae, which are a family of butyrate-producing bacteria, suggesting that CIF promotes the synthesis of butyric acid, with the consequent enhancement of the intestinal barrier function, which in turn can hinder the entry of LPS into the blood and reduces the level of inflammation.
## 5. Conclusions
This study shows that excessive amounts of capsaicin can trigger gut microbiota disorder, increase the degree of LPS released into the blood and induce low-grade inflammation in the system. Therefore, capsaicin can be used to establish a FSLI model. On the other hand, treatments with medium and high doses of CIF can help restore the structure of gut microbiota, increase the production of SCFAs such as butyric acid, prevent the entry of LPS into the blood and inhibit FSLI. These findings provide scientific evidence to support the use of CIF for preventing and treating inflammation induced by unhealthy diet and are a foundation for the development of CIF-based functional foods and drinks for the prevention of chronic low-grade inflammation-related disorders.
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|
---
title: 'Elementary School-Aged Children’s and Parents’ Report of Health-Related Quality
of Life and Relationships with Lifestyle Measures: A Cross-Sectional Study'
authors:
- Soili Alanne
- Ella Koivuniemi
- Eliisa Löyttyniemi
- Kirsi Laitinen
journal: Nutrients
year: 2023
pmcid: PMC10005714
doi: 10.3390/nu15051264
license: CC BY 4.0
---
# Elementary School-Aged Children’s and Parents’ Report of Health-Related Quality of Life and Relationships with Lifestyle Measures: A Cross-Sectional Study
## Abstract
Supporting a child’s health-promoting lifestyle is an investment in their future health and health-related quality of life (HRQoL). Particularly children with overweight and obesity may be at an increased risk of a poor HRQoL. Currently, a comprehensive evaluation of lifestyle factors and age in relation to HRQoL in healthy children and, further, separate child and parental proxy-reports of HRQoL are lacking. The aims of this cross-sectional study in Finland are to compare healthy elementary school-aged children’s and parents’ reports of the child‘s HRQoL, and to view them in relation to lifestyle markers. The HRQoL was measured with Pediatric Quality of Life InventoryTM 4.0, and the following lifestyle markers: leisure-time physical activity as MET, diet quality via a validated index (ES-CIDQ), sleeping time and screen time by questionnaires. Furthermore, age and BMI were recorded. Data were obtained from 270 primary school-aged children (6–13 years). Female gender, the child’s older age (8–13 years), high physical activity level and less screen time were strong predictors of a higher HRQoL in both the child’s and parental proxy-reports. Means to promote healthy lifestyles should be particularly targeted to young children, especially boys, and new ways to promote physical activity and other forms of free-time activities should be sought.
## 1. Introduction
Influencing in a positive way a child’s lifestyle is an investment in their future health. Therefore, health-promoting habits such as encouraging healthy eating, participating in physical activity, ensuring adequate hours of sleep, and a low amount of sedentary behaviours should be supported from early childhood onwards. According to the Finnish Current Care Guideline for obesity in children, adolescents, and adults, the promotion of healthy lifestyles is crucial for both the prevention and treatment of obesity [1]. In global terms, the prevalence of childhood obesity has increased and remains high in many countries [2]. In Finland, about one in four preschool-aged boys ($24\%$) and every seventh girl ($15\%$) were at least overweight, and by the time they reach elementary school, the prevalences increased to $28\%$ in boys and $18\%$ in girls [3].
Children are prone to be influenced by their everyday surroundings, not only by their family, friends, peers, but also by the media. Satisfaction with life reflects how a person in their social surroundings experiences the quality of their life against peers, and the children do not represent an exception. The health-related quality of life (HRQoL) is defined as the subjective assessment of the impact of disease and treatment across the physical, psychological, social, and somatic domains of functioning and wellbeing [4]. The HRQoL measures are generic questionnaires intended to obtain a subjective opinion by the respondents about their own health and wellbeing. It is important to take children‘s views into account when making decisions that affect them. The younger the children are, the more relevant it is to have access to a parallel parental proxy report of their child’s HRQoL [5]. Although studies are few, it has been noted that there are differences between children’s and parents’ reports; these vary according to the child’s age and gender [6]. The extent of agreement between the child’s and the parent’s reports has been shown to be affected by the child’s age, the domains investigated, and the parent’s own quality of life [7]. For example, parents tend to underestimate their child’s HRQoL when they have a congenital health condition [8] or a chronic illness [9]. Differences between boys’ and girls’ reports (aged 5 to 18 years) have also been detected; girls report their HRQoL as poorer than boys [10]. When comparing mean total scores of HRQoL across studies, children below the age of seven and adolescents above the age of 12 had higher HRQoL scores and thus better HRQoL [10].
Several previously published papers have focused predominantly on obese and overweight children [11,12]; they have revealed that obesity contributes to a lower HRQoL [11,13], lower physical, and psychosocial HRQoL [11]. However, the extent to which lifestyle factors, particularly diet intake, contribute to HRQoL were not inspected in these studies. Only recently, eating behaviours and especially adherence to the Mediterranean diet have been considered [14,15] There are also some studies which have focused on healthy children, regardless of their body weight. A high amount of screen time, a short sleep duration, and low physical activity were reported to be linked with poor HRQoL [16]. A systematic review evaluated the studies that had reported associations between physical activity, sedentary behaviours, and HRQoL in children and adolescents, and concluded that a higher level of physical activity with less time being spent on sedentary behaviours was associated with increased HRQoL among children and adolescents [17].
With respect to diet, a good diet quality and healthy dietary behaviours were associated with increased HRQoL in children [14] and adolescents [18]. There are, however, very few reports in which all important lifestyle factors and age have been evaluated in the same study in healthy children, and further, included a separate child and parental proxy-report of HRQoL. In the current cross-sectional design-based study, HRQoL was compared against lifestyle factors in elementary school-aged children from the ages of 6 to 13 years. The hypothesis was that health-promoting lifestyle habits would be related to better HRQoL in the reports of children and their parents. In aging societies the health of children is an even more important factor for future and needs to be followed up on regularly.
The aims of this study are to measure elementary school-aged children’s HRQoL, to compare children’s and the parental reports of their child‘s HRQoL, and to model the lifestyle factors including diet quality, physical activity, screen time, sleep duration, as well as the child’s and the parents’ body mass index in association with the child’s and parents’ proxy report of HRQoL.
## 2.1. Subjects
In this cross-sectional study, a random sample of 5000 families was approached through the Finnish Population Information System, as well as through their hobbies and schools. Information about the study was mailed and posted to the parents, with the invitation to participate in the study sent between March 2017 and February 2018. Parents contacted the investigators via telephone or email if they were interested to participate in this study, and the study visit was arranged. The inclusion criteria were that children were elementary school aged, from grades 1 to 6, from eastern Finland (Kuopio) and southwest Finland (Turku) areas. In Finland, basic education is provided for all youngsters between 7 and 15 years (grades 1 to 6 in elementary school and grades 7 to 9 in upper elementary school). Pre-primary education starts one year before basic education at the age of 6. The children and their families had to have sufficient skills in the Finnish language in order to fill in the questionnaires. Exclusion criteria were: inability to give informed consent for the study, not able to fill in the questionnaires, and parents’ young age (age <18 years). Children with a chronic or serious disease (cancer, surgical treatment) or disability, multiple food allergies or those adhering to a special diet (gluten-free diet, vegan diet) were also reasons of exclusion (except one child with stable type 1 diabetes was included). The study design and participants have been previously described in detail [19].
## 2.2. Measurement of HRQoL
The measurements of HRQoL were made with the Pediatric Quality of Life InventoryTM 4.0 (PedsQLTM 4.0) Generic Core [20], which is a validated measure in this age group of children in Finland [6,21,22]. The PedsQLTM 4.0 comprise parallel child self-report and parental proxy-report formats. The child’s self-reports were inquired of children aged 5–7 and 8–12 [20,23]. The parental proxy report was targeted at parents with children aged 5–7 and 8–12 years and assessed the parents’ perceptions of their child’s HRQoL. The items in the children’s forms are identical, differing only in the questions being framed in developmentally appropriate language. The instructions inquire how much of a problem each item has been during the past month. In the child’s self-report for ages 8–12 and parental proxy reports, a five-point response scale is utilised (never, almost never, sometimes, often, almost always). For the younger children (ages 5–7), the response scale is a three-point scale (not at all, sometimes, a lot of), with each response choice anchored to a happy to a sad face scale [20,23]. PedsQLTM 4.0 consists of 23 items which assess physical functioning (8 items), emotional functioning (5 items), social functioning (5 items) and school functioning (5 items). Items were reverse-scored and converted into a 0–100 scale. Scores were calculated as the sum of the items and divided by the number of items answered (scores were not calculated if more than $50\%$ of the items were missing). There is one total scale score that describes the total HRQoL, four subscales for physical, emotional, social and school functioning, and two summary scores—physical and psychosocial health. Higher scores indicate better HRQoL [20].
## 2.3. Diet Quality
Diet quality was measured with the elementary school-aged Children’s Index of Diet Quality (ES-CIDQ). This index has been developed in the context of this study and was based on the food consumption evaluated by a Food Frequency Questionnaire (FFQ) and nutrient intakes calculated from a 5-day food diary. The development process of this index has been described in detail [19]. The total ES-CIDQ score ranges from 0 to 16.5 points. The overall diet quality was defined as poor (score <6 points) or good (score ≥6 points). The criteria for considering a diet to be health-promoting included the intake of sucrose, saturated fatty acid intake, dietary fiber, vitamin C, calcium, zinc and amounts of vegetables, fruits, and berries consumed in accordance with the national diet recommendations [19].
## 2.4. Physical Activity
During the visit, the leisure-time physical activity (LTPA) was assessed by a self-administered questionnaire. Children and their parents were asked about the frequency of participation in physical activity outside school or working hours and its average intensity and duration of habitual LTPA [24]. The questionnaire has been described in detail [25]. LTPA was calculated as a multiple of the resting metabolic rate (metabolic equivalent [MET] h/week) by multiplying the frequency, mean duration, and mean intensity of weekly LTPA [26]. LTPA was categorised into 3 different activity levels: low (<5 MET h/week, i.e., 10 min/d of moderate-intensity physical activity), moderate (≥5 to ≤30 MET h/week, i.e., physical activity more than 1 h weekly but less than 1 h daily) and active (>30 MET h/week, i.e., 1 h of moderate-intensity physical activity daily) [27].
## 2.5. Screen Time
Time spent looking at a screen was inquired with a question: On average, how many hours per day did the child spend viewing a laptop, computer, television, tablet or play console in a day in the preceding week including weekend? The answers were categorised into three groups in a data-driven manner (≤2 h/d, 2.5 to 4 h/d, >4 h/d).
## 2.6. Sleep Duration
The length of night-time sleep was inquired by the question: How many hours per night did the child sleep on weekdays and weekends during the previous week? Three separate categories were formed based on the mean length of sleep (<9 h/day, 9–10 h/day, >10 h/day).
## 2.7. Anthropometric Measurements and Background Information
Height and weight were measured as described earlier [19]. The body mass index standard deviation score (BMI SDS) was calculated according to the Finnish growth reference curves [28]. Children were categorised as normal weight (including underweight), overweight, or obese. Family background information was inquired with a questionnaire. This included mother’s and father’s ages, weights, and heights, to allow their BMI values to be calculated.
## 2.8. Ethics
The study was conducted according to the guidelines of the Declaration of Helsinki, and the protocol was approved by the Ethics committee for Human Sciences of the University of Turku in 2017 (statement $\frac{3}{2017}$). All parents and children provided written informed consent prior to participation.
## 2.9. Statistical Analyses
Background information and HRQoL scores were reported as medians (Q1, Q3) because of the skewness of the distribution. Chi-square or Fisher’s exact test for categorical variables were used to test if there were any differences between the background information of the different age groups. Spearman rank correlations were used to examine the correlations between children’s and parents’ reported HRQoL scores and the measured continuous variables. Mann–Whitney U test was used to evaluate the differences between genders and the related-samples Wilcoxon signed-rank test applied when comparing between the children’s and parents’ evaluation of the HRQoL scores.
The association between HRQoL scores and relevant background factors (town, gender, age as categorised, BMI, ES-CIQO, physical activity (MET, categorised), screen time (categorised) and sleep duration (categorised) were first examined using one-way analysis of variance/covariance. From these analyses, significant explanatory variables were taken into starting multivariable models (linear model), as well as interactions with the age category. The multivariable model was then simplified step-by-step by removing non-significant terms from the statistical model. If the effect included more than two categories, a pairwise comparison was created between categories and these pairwise comparisons were adjusted with Tukey’s method. Since the observed distributions of all the scores showed clear deviations from a normal distribution (most of children/adults had a high quality of life), a special transformation was required to satisfy the assumptions of the linear models. First, we created a ‘mirror’ distribution by subtraction of (101—score) in order to obtain a right-skewed distribution from which the square root was calculated to achieve a normal distribution. In addition, the Spearman correlation was calculated between the scores. In all analyses, a significance level of 0.05 (two-tailed) was used. The data analysis for this paper was generated using SAS software, Version 9.4 of the SAS System for Windows (SAS Institute Inc., Cary, NC, USA) and IBM SPSS Statistics version 28 for Windows (IBM Corp, Armonk, NY, USA).
## 3.1. Background Information of the Children and Their Parents
The data were obtained from 270 primary-school-aged children from each school class (1st $20.7\%$, 2nd $21.1\%$, 3rd $20\%$, 4th $13.3\%$, 5th $13.3\%$ and 6th $11.5\%$) with a median age of 9.7 years (range 6.8–13.4), who were $47\%$ female (Table 1). Most of the children were normal weight. The younger group (6–7 years) included $18.9\%$ of the children with the remaining $81.1\%$ of the children in older group (8–13 years). Diet quality measured with ES-CIDO was a median of 5.9 points (Q1: 4.0, Q3: 8.0); LTPA a median of 31.3 MET (19.5, 35.0); screen time a median of 1 h/day and sleep duration 9.5 h/night. Data were grouped based on the child’s age and the PedsQL-age specific measure (Table 1). Differences between younger and older children were detected in age, diet quality, LTPA, screen time, sleep duration, mother’s age, and father’s age.
## 3.2. Children’s Reports of HRQoL
Younger children reported lower HRQoL than the older children (Table 2). This was detected in all the subscales of PedsQL (all $$p \leq 0.001$$). The lowest scores in the younger children were evident in the psychosocial health and its subscales. Older children reported the lowest scores with respect to their emotional functioning. Furthermore, the scores differed between the sexes, i.e., the total score and the subscales for physical and psychosocial health were higher in girls.
## 3.3. Comparison of Child and Parent Proxy-Reports of HRQoL
Mothers were responsible for giving the parent proxy-report of their child’s HRQoL in $93.8\%$ of the cases, and the mother and father together in less than $7\%$ of the cases. Generally, the parents rated their child’s HRQoL better than the younger children themselves but similar as the older children (Table 2). The differences between younger children’s and parent proxy-reports were significant in the total score, and the subscales of physical health, psychosocial health, and school functioning. Here, negative means that the parent reported the subscale as better than the child. Emotional functioning was the only subscale rated better by the older children than by their parents. A better agreement between the child and their parents was detected in the group of older children. Correlations between the child’s and parent proxy-reports were modest in the younger children (Table 3). The strongest correlation was detected in the parental reported total score and the subscales of their child’s physical health, the assessments by both the parent and the child about their physical health and the evaluation by the parent and child on the school functioning subscale. The correlations in the older children’s were also rather weak but nonetheless statistically significant and were related to all subscales of PedsQL with a range of 0.19 to 0.49 (Table 4). The strongest associations were detected between the parent’s and the child’s total score, and the following subscales: parent’s total score and the child’s psychosocial health; parent’s total score and the child’s school functioning; parent’s physical health and the child’s total score; parent’s and the child’s physical health; parent’s psychosocial health and the child’s total score; parent’s and the child’s psychosocial health; and finally, parent’s and the child’s school functioning score.
## 3.4. Children’s Reports of HRQoL and Associated Lifestyle Measures
In the univariate models (Table 5), a better PedsQL total score was significantly associated with female sex, older age, a higher categorised BMI, high physical activity in comparison with low or moderate physical activity and less screen time. The values of ES-CIDQ and sleeping durations were not statistically significantly related to the total score or any of the subscales. Physical health score was significantly associated with older age, a higher BMI, and high physical activity in comparison with children with low or moderate physical activity.
In the multivariable models (Table 5), a higher PedsQL total score was linked to the female gender, older age, high physical activity in comparison with low or moderate physical activity and less screen time (≤2 h/d). The impact of physical activity in this sample was not linear.
The higher physical health score was explained by older age and high physical activity in comparison with low or moderate physical activity. Furthermore, the psychosocial health score was explained by female gender, older age, high physical activity, and less screen time (≤2 h/d).
## 3.5. Parental Reports of HRQoL and Associated Lifestyle Measures
In the univariate models mother´s higher BMI was linked to better parent -proxy- report of total PedsQl total score, physical score, and psychosocial health (Table 6). In the multivariable models better PedsQL total score was explained by a child´s female gender, older age, high physical activity, and less screen time (≤2 h/day). Physical health was associated with the female gender, older age, moderate or high physical activity, and less screen time (≤2 h/d). Psychosocial health was explained by female gender, older age, and high physical activity (Table 7). In children’s reports psychosocial health score was associated with female sex, older age, higher categorised BMI and less screen time (Table 7).
## 4. Discussion
The results revealed that a significantly better HRQoL was reported by 8–13-year-old children (older) in comparison with the 6–7-year-old children (younger). Parents assessed their child’s HRQoL better than the younger children themselves in all the subscales of PedsQLTM 4.0, but about the same as the older children. Female gender, the child’s older age, high physical activity level, and a low amount of screen hours (≤2 h/day) were significant predictors of a better HRQoL in the children’s report and high physical activity, and less screen time (≤2 h/day) in the parental proxy-reports. At odds with our working hypothesis, sleeping and diet were not associated with HRQoL.
The HRQoL total score was a median of 73.9 (mean 73 (SD 11.0)) in the younger children (6–7 years) and a median of 84.8 (mean 83.1 (SD 11.3)) in their older counterparts (8–13 years). In the Finnish validation study of PedsQLTM 4.0 conducted in 8–12-year-old children in the fourth-grade pupils, the mean of the total score was 81.54 (11.46) [21] and this remained almost the same, i.e., a mean of 80.96 (11.76) at the age of ten years [22]. Nonetheless, the follow-up revealed that HRQoL values increased to a mean of 85.1. ( 10.1) as children grew from age 10 to 12 years. These values are close to those measured in this study. The proposed average HRQoL score measured in healthy populations all around the world is a mean of 80.9 (SD 12.6); the European reference mean score was 80.3 (SD 8.3) with a range of 70.6 to 86.1 [10]. It has been shown that children below the age of seven and above the age of 12 have higher scores [10]. This contrasts with our study where older children had better HRQoL. Similar results were measured in a study which compared children of three ages, ages 6–8, 9–11 and 12–17, of which the oldest age group exhibited a decline in the HRQoL domains [16]. A decrease in HRQoL has been found as Chinese children grow older from 9 to 17 years of age [29]. The differences in the reports between younger and older children may reflect the fact that elementary school-aged children in the first and second grades are still not mature enough to be able to reliably report their HRQoL. However, the developer of this instrument has demonstrated that 5-year-old children can reliably and validly self-report their HRQoL with an age-appropriate instrument [5]. However, if younger children are not asked to fill in the questionnaires, this may potentially have affected their reports.
The subscales that were reported with the highest values were physical health both in younger and older children, and social functioning in older children. The results support previous findings both from Finland and elsewhere in which children reported the highest subscales of physical health [6,16,22] and social functioning [6,16,22]. The lowest scores in younger children were encountered with psychosocial health and its subscales. Older children reported their emotional functioning as the worst, as has been the case in the earlier studies [6,16,20].
A better HRQoL was seen in girls in comparison with the boys when we assessed total, physical, psychosocial, emotional, and school functioning, but no differences were found in social functioning. In one report, Finnish 10-year-old girls reported lower emotional functioning than the boys [22]. In comparison, at the age of 10 to 12 years, all HRQoL scores improved, with the boys reporting significantly better HRQoL [6,30]. There are differences between the Finnish studies conducted over 10 years ago by Laaksonen and our study, i.e., the children that they examined were 4th grade boys and girls, whereas in our study, children were elementary school-aged; nonetheless, the results are rather similar.
We compared and demonstrated that children’s and parental correlations of the HRQoL subscales were higher in the group of older children and this finding is supported by earlier publications [6,7]. Differences between children’s and their parents’ reports of HRQoL were statistically significant in the total, physical, and psychosocial health, and school functioning in the younger children, and between emotional and school functioning in the older children. Younger children´s parents reported higher HRQOL than the children themselves. Varni [23] detected a trend towards higher inter-correlations between the parent’s and child’s reports as the child became more mature, which was seen in our study. In the Finnish study, 10-year-old children reported higher HRQoL scores than parents in the following domains—physical, emotional, school and social—but lower in social and school functioning than the assessment of the parents [6]. HRQol increased in both the children’s and parents’ reports with age [6]. The child’s age and domains of the measure and the parents’ own HRQoL may affect the agreement between how the parent and their child assesses the HRQoL [7].
From all of the measured lifestyles, only high physical activity and less screen time were significantly related to HRQoL in both children’s and parent’s reports, and child’s age and gender in the children’s reports. Parallel results have been reported where HRQoL physical health and psychosocial health were positively associated with a sufficient sleep duration and moderate/vigorous activity and negatively correlated with screen time [16]. A systematic review found evidence that higher levels of physical activity were associated with higher HRQoL, whereas more sedentary behaviours were linked with lower HRQoL [17]. Sedentary behaviour was characterised as screen-based activities such as watching television, using smartphones, and playing computer games. Physical activity and screen time were self-reported in most of the studies and HRQoL was measured with seven different HRQoL instruments in children aged 3 to 18 years [17]. Very few studies on healthy children have included how the child assessed their HRQoL in association with lifestyle measures including diet, physical activity, screen time, sleeping duration and BMI [29,31]. Children from 12 countries were compared and HRQoL was assessed with the KIDSCREEN-10 measure of HRQoL. A cluster of healthy lifestyle behaviours in 9–11-year-old children was found, i.e., low screen time, a healthy eating pattern based on a food frequency questionnaire, moderate physical activity, and moderate sedentary behaviour [31]. Most often either physical activity and screen time or physical activity and diet quality have been measured [30,32].
Leisure-time physical activity was low, $6.9\%$, moderate, $35.4\%$, and high, $57.7\%$ in the children. In the models, differences were detected in comparisons between low activity versus high activity, and moderate versus high activity groups. It is difficult to compare across studies because of the different physical activity measures being applied. Some measurements have been performed with self-reported questionnaires, and others with accelerometers which give a very accurate picture of daily physical activity. Nonetheless, we believe that the children in our study were physically active. Wong [16] used the International Physical Activity Questionnaire (IPAQ) and grouped the physical activity of 6–17-year-old children in the following manner: low, $2.6\%$, moderate, $29.9\%$, and the majority, i.e., $67.4\%$, highly active.
We assessed screen time as viewing a laptop, computer, television, tablet or play console and the median time spent was one hour per day. A total of $67.4\%$ of the children spent ≤2 h/day on these activities, a value that is much less than in some reports for gaming and leisure activities where durations of 3.1 h per day have been described in children aged 6 rising up to 3.7 h in 11 year-olds [16]. Another study reported values of under 2 h per day in $96.8\%$ and over 2 h per day in $3.2\%$ of primary school-aged children [29]. Better HRQoL was associated with less than 1–2 h per day spent on screen-based activities in that study.
Usually, diet is not included in lifestyle measures. The ES-CIDQ-index was used to measure the quality of a diet but no association with HRQoL was found, although there was a difference between younger and older children, i.e., younger children had healthier food choices. One explanation may be the fact that the PedsQLTM 4.0 does not include questions that are connected to diet or eating behaviour; it is really intended to assess physical, emotional, social functioning and school functioning, and thus wellbeing in general. It is possible that an association may become evident in large samples. In a systematic review and meta-analysis of published studies including healthy children and adolescents, a high-diet quality, and healthy dietary behaviours were associated with increased HRQoL [18]. The studies included in that review had applied various measures of HRQoL. In a recent systematic review, adherence to a Mediterranean diet was explored. The researchers concluded that there was a positive correlation between how well the children and adolescents consumed a Mediterranean diet and their HRQoL values. However, only four out of eleven studies were assessed as having a low risk of bias, Mediterranean diet adherence was assessed with the KIDMED index or Krece Plus test, and the ages of children have varied from 6 to 18 years. Most studies have been conducted in South European countries [14,33]. In a recent study, children were grouped on the basis of HRQOL as measured with the EuroQol-5 Dimensions-5 levels (EQ-5D-5L) validated questionnaire and adherence to a Mediterranean diet was strongest in the $70.8\%$ of those children with the highest HRQoL [34]. In a Canadian study, children with healthier eating patterns had higher HRQoL, and unhealthy eating patterns were linked to lower HRQoL [15].
We hypothesised that there would be an association between HRQoL and diet because earlier studies have shown HRQoL to be worse in overweight and obese children. The child’s categorised BMI (i.e., as normal weight, overweight, obese) was an important predictor in the child´s reports with respect to the total PedsQL score, physical health, and psychosocial health in univariate models. A similar phenomenon was detected in mothers’ BMI as a continuous variable in relation to parental proxy-report of the PedsQL score, physical health, and psychosocial health. The sample included $15.9\%$ overweight and $6.2\%$ obese children. The HRQoL was lower in the obese children than in normal weight children. Nonetheless, in the multivariate models, this relationship disappeared. Research has shown that overweight and obese children with BMI values above normal have significantly lower total, physical, and psychosocial HRQoL [11,13] and their parents reported even more greater HRQoL reductions than the children themselves [11]. This association has not been observed in all studies [34].
There are many strengths in this study. Firstly, the objective was to examine children from each of the school grades (1 to 6) evenly; unfortunately, this was not fully realised as the number of children was lower in the older grades. One explanation may be that the age of 11 to 12 is the time of prepuberty, when children’s willingness to cooperate may be reduced. Secondly, the HRQoL reports were obtained from both parents and children. Thirdly, an extensive battery of lifestyle measures was included: diet, physical exercise, sleep duration, screen time, with the measurements made with validated instruments. Fourthly, we had national reference data for comparison from an earlier national HRQoL validation study in elementary school-aged children which unfortunately did not assess lifestyle measurements.
The limitations of this study were, firstly, that it was cross-sectional. Secondly, some selection bias may have occurred favouring families with healthier lifestyles when families were approached through the Finnish Population Information System, as well as through hobbies and schools, and the sample was quite small. Community samples have been shown to have better HRQoL values. The ability of the children in the early school years to complete HRQoL life instruments may be somewhat limited. However, the PedsQL instrument has been validated and widely used in studies with the ages of the respondents ranging from infants aged 5- to 18-year-old adolescents. Thirdly, the lifestyle measures were self-reported and screen time, sleeping duration, and LTPA were collected by self-reported questionnaires. Seasonal variation and puberty were not taken into account in the data collection.
Though there has been increasing interest in measuring HRQoL in recent years, systematic review and meta-analyses are needed to collect and evaluate the data. It has been challenging to draw definitive conclusions because of the different ways that HRQoL can be measured, some of which are not entirely comparable [35], and there have also been different measures of physical activity, diet, and screen time. Cultural differences exist and these introduce challenges when comparing studies from different countries. Repeated studies using the same measures are needed to confirm the results and measure the change with time in various cultural contexts and children in different age-groups separately. The comparability between studies would increase if one were to conduct two different measures of HRQoL in the same study. More longitudinal and repeated studies are needed to confirm the age- and gender-dependent differences observed here.
Means to promote healthy lifestyles should be particularly targeted to young children and boys. It would be important to find child-friendly ways to promote physical activity, as well as free-time activities which would compete with the enticements of the computer screen and the smart phone. In fact, these may represent means to reduce sedentary behaviours at the societal level. In Finnish studies, the HRQoL has been high, and subscales have been rated in the best categories despite the changes that have happened in broader society. HRQoL and subscale-related differences between sexes and lifestyle measures in healthy children reveal areas for health promotion. New and varied approaches are needed in societies to prevent the rising trend of overweight and obesity, and the development of inequalities. As populations are aging and the birth rates are declining, this is more important than ever before.
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|
---
title: Circulating Levels of Nesfatin-1 and Spexin in Children with Prader-Willi Syndrome
during Growth Hormone Treatment and Dietary Intervention
authors:
- Joanna Gajewska
- Katarzyna Szamotulska
- Witold Klemarczyk
- Magdalena Chełchowska
- Małgorzata Strucińska
- Jadwiga Ambroszkiewicz
journal: Nutrients
year: 2023
pmcid: PMC10005720
doi: 10.3390/nu15051240
license: CC BY 4.0
---
# Circulating Levels of Nesfatin-1 and Spexin in Children with Prader-Willi Syndrome during Growth Hormone Treatment and Dietary Intervention
## Abstract
Background: Despite observable improvement in the treatment outcomes of patients with Prader-Willi syndrome (PWS), adequate weight control is still a clinical problem. Therefore, the aim of this study was to analyze the profiles of neuroendocrine peptides regulating appetite—mainly nesfatin-1 and spexin—in children with PWS undergoing growth hormone treatment and reduced energy intake. Methods: Twenty-five non-obese children (aged 2–12 years) with PWS and 30 healthy children of the same age following an unrestricted age-appropriate diet were examined. Serum concentrations of nesfatin-1, spexin, leptin, leptin receptor, total adiponectin, high molecular weight adiponectin, proinsulin, insulin-like growth factor-I, and total and functional IGF-binding protein-3 concentrations were determined using immunoenzymatic methods. Results: The daily energy intake in children with PWS was lower by about $30\%$ ($p \leq 0.001$) compared with the controls. Daily protein intake was similar in both groups, but carbohydrate and fat intakes were significantly lower in the patient group than the controls ($p \leq 0.001$). Similar values for nesfatin-1 in the PWS subgroup with BMI Z-score < −0.5 and the control group, while higher values in the PWS subgroup with BMI Z-score ≥ −0.5 ($p \leq 0.001$) were found. Spexin concentrations were significantly lower in both subgroups with PWS than the controls ($p \leq 0.001$; $$p \leq 0.005$$). Significant differences in the lipid profile between the PWS subgroups and the controls were also observed. Nesfatin-1 and leptin were positively related with BMI ($$p \leq 0.018$$; $$p \leq 0.001$$, respectively) and BMI Z-score ($$p \leq 0.031$$; $$p \leq 0.027$$, respectively) in the whole group with PWS. Both neuropeptides also correlated positively in these patients ($$p \leq 0.042$$). Conclusions: Altered profiles of anorexigenic peptides—especially nesfatin-1 and spexin—in non-obese children with Prader-Willi syndrome during growth hormone treatment and reduced energy intake were found. These differences may play a role in the etiology of metabolic disorders in Prader-Willi syndrome despite the applied therapy.
## 1. Introduction
Prader-Willi syndrome (PWS) is a rare congenital neurodevelopmental disorder characterized by hyperphagia and many behavioral disturbances leading to morbid obesity [1]. In addition, hypotonia in early infancy, short stature, hypogonadism, and developmental delay are observed in patients with this syndrome. PWS is caused by the loss of genes on the paternally acquired chromosome 15q11-q13 and the prevalence of this syndrome is $\frac{1}{10}$,000–$\frac{1}{30}$,000 cases worldwide. The conservation of the PWS genetic interval on human chromosome 15q11-q13 and the gene cluster on mouse chromosome 7 facilitated the use of mice as animal models of PWS [2]. Some models mimicked the loss of all gene expression from the paternally inherited PWS genetic interval, while others considered smaller regions or single genes. These models revealed a number of mechanisms responsible for hypothalamic dysfunctions, resulting in hyperphagia, growth retardation, and metabolic disorders. Most of the typical features of PWS may be due to hypothalamic dysfunction both in orexigenic and anorexigenic neural populations [3]. Abnormal brain networks disrupt the physiological control of food intake and weight, which is observed in patients with PWS [4]. There are a number of nutritional phases in children and adolescents with PWS [5]. During phase 1, the infants are hypotonic and not obese. Phase 2 is associated with weight gain, but in sub-phase 2a the weight increases without a significant change in appetite or caloric intake (age quartiles 20–31 months). Next, in sub-phase 2b the weight gain is associated with a concomitant increased interest in food (quartiles 3–5.25 years). Phase 3 is characterized by hyperphagia accompanied by a lack of satiety (quartiles 5–13 years). Among the appetite-regulating neuroendocrine factors, there are orexigenic factors that stimulate food intake and/or increase body weight and anorexigenic factors that inhibit food intake and/or decrease in body weight [6]. Leptin—an anorexigenic peptide mainly secreted by white adipose tissue—reduces food intake and energy metabolism at the hypothalamus level via melanocortin receptors [7]. Soluble leptin receptor (sObR) is the main leptin-binding protein in the blood and can affect leptin bioavailability and functioning [8]. Plasma leptin in patients with PWS is positively correlated with body mass index (BMI) and body fat mass, but lower, unchanged as well as higher, leptin concentrations were found in these patients in comparison with healthy controls [9,10].
Adiponectin is another peptide secreted by adipocytes, but its effect on feeding behavior is controversial and closely related to nutritional status and food consumption [11]. Higher adiponectin concentrations were found in patients with PWS compared with obese and non-obese controls [12,13], whereas other authors observed no differences compared with obese subjects [14].
Nesfatin-1 and spexin were recently identified anorexigenic peptides involved in energy homeostasis that have not yet been studied in PWS patients. Nesfatin-1 is produced from nucleobindin-2 (NUCB2) precursor protein [15]. As a result of the post-translational modification of NUCB2, three cleavage products are formed: nesfatin-1 (amino acids 1–82), nesfatin-2 (amino acids 85–163), and nesfatin-3 (amino acids 166–396). Among these three forms, only nesfatin-1 shows biological activity. The physiological effect of nesfatin-1 relates to the reduction of food intake through central and peripheral actions. This peptide is expressed in the central nervous system (CNS) and peripheral tissues, such as adipose tissue, gonads, stomach, pancreas, and liver [16]. To date, the mechanism of nesfatin-1 action has not been clarified. This peptide may act in the inhibition of feeding via oxytocin, melancortin, and other systems to relay its anorexigenic properties [17]. Several studies showed that peripheral nesfatin-1 may be associated with BMI and body fat in obese children and adults, however, positive as well as negative correlations between BMI and serum nesfatin-1 levels were found in obese patients [18,19,20].
Spexin (SPX) is a 14 amino acid peptide encoded by the C12orf29 gene and located on chromosome 12 of the human genome [21]. This peptide is mainly produced in white adipose tissue, brain, heart, muscles, ovaries, testes, and gastrointestinal tract [22]. Spexin plays a role in glucose and lipid metabolism. Thus, it is speculated that this peptide may be one of the protective agents against obesity and metabolic syndrome (MetS) [23]. Some studies reported no differences in spexin concentrations between obese and normal-weight subjects, whereas other studies obtained lower values of spexin in obese subjects compared with non-obese controls [24,25]. It has been suggested that altered spexin expression may affect the cross talk between the brain and peripheral organs in obese patients [26].
Current therapeutic strategies in PWS consist of preventive methods for weight management and mainly include early therapy with growth hormone (GH), dietary recommendations, and behavioral interventions [1]. Despite observable improvement in the treatment outcomes of patients with PWS, adequate weight control is still a clinical problem as patients often develop obesity and/or metabolic disorders [27]. Therefore, the aim of this study was: (i) to analyze the differences in the profiles of circulating peptides regulating appetite—mainly nesfatin-1 and spexin—in children with PWS undergoing GH treatment and reduced energy intake with the profiles in healthy, non-obese children, and (ii) to evaluate the relationships between the biochemical parameters and anthropometric indices in children with PWS during GH treatment and dietary intervention.
## 2.1. Patients
We examined 25 Caucasian children with Prader-Willi syndrome aged 2–12 years, who were recruited between 2020 and 2022 from a group of consecutive patients seeking dietary counseling in the Department of Nutrition at the Institute of Mother and Child in Warsaw, Poland. The inclusion criteria for this study were: (a) genetically confirmed diagnosis of PWS; (b) GH treatment for at least one year, and being on GH at the time of inclusion (0.025 mg/kg/day); (c) being on an energy-restricted diet. The exclusion criteria were: (a) a body mass index (BMI) Z-score > 1; (b) chronic secondary illness, such as diabetes mellitus, liver, or kidney disease; (c) taking investigational drugs; (d) not signing the informed consent form. The average duration of treatment with growth hormone in the whole group of patients with PWS was 4.7 ± 2.8 years.
The control group consisted of 30 non-obese, healthy children (BMI Z-score < −1 + 1) within the same age range as the group with PWS, an adequate nutritional or dietary status according to the recommendations of Kułaga et al. [ 28] and Jarosz et al. [ 29]. The control group in the study were children: (a) without acute or chronic disorders; (b) not taking any medications that could affect their development and nutritional or dietary status; (c) IGF-1 levels within the normal range for age and sex. Written informed consent was obtained from the parents of all the examined children. The study was performed in accordance with the Helsinki Declaration for Human Research, and the study protocol was approved (protocol code: $\frac{8}{2020}$; date of approval: 6 April 2020) by the Ethics Committee of the Institute of Mother and Child in Warsaw, Poland.
## 2.2. Assessment of Dietary Intake
Based on lower energy requirements for children with PWS, the recommendations included limiting caloric intake by 20–$40\%$ along with a well-balanced macronutrient distribution [30]. Two weeks before the child was due to visit the Department of Nutrition, a food diary was completed by the parents at home. The parents had previously been trained by a nutritionist to provide estimates of diary intake. Next, nutritionists carried out interviews concerning nutritional behaviors, and checked the diary in the presence of the child and their parents. The nutritionist also asked for detailed information about the recorded foods and drinks, such as portion sizes and preparation methods. The portion sizes were corrected during the visit using a photo album of products and dishes presenting meal portion sizes [31]. The three-day methodology was used according to the methodological guide on nutrition research to assess intake in the children’s dietary habits [32]. The data of the three-day dietary records—two weekdays and one weekend day—were entered into the nutrition analysis software (Dieta 5®, National Food and Nutrition Institute, Warsaw, Poland) to assess the average daily energy intake and the percentage of energy intake from fat, protein and carbohydrates [33]. The data for each participant were compared with the recommendations for the appropriate age and sex. The age- and sex-specific percentage of estimated energy requirement (EER) for total energy intake and adequate intake (AI) for fiber were calculated [29]. The children in the present study did not receive supplements, except standard supplementation with vitamin D.
## 2.3. Anthropometric Measurements
Physical examinations—including body height and weight measurements—were performed in both of the studied groups. Body height was measured using a standing stadiometer and recorded with a precision of 1 mm. Body weight was assessed unclothed —to the nearest 0.1 kg—with a calibrated balance scale. Body mass index(BMI, body weight divided by height squared, kg/m2) of each individual was converted to BMI Z-score for the child’s age and sex using Polish reference tables [28]. Body composition was measured by dual-energy X-ray absorptiometry (DXA) using Lunar Prodigy (General Electric Healthcare, Madison, WI, USA) using pediatric software version 9.30.044. All children were measured with the same equipment using standard positioning techniques.
## 2.4. Biochemical Analyses
Venous blood samples were collected between 8:00 and 10:00 a.m. after an overnight fast. To obtain serum, the blood was centrifuged at 1000× g for 10 min at 4 °C. Serum specimens were stored at −70 °C prior to assay. Serum nesfatin-1, spexin, leptin, leptin receptor, total adiponectin, high molecular weight (HMW) adiponectin, proinsulin, insulin-like growth factor-I (IGF-I), total-IGF-binding protein-3 (t-IGFBP-3), and functional IGFBP-3 (f-IGFBP-3) concentrations were determined using immunoenzymatic methods. The concentrations of nesfatin-1 and spexin were determined by the Elisa kits (Elabscience, Houston, TX, USA) with anti-human NES-1 antibody and anti-human SPX antibody, respectively. The intra- and inter-assay CVs were $4.8\%$ and $5.2\%$ for nesfatin-1, and $5.1\%$ and $4.2\%$ for spexin, respectively.
Elisa kits from DRG Diagnostics (Marburg, Germany) were used to determine leptin and soluble leptin receptor (sOB-R) concentrations. The intra- and inter–assay CVs were less than $9.6\%$ and $9.1\%$ for leptin, and $7.2\%$ and $9.8\%$ for leptin receptor, respectively.
Serum levels of total adiponectin and HMW adiponectin were determined using an ELISA kit (ALPCO Diagnostics, Salem, NH, USA). The intra- and inter-assay variations were less than $5.7\%$ and $6.4\%$, respectively. The concentrations of proinsulin were measured using kit from TECO Medical (Sissach, Switzerland). The intra- and inter-assay variations were less than $2.2\%$ and $4.0\%$, respectively.
IGF-I and t-IGFBP-3 values were determined using ELISA kits from Mediagnost (Reutlingen, Germany). The intra- and inter-assay coefficients of variation were less than $6.7\%$ and $6.6\%$ for IGF-I, and $2.2\%$ and $7.4\%$ for t-IGFBP-3, respectively. We calculated the IGF-I/IGFBP-3 molar ratio—an estimation of free IGF-I concentration—as [IGF-I (ng/mL) × 0.130]/[IGFBP-3 (ng/mL) × 0.036]. The IGF-I/IGFBP-3 molar ratio has been used as a surrogate parameter associated with IGF-I bioactivity [25].
The f-IGFBP-3 concentration was determined using ligand-binding immunoassay (LIA) (Mediagnost, Reutlingen, Germany) with intra- and inter-assay variability of less than $5.6\%$ and $6.8\%$, respectively. This assay for f-IGFBP-3 exclusively detects IGFBP-3 capable of IGF binding. The f-IGFBP-3/t-IGFBP-3 molar ratio was calculated to serve as an index of IGFBP-3 fragmentation. A ratio close to 0 indicates almost complete fragmentation and loss of IGFBP-3 function, whereas a ratio close to 1 indicates the presence of mostly intact protein with unchanged biological function. The analysis of each parameter was performed in duplicate.
Total cholesterol, LDL and HDL-cholesterol, triglycerides and glucose levels were measured using standard methods (Roche Diagnostics, Basel, Switzerland).
## 2.5. Statistical Analyses
The results are presented as means ± standard deviation (SD) for symmetric distributed data or medians and interquartile range (25–75th percentiles) for skewed distributed variables. The Kolmogorov-Smirnov test was used to evaluate distribution for normality. Differences in anthropometric characteristics, biochemical parameters, and dietary intake of patients with Prader-Willi syndrome and healthy, non-obese children were assessed using the non-parametric Mann-Whitney U test.
We categorized children with PWS and the control group into subgroups according to BMI Z-score: PWS 1 ($$n = 13$$) and Controls 1 ($$n = 10$$) with BMI Z-score < −0.5; PWS 2 ($$n = 12$$) and Controls 2 ($$n = 20$$) with BMI Z-score ≥ −0.5. Differences in BMI, fat mass, biochemical parameters—including nesfatin-1, spexin, leptin, and adiponectin—and dietary intake were compared between the subgroups with PWS with the appropriate control subgroups.
Quantile regression was used to assess the age-adjusted relationships between the studied appetite-regulating peptides as well as the age-adjusted relationships between appetite-regulating peptides and selected anthropometric parameters in the entire group of patients with PWS. For trend analysis, the Jonckheere-Terpstra test was used. A p-value < 0.05 was considered to be statistically significant. Statistical analysis was performed using IBM SPSS v.25.0 software (SPSS Inc., Chicago, IL, USA) and Stata Statistical Software: Release 17 (StataCorp. 2021, College Station, TX, USA: StataCorp LLC).
## 3.1. Clinical Characteristics and Dietary Intake of Children with PWS
Significantly lower BMI and BMI Z-score values were observed in children with PWS than healthy children of the same age ($p \leq 0.05$), but a similar body fat mass was found in both groups (Table 1).
Children with PWS had higher serum nesfatin-1 levels ($$p \leq 0.019$$) by $40\%$ and lower serum spexin concentrations by half ($p \leq 0.001$) compared with the controls. Significant differences in lipid profile between these groups were also observed. Higher concentrations of total cholesterol by about $15\%$ ($$p \leq 0.001$$), LDL cholesterol by about $25\%$ ($p \leq 0.001$), and triglycerides by about $25\%$ ($$p \leq 0.005$$) were found in patients with PWS than in healthy children. Similar values of serum leptin, sOB-R, total adiponectin, HMW adiponectin, and glucose were found in both studied groups. However, slightly higher proinsulin concentrations ($$p \leq 0.069$$) were observed in children with PWS.
The IGF-I, t-IGFBP-3 concentrations, and the IGF-I/t-IGFBP-3 molar ratio in patients with PWS were higher by 2-fold ($p \leq 0.001$), $20\%$ ($$p \leq 0.016$$), and $50\%$ ($p \leq 0.001$) than in controls, respectively. The level of functional IGFBP-3 in patients was higher by about $30\%$ ($$p \leq 0.055$$) than in healthy children, but the p-value was borderline. The f-IGFBP-3/t-IGFBP-3 molar ratio was similar in both studied groups.
The daily energy intake and the percentage of EER in children with PWS were lower ($p \leq 0.001$), but the percentage of energy from proteins was significantly higher ($p \leq 0.001$) than in healthy children (Table 2).
The proportion of carbohydrates in daily energy intake was similar in both groups, but the proportion of fat was significantly lower in patients with PWS than controls ($$p \leq 0.018$$). Daily protein intake was similar in both groups, but daily carbohydrate and fat intakes were significantly lower in the group with PWS than in the controls ($$p \leq 0.001$$). Lower daily intake in patients with PWS was also found for cholesterol ($$p \leq 0.006$$) and saturated fatty acids ($$p \leq 0.001$$). Similar values for fiber intake were observed in both groups.
## 3.2. Biochemical Characteristics and Dietary Intake of the PWS Subgroups with Lower BMI Z-Score (BMI Z-Score < −0.5) and Higher BMI Z-Score (BMI Z-Score ≥ −0.5)
Table 3 shows the comparison of the PWS subgroups with lower BMI Z-score (PWS 1; BMI Z-score < −0.5) and higher BMI Z-score (PWS 2; BMI Z-score ≥ −0.5) with the control subgroups.
No differences were observed concerning age, height, BMI, and fat mass between the subgroups with PWS and the respective healthy children, except a lower BMI Z-score in PWS 2 than Controls 2 ($$p \leq 0.035$$).
The PWS 2 subgroup with higher BMI Z-score was characterized by higher BMI ($p \leq 0.001$) and fat mass percentage ($$p \leq 0.031$$), higher leptin/sOB-R and leptin/adiponectin ratios ($p \leq 0.001$) and lower sOB-R concentrations ($$p \leq 0.016$$) than the PWS 1 subgroup with lower BMI Z-score. We observed similar values of lipid parameters in PWS 1 and PWS 2 subgroups ($p \leq 0.05$), but when the PWS subgroups were compared with the corresponding controls, significantly higher concentrations of total cholesterol, LDL-cholesterol, and triglycerides were found in patients with PWS. The concentrations of sOB-R, total adiponectin, HMW adiponectin, proinsulin, and glucose were similar in patient and control subgroups ($p \leq 0.05$).
The daily energy intake was lower ($$p \leq 0.004$$) in PWS 1 compared with Controls 1, but the percentage of energy from protein was significantly higher ($$p \leq 0.030$$) than in healthy children. The proportion of carbohydrates in daily energy intake was similar in both subgroups, but the percentage of energy from fat was significantly lower than in Controls 1 ($$p \leq 0.049$$). Lower daily intake in PWS 1 was also found for fat ($$p \leq 0.001$$), cholesterol ($$p \leq 0.015$$), and saturated fatty acids ($$p \leq 0.004$$). Fiber intake was higher in PWS 1 than in Controls 1.
Daily intake did not differ statistically significantly, but the percentage of EER in children in PWS 2 was lower ($p \leq 0.001$) compared with Controls 2. The percentage of energy from protein was significantly higher ($p \leq 0.001$) than in healthy children, but the proportions of carbohydrates and fat in daily energy intake were similar in both groups. Daily protein and fat intake were similar in both groups, but daily carbohydrate intake was significantly lower in PWS 2 than in Controls 2 ($$p \leq 0.009$$). Lower daily intake in PWS 2 was also found for saturated fatty acids ($$p \leq 0.033$$). Similar values for fiber intake were observed in both subgroups.
## 3.3. Appetite-Regulating Peptides in the PWS Subgroups with Lower BMI Z-Score (BMI Z-Score < −0.5) and Higher BMI Z-Score (BMI Z-Score ≥ −0.5)
Analyzing neuropeptide concentrations in the PWS subgroups compared with the controls, we observed similar values for nesfatin-1 in PWS1 and Control 1 ($$p \leq 0.446$$), but higher in PWS 2 than in Control 2 ($p \leq 0.001$) (Figure 1A). Moreover, nesfatin-1 values were higher in PWS 2 than PWS 1 ($p \leq 0.001$).
Spexin concentrations were significantly lower in PWS subgroups and controls (PWS 1 vs. Controls 1, $p \leq 0.001$; PWS 2 vs. Controls 2, $$p \leq 0.005$$) (Figure 1B). In fact, spexin concentrations were higher in the PWS 2 subgroup than PWS 1, but the p-value was borderline ($$p \leq 0.054$$).
Leptin and adiponectin concentrations were similar in PWS compared with the Control subgroups (Figure 1C,D). However, when comparing both PWS subgroups, leptin concentrations were higher in PWS 2 than PWS 1 ($$p \leq 0.001$$).
## 3.4. Associations between Peptides Regulating Appetite and Anthropometric Parameters in Children with PWS
Age-adjusted associations between peptides regulating appetite and between these peptides and anthropometric parameters in the entire group of children with PWS are presented in Table 4.
Nesfatin-1 and leptin were associated positively with BMI ($$p \leq 0.018$$; $$p \leq 0.001$$, respectively), BMI Z-score ($$p \leq 0.031$$; $$p \leq 0.027$$, respectively), and leptin additionally with body fat mass percentage ($$p \leq 0.026$$). Moreover, both peptides were associated positively in patients with PWS ($$p \leq 0.042$$). We did not observe any associations between spexin and adiponectin with anthropometric and biochemical parameters in these patients. In addition, we did not observe any associations between peptides regulating appetite and other anthropometric and biochemical parameters in patients with PWS ($p \leq 0.05$).
## 3.5. Nesfatin-1 and Spexin Concentrations in Children with PWS Depending on the Nutritional Phase
In children with PWS, we found significant associations between both neuropeptides and the nutritional phases (ptrend = 0.004 for nesfatin-1; ptrend = 0.041 for spexin) (Table 5).
We observed the highest values of these peptides in the group with PWS aged 6–12 years (phase 3). In healthy children, we did not find any associations between the concentrations of nesfatin-1 and spexin and the nutritional phases.
## 4. Discussion
Obesity in Prader-Willi syndrome resulting from hyperphagia can be prevented by caloric intake restriction [34]. To maintain a healthy weight and avoid obesity, children with PWS required about $30\%$ less energy in the presented study. Moreover, these patients consumed less fat, less carbohydrates, a similar amount of protein, and a fairly high amount of fiber. This is in line with many studies showing that children with PWS require a 20–$40\%$ reduction in energy intake to maintain a healthy body weight [30]. In our study, all children with PWS were not obese and even had slightly lower BMI and BMI Z-score values and a similar amount of body fat mass compared with healthy children. Besides a low-energy diet, the BMI values of our patients could also be modulated by GH treatment. Irizarry et al. [ 35] found that GH treatment was associated with a lower BMI Z-score and higher IGF-I in patients with PWS. We also observed higher concentrations of IGF-I and t-IGFBP3, and slightly higher f-IGFBP3 concentrations in children with PWS than in the controls. The f-IGFBP-3/t-IGFBP-3 molar ratio obtained in our study was similar in both groups and reflected the same degree of this protein fragmentation in PWS and healthy children. The IGF-I/IGFBP-3 molar ratio as an indicator of free IGF-I, with a higher ratio in our patients due to excess IGF-I may reflect higher IGF-I bioactivity. According to some authors, young children with PWS treated with GH would need relatively high IGF bioactivity because they grow fast [36].
Scientific research on the treatment of patients with PWS attributes an important role to nutritional intervention to ensure sustainable growth while preventing obesity and malnutrition in these patients. The exact mechanism of obesity development in Prader-Willi syndrome is not fully understood. Abnormalities in the hypothalamic satiety center and its hormonal circuits can affect energy expenditure, food intake, body composition, and endocrine factors deficiencies in patients with PWS [3].
The present study is the first to evaluate anorexigenic neuropeptides—nesfatin-1 and spexin—levels in relation with anthropometric parameters and other peptides regulating appetite in children with PWS. In our study, we found higher concentrations of nesfatin-1 in children with PWS than in healthy children and positive associations between nesfatin-1 and BMI and BMI Z-score. In addition, higher values of this peptide were observed in patients with the higher BMI Z-score (BMI Z-score ≥ −0.5) than with the lower BMI Z-score (BMI Z-score < −0.5). It seems that the higher nesfatin-1 concentrations may appear in response to the body’s energy status and/or nutritional phase. Patients belonging to the age group characterized by hyperphagia (phase 3) had the highest values of nesfatin-1 in comparison with nutritional phases 2a and 2b. In addition, patients with a higher BMI Z-score were characterized not only by higher nesfatin-1 concentrations, but also by higher adipose tissue mass, higher leptin concentrations, and a higher leptin/sOB-R ratio than children with lower BMI-Z-scores. We also found positive associations between nesfatin-1 and leptin in children with PWS.
Conflicting results were reported regarding fasting serum nesfatin-1 concentrations and the relation between nesfatin-1 and BMI values in malnourished as well as obese children [18,19,20,37,38,39]. Lower nesfatin-1 levels in acute malnourished children were found by Kahraman et al. [ 39] and higher levels of this adipokine were observed by Kaba et al. [ 37] and Acar et al. [ 38]. The authors suggested that high nesfatin-1 may be one of the reasons for chronic malnutrition by causing poor appetite due to feelings of satiety. However, a positive [39], negative [38], and no correlations [37] between BMI SDS and nesfatin-1 were observed in children with malnutrition. It also cannot be ruled out that nesfatin-1 synthesis and serum nesfatin-1 concentration may differ in the studied populations due to nesfatin-1 gene polymorphism, as suggested by some authors [38,40].
Different results regarding serum nesfatin-1 concentrations were observed in obese than non-obese children by other authors [18,19,20,41]. Abaci et al. [ 19] and Kim et al. [ 20] reported lower serum nesfatin-1 levels in obese subjects compared with healthy controls and a negative correlation between nesfatin-1 and BMI in these patients. The authors speculated that low levels of this satiety peptide may be one of the reasons for inadequately controlled food intake. However, Anwar et al. [ 18] found higher serum nesfatin-1 in obese children (range 5–15 years) than in control subjects and positive correlations between nesfatin-1 and BMI-SDS. According to Tan et al. [ 41] the saturation of transporters for nesfatin-1 into cerebrospinal fluid (CSF) may explain the higher concentrations of this peptide in plasma.
Nesfatin-1 exerts anorexigenic functions, but according to Dore et al. [ 42] it works independently of the leptin pathway. However, nesfatin-1 signaling may be important in mediating leptin-induced anorexia [43]. Wernecke et al. [ 44] suggest common downstream signaling mechanisms for both peptides, because central co-administration of leptin and nesfatin-1 did not yield larger effects on energy expenditure than nesfatin-1 or leptin alone. The results obtained in our study also do not exclude the presence of functional relationships between nesfatin-1 and leptin in Prader-Willi syndrome.
Spexin, the next anorexigenic peptide, was significantly lower in our children with PWS compared with healthy children. We did not observe any relations between spexin and other biochemical and anthropometric parameters. Circulating spexin levels were also lower in obese children compared with normal-weight controls and did not correlate with other adipokines and cardiometabolic risk factors [45,46]. However, we found significant associations between spexin and the nutritional phases in our patients with PWS. Spexin signals neurons in the hypothalamus directly reducing food intake by enhancing leptin receptor and melanocortin 4 receptor expression, while decreasing neuropeptide Y type 5 receptor and ghrelin receptor expression [47]. Therefore, it is suggested that spexin expression is regulated by metabolic status or feeding conditions.
The role of spexin in obesity appears to be related to various factors, such as the regulation of appetite, eating behaviors, regulation of body weight, glucose homeostasis, and inhibiting long-chain fatty acid uptake into adipocytes [48]. This neuropeptide regulates fat tissue metabolism through the induction of lipolysis and inhibition of lipogenesis in human adipocytes and murine 3 T3-L1 cells [49]. Spexin has been found to efficiently reduce total lipids in the liver, suggesting its key role in lipid metabolism in mammals [50]. In our non-obese patients with PWS, lower concentrations of spexin were observed with altered lipid profile. Kavalahat et al. [ 26] reported that circulating levels of spexin were decreased with obesity and diabetes in adults and inversely correlated with lipid markers such as total cholesterol, LDL cholesterol and triglycerides, but positively correlated with HDL levels. Although we did not observe any significant associations between spexin and lipids, the influence of the deficit of this peptide on lipid metabolism in children with Prader-Willi syndrome may be considered.
Despite growth hormone therapy, metabolic disorders such as dyslipidemia and insulin resistance are described in some patients with PWS [27]. It is known that adiponectin is associated with increased insulin sensitivity and has anti-inflammatory properties [51]. In our patients—compared with controls similar—concentrations of adiponectin, HMW adiponectin, glucose, and slightly higher proinsulin concentrations were observed. Other authors found higher plasma concentrations of this adipokine in the PWS population, which was correlated with insulin sensitivity in these patients [52].
The present study had several limitations. First, we had a relatively small number of participants owing to the rarity of Prader-Willi syndrome. However, the study group was homogeneous in terms of therapy (GH therapy and low-energy diet) and anthropometrically and biochemically. The second limitation of this study was its cross-sectional nature and the absence of a prospective longitudinal analysis, which is needed to examine the relationship between circulating peptides regulating appetite and clinical outcomes in these subjects during therapy. Therefore, the assessment of the clinical utility of nesfatin-1 and spexin in patients with PWS requires long-term monitoring of the concentrations of these peptides during growth hormone therapy as well as dietary intervention. The next limitation was the lack of comparison between non-obese and obese children with PWS. In our Institute, early diagnosis and therapeutic intervention significantly reduce the number of obese patients with PWS. Further studies are needed to clarify the functional relationships between anorexigenic peptides in obese and non-obese children with PWS.
In conclusion, we observed an altered profile of circulating peptides regulating appetite—especially nesfatin-1 and spexin—in non-obese children with Prader-Willi syndrome during growth hormone treatment and reduced energy intake. In addition, nesfatin-1 concentrations are associated with leptin concentrations and BMI values in these patients. It seems that lower concentrations of spexin could affect the lipid profile in children with PWS. Changes in anorexigenic peptides may play a role in the etiology of metabolic disorders in Prader-Willi syndrome despite the applied therapy.
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|
---
title: 'Assessment of the Composition of Breastmilk Substitutes, Commercial Complementary
Foods, and Commercial Snack Products Commonly Fed to Infant and Young Children in
Lebanon: A Call to Action'
authors:
- Maha Hoteit
- Carla Ibrahim
- Joanna Nohra
- Yonna Sacre
- Lara Hanna-Wakim
- Ayoub Al-Jawaldeh
journal: Nutrients
year: 2023
pmcid: PMC10005724
doi: 10.3390/nu15051200
license: CC BY 4.0
---
# Assessment of the Composition of Breastmilk Substitutes, Commercial Complementary Foods, and Commercial Snack Products Commonly Fed to Infant and Young Children in Lebanon: A Call to Action
## Abstract
[1] Background: Nutrition for optimum growth and physical development is acquired by adequate infant feeding practices. [ 2] Methods: One hundred seventeen different brands of infant formulas ($$n = 41$$) and baby food products ($$n = 76$$) were selected from the Lebanese market and were analyzed for their nutritional content. [ 3] Results: Saturated fatty acid content was detected to be the highest in follow-up formulas (79.85 g/100 g) and milky cereals (75.38 g/100 g). Among all saturated fatty acids, palmitic acid (C16:0) accounted for the greatest proportion. Moreover, glucose and sucrose were the predominant added sugars in infant formulas, while sucrose was the main added sugar in baby food products. Our data showed that the majority of the products were non-compliant to the regulations and the manufacturers’ nutrition facts labels. Our results stated also that the contribution to the daily value for the saturated fatty acids, added sugars, and protein exceeded the daily recommended intake for most infant formulas and baby food products. [ 4] Conclusions: This requires careful evaluation from policymakers in order to improve the infant and young children feeding practices.
## 1. Introduction
Nutrition for optimum growth and physical development is acquired by adequate infant feeding practices. It has been proven that a child’s first 1000 days are vital to their development [1]. Throughout this time, children are growing more rapidly than any other point in their lives [1]. According to UNICEF estimates, 149 million children under the age of five have stunted growth and development as a result of a chronic shortage of nutrient-rich food in their diets, and 45 million children under the age of five experience wasted growth [2]. Therefore, an inadequate nutritional status can lead to serious health problems in infants and young children [3].
Breastmilk is the golden standard for a child’s health and survival [4]. It is a complete food rich in nutrients such as carbohydrates, protein, fat, vitamins, minerals, digestive enzymes, and hormones [5]. Moreover, breastmilk offers a variety of bioactive substances, such as oligosaccharides, that support the growth of a robust immune system and a healthy microbiota [6]. However, each woman’s breastmilk is distinct and depends on a variety of elements, such as her diet, general health, and the infant’s needs [7]. That being said, manufacturers aim at producing infant formulas that closely as possible resemble human breastmilk’s nutritional profile [8], so that most babies who receive alternative forms of feeding, when breastfeeding may not be appropriate or feasible, to meet all or part of their nutritional needs [5]. The International Code of Marketing of Breastmilk Substitutes (the Code) was published by the World Health Assembly in 1981 and written in response to the marketing activities of the infant feeding industry which were promoting formula feeding over breastfeeding, and in turn leading to dramatic increases in maternal and infant morbidity and mortality [9]. Despite all the regulations that have been put in place, irresponsible marketing of breastmilk substitutes hinders global efforts to increase the rate and duration of breastfeeding [4]. Moreover, poor implementation of the labels’ nutritional facts, packaging design features, and health claims are of utmost concern [7]. Many of the standards were substantially affected by infant formula companies, who put profits before the health of children [10].
According to the WHO guidelines, infants should be exclusively breastfed for the first six months of their lives, and thereafter consume nutrient-adequate and appropriate complementary foods with continued breastfeeding to two years of age or beyond [11]. Complementary feeding is a crucial phase and depends on many factors; including food availability and access, local culture, and pediatrician’s guidance; therefore, any failure can result in a long-term consequence that can last into adulthood [12,13,14]. In addition, the baby and toddler food market has grown over the years and its applications have expanded in tandem with the rise in working mothers [15]. Given that dietary habits can form as early as 2–3 years of age and last a lifetime, the wide range of products that are readily available can perplex family decisions [15].
Hence, our study is the first national study that evaluated the composition of infant formulas and baby food products, thus we intended [1] to assess the energy, fat, carbohydrate, sugar, protein, ash, moisture, and chloride contents of infant formulas and baby food products available in the Lebanese market, [2] to determine the level of compliance of infant formulas and baby food products with Codex, EFSA, and Libnor standards, [3] to tackle the difference between the infant foods actual nutrient content and their label claims, and [4] to evaluate the adequacy of the nutritional composition of these products with the daily recommended value in infants.
## 2.1. Data Collection
A total of 117 different brands of infant formulas ($$n = 41$$): 13 starting formulas (0–6 months), 3 follow-up formulas (6–12 months), 7 growing-up formulas (1–3 years), and 18 extra-care formulas (0–12 years), and commercial baby food products ($$n = 76$$): 16 cereals (5 milky cereals and 11 cereal meals), 21 cornflakes, 7 biscuits, and 32 pureed foods (12 fruit puree, 6 vegetable and legume puree, 12 meat or fish puree with vegetables, and 2 milk-based puree) were selected, based on their availability and high purchasing levels from pharmacies and supermarkets in Lebanon. Infant and toddler food products were classified according to their primary ingredient showing on the jars of the pureed foods and the carton back of the cereals, biscuits, and cornflakes. Additionally, all complementary foods indicated the corresponding age of use, which was 6 months and above. Information on the containers of each formula and baby food products was carefully examined and translated in an excel spreadsheet; all data were collected from the nutrition information panel (NIP) on the back of the package and information regarding the infant formula and baby food products was adjusted to meet the same measuring unit of the analyzed products. Prior to the analysis, all samples were coded and retained in an appropriate humidity and temperature conditions.
## 2.2.1. Fat Content Analysis
Fat was extracted using the Rose Gotlieb method. In order to disassemble the bonds that hold the lipid and non-lipid components together, the sample was treated with hydrochloric acid and ethyl alcohol. Organic solvents (di-ethyl ether and Hexane) were then added to extract the fat. The solvent was evaporated, and the residue was weighted.
## 2.2.2. Fatty Acid Profile Analysis
A total of 0.5 g of extracted fat was boiled for 5 min with 5 mL of 0.5 N methanolic KOH to separate the fatty acids from the glycerol. Esterification of fatty acids was performed by refluxing by boiling for 15 min with 15 mL of an esterification reagent. The fatty acid methyl esters were then extracted in a separatory funnel with 25 mL of diethyl ether. The organic layer was washed twice with 25 mL distilled water and diluted with diethyl ether after the aqueous layer was discarded. The fatty acids were identified by comparing them to a chromatogram of commercially available fatty acid methyl esters, and their area percentages were calculated. Fatty acid results were expressed as %wt/wt of fatty acids.
## 2.2.3. Protein Content Analysis
According to AOAC, the formal titration method was used to determine the protein concentration. The protein content of infant formulas and baby foods was obtained by multiplying the nitrogen content 6.38 dairy product factor (N × 6.38).
## 2.2.4. Carbohydrate Content Analysis
The total of the protein, fat, moisture, and ash values was deducted from 100 to calculate the total carbohydrate content of infant formulas and baby foods.
## 2.2.5. Total Sugars
The Munson and Walker principle was used to calculate total sugars. Fehling’s solution was boiled with a solution containing reducing sugars, and converted to insoluble dark red cuprous oxide (Cu2O). The precipitate obtained was filtered using a special perforated crucible and then weighed. Reducing or total sugars were then calculated from tables depending on the weight of cuprous oxide precipitate.
## 2.2.6. Total Energy Analysis
To calculate the total energy, Atwater coefficients for protein, carbohydrate, and fat were used. For each gram of protein, carbohydrate, and fat, the standard values of 4, 4, and 9 calories per gram were applied, respectively. The following equation was used to determine calories: Caloric content per 100 g or mL = (4 × protein) + (4 × carb) + (9 × fat)
## 2.2.7. Ash Content Analysis
The ash content is determined by calculating the weight loss caused by drying the sample in a high temperature muffle furnace (500–600 °C), which causes water and other volatile materials to vaporize and organic substances to be converted to CO2, H2O, and N2 in the presence of oxygen in the air.
## 2.2.8. Moisture Content Analysis
A 3 g sample was dried in a convection drying oven at 105 ± 3 °C for 3 h. The sample was then covered, placed in a desiccator to cool, and weighed when it reached room temperature. The procedure was repeated until the weight was constant.
## 2.2.9. Chloride Analysis
Titration was used to determine the concentration of chloride. Chloride was extracted from the sample (except for water) by ashing at 550 °C. After dissolving the ash in distilled water, the chlorides were titrated with silver nitrate, resulting in a silver chloride precipitate. The titration was finished when all of the chloride ions precipitated.
## 2.3. Statistical Analysis
All data were analyzed using IBM SPSS Statistics for Windows, Version 22.0. IBM Corp, Armonk, NY, USA. The data obtained were presented as the mean and standard deviations (SD). The paired sample t test was used to determine the compliance of the measured values of the infant formulas and baby food products with the labeling at $p \leq 0.05.$ The percentage of fatty acids of the labeled products was converted to % of total fatty acids and compared to the measured values.
In order to evaluate our results with the regulations, the total grams or milligrams per 100 kcal was calculated for infant formulas and baby food products by dividing the measured value (g or mg/100 g) by the total calories (per 100 g) and then multiplying the result by 100.
As for the percentage of daily value (DV), it was calculated using the following equation:%DV: Measured value*daily value×100 The US Food and Drug Administration (FDA) defines the daily value (DV) as “reference values for reporting nutrients on the nutrition labels”. Furthermore, %DV helps the consumer determine whether the serving of food and its nutrient content is high (>$20\%$), good (10–$19\%$), or low ($10\%$) [16].
* The measured value was converted from g/100 g to g/100 mL and then used to calculate the percentage of daily value:Each 30 mL of milk needs 1 scoop of powdered milk, equal to 4 g. This information was retrieved from the recommendation mentioned on the back of the package. Step 1: 100 mL of milk = 100 mL × 4g30 mL = 13.3 gStep 2: Measured value in g/100 mL = 13.3 g × measured value g100 g The real intake per day according to the daily intake was calculated based on the serving size indicated on the label: starting formulas: 751 mL/day, follow-up formulas: 675 mL/day, growing-up formulas: 540 mL/day, extra-care formulas: 770 mL/day, cereals: 44 g/day, cornflakes: 30 g/day, biscuits: 16 g/day, puree food: 361 g/day: Real intake per day=%DV × serving size from the label100 reference intake* * Reference intake is defined as 100 mL for infant formulas and 100 g for baby food products.
## 3. Results
A total of 41 infant formulas and 76 baby food products available in the Lebanese market were identified between April and May 2021. Tables S1 and S2 provide a summary of the nutrient content of the tested products.
## 3.1.1. Total Energy
The energy content in infant formulas ranged from 432 to 508 kcal/100 g. The highest content of energy was reported in the starting formulas with a mean ± SD of 489.5 ± 14.4 kcal/100 g. Yet, growing-up formulas had the lowest calorific value of 453.7 ± 13.86 kcal/100 g. As for baby food products, the content of total energy ranged between 31 and 472 kcal/100 g. Among biscuits, cereals, and cornflakes, the highest mean value was detected in biscuits (437.28 ± 26.1 kcal/100 g). As for the pureed foods, milk-based puree had the maximum calorific content of 92.5 ± 4.94 kcal/100 g.
## 3.1.2. Total Fat
The total fat content in infant formulas ranged from 6.4 to 25.2 g/100 g. Starting formulas had the highest fat content of 21.1 ± 2.8 g/100 g. The lowest content was seen among growing-up formulas with a mean value of 15.3 ± 2.9 g/100 g (Figure 1a). Moreover, the amount of total fat in baby food products had a minimum level of 0.1 g/100 g and a maximum level of 17.3 g/100 g. Among biscuits, cereals, and cornflakes, biscuits are reported to have the highest mean value of 11.58 ± 4.74 g/100 g. As for the pureed foods, milk-based puree had the highest fat content (2.6 ± 1.13 g/100 g) (Figure 1b).
## Fatty Acids
The highest content of total saturated fatty acids was reported in the follow-up formulas (79.85 g/100 g). Among all fatty acids, palmitic acid (C16:0) accounted for the greatest proportion with a range of 11.4 and 68.2 g/100 g; growing-up formulas contain the highest amount with a mean ± SD of 50.42 ± 9.30 g/100 g (Figure 2a). As for baby food products, the saturated fatty acid content had a minimum and maximum level of 8.78 g/100 g in fruit puree and 75.38 g/100 g in milky cereal, respectively. Palmitic acid (C16:0) contributed as well for the highest amount of total fatty acid in infant foods, except for fruit puree and vegetable and legume puree (Figure 2b).
Monounsaturated fatty acids’ content was between 17.4 g/100 g in the follow-up formulas and 26.28 g/100 g in the starting formulas. Oleic acid (C18:1) had the highest percentage among total fatty acids, where extra-care formulas contained the maximal amount of 16.4 ± 10.27 g/100 g. Baby food products reported a monounsaturated fatty acids’ amount ranging from 5.19 g/100 g in the fruit puree and 52.5 g/100 g in the meat or fish puree with vegetables. Oleic acid (C18:1) accounted as well for the greatest amount of total fatty acid in infant foods with the highest level detected in meat or fish puree with vegetables (49.69 ± 7.18 g/100 g).
The content of polyunsaturated fatty acids in infant formulas was between 2.16 g/100 g in the follow-up formulas and 2.67 g/100 g in the growing-up formulas. Among all fatty acids, linolenic acid (C18:3) accounted for the greatest proportion with a range from 0.1 to 2 g/100 g; growing-up formulas contained the highest amount with a mean ± SD of 1.31 ± 0.56 g/100 g. Polyunsaturated fatty acids in baby food products were found within a range of 1.53–22.5 g/100 g. The percentage of linoleic acid (C18:2) was detected to be high among the total fatty acids; meat or fish puree with vegetables had the highest level (17.05 ± 9.1 g/100 g).
Trans fatty acids’ content in infant formulas had a minimum level of 0.25 g/100 g in the starting formulas and a maximum level of 0.59 g/100 g in the growing-up formulas. Further, among all baby food products, milk-based puree accounted for the highest level of trans fatty acids (1.65 g/100 g).
## 3.1.3. Total Carbohydrates
The total carbohydrate content in infant formulas was 47.8–75.4 g/100 g. The highest content was detected in growing-up formulas with a mean ± SD of 64.88 ± 8.92 g/100 g, while follow-up formulas accounted for the lowest amount (61.8 ± 2.9 g/100 g) (Figure 1a). As for baby food products, the content of total carbohydrates was between 6.3 and 90.2 g/100 g. Among biscuits, cereals, and cornflakes, the highest mean value was detected in cornflakes (83.78 ± 4.12 g/100 g). As for the pureed foods, fruit puree had the maximum carbohydrate content (15.16 ± 3.59 g/100 g) (Figure 1b).
## Added Sugars
The total added sugar content in infant formulas was detected to be between 0.37 g/100 g in follow-up formulas and 4.17 g/100 g in growing-up formulas. Among total added sugars, glucose was the predominant added sugar in infant formulas, where extra-care formulas accounted for the highest concentration (2.53 ± 2.62 g/100 g), followed by fructose that was the highest in the growing-up formulas (1.48 ± 1.27 g/100 g) (Figure 3a). Additionally, the amount of total sugar among biscuits, cereals, and cornflakes, and milky cereals had a maximum level of 34.58 ± 0.48 g/100 g. As for the pureed food, fruit puree had the maximum total sugar content (11.25 ± 4.05 g/100 g). From total sugars, sucrose was most predominant in infant foods, of which cornflakes contributed to the highest proportion of 21.15 ± 8.74 g/100 g (Figure 3b).
## 3.1.4. Protein
Protein content in infant formulas was 5.3–63.7 g/100 g. The highest content was detected in follow-up formulas with a mean ± SD of 16.8 ± 3.16 g/100 g, while staring formulas accounted for the lowest amount (11.8 ± 1.57 g/100 g) (Figure 3a). As for baby food products, the protein content was reported to be between 0.3 and 17.6 g/100 g. Among biscuits, cereals, and cornflakes, the highest mean value was detected in milky cereals (14.96 ± 1.54 g/100 g). As for the pureed food, milk-based puree accounted for the highest protein content (3.25 ± 0.07 g/100 g) (Figure 3b).
## 3.2. Compliance of the Measured Values of Infant Formulas and Baby Food Products with Codex, EFSA, and Libnor Regulations
Table 1 compares some of the measured products and their conversions to the standards established by the Codex Alimentarius, EFSA, and Libnor.
The follow-up formulas (3.51 g/100 kcal) and growing-up formulas (3.37 g/100 kcal) had total fat levels below the recommended limits of EFSA (4.4–6 g/100 kcal). Additionally, all baby food products’ fat content was lower than the regulations set by Codex Alimentarius (at least $20\%$ of total energy from fat) and Libnor (10–25 g/100 g), with the exception of meat or fish puree with vegetables and milk-based puree that was consistent with the Codex regulations only. The linoleic and alpha linolenic acid content in the infant formulas was below the recommendations except for the alpha-linolenic content in starting formulas (53.16 mg/100 kcal). Further, all baby food products had linoleic acid levels that were below the reference range, except for the meat or fish puree with vegetables (492.26 mg/100 kcal). As for the carbohydrate content, it exceeded EFSA’s regulations (14 g/100 kcal) in the growing-up formulas (14.3 g/100 kcal). Furthermore, the protein content (3.65 g/100 kcal) of the growing-up formulas (3.07 g/100 Kcal) and extra-care formulas (2.69 g/100 kcal) exceeded EFSA’s regulations (1.8–2.5 g/100 Kcal). Moreover, the protein content of the baby food products was below the regulations except for cereal meal.
A more detailed description of the above findings can be found in the Supplementary Material Table S3.
## 3.3. Contributions to Daily Values
The contributions to daily values are shown in Table 2. The total fat content of all infant formulas, cereal meals, cornflakes, and pureed foods was low in which their contribution to the DV was below $10\%$. Moreover, the saturated fatty acids in the growing-up formulas contributed to $85\%$ of the total daily intake. Furthermore, the trans fatty acid %DV was low in the growing-up formulas ($8.2\%$) and all the baby food products. Likewise, the carbohydrate content was low (<$10\%$) in all the infant formulas and vegetable and legume puree, good (10–$19\%$) in fruit puree, meat or fish puree with vegetables and milk-based puree, and high (>$20\%$) in biscuits, cornflakes, cereal meals, and milky cereals. Hence, the contribution of added sugar to the daily intake was reported to be $155.5\%$, $162.4\%$, and $104.7\%$ in growing-up formulas, fruit puree, and milk-based puree, respectively. Additionally, the %DV of protein content was higher than $10\%$ in all the infant formulas and greater than $20\%$ in all the baby food products except for fruit puree ($11.8\%$) and vegetable and legume puree ($13.9\%$). Further, the contribution of protein to the daily intake was seen to be $131\%$, $139.05\%$, $147.07\%$, $101.8\%$, and $106.5\%$ amongst starting formulas, follow-up formulas, extra-care formulas, meat or fish puree with vegetables, and milk-based puree, respectively.
## 3.4. Nutrient Content and Labeling Discrepancies in Infant Formulas and Baby Food Products
When compared to the content of each product’s nutrition facts label, it was discovered that the majority of the products had inconsistencies in reflecting the real nutrient content and some of them had undeclared values. Most products showed a deviation from $0.5\%$ to $46\%$ between the label and the laboratory values (Table 3).
Referring to the energy content, a non-conformity was seen among the starting formulas, extra-care formulas, cereal meal, fruit puree, and meat or fish puree with vegetables with a deviation of $12.6\%$, $17.9\%$, $7.3\%$, $8.23\%$, and $5.52\%$, respectively. This difference was statistically significant. Moreover, the content of total fat was significantly different in all infant formulas, cornflakes, and biscuits with a deviation ranging from $0.69\%$ to $6.04\%$. The labeled and tested content of saturated, mono-, and polyunsaturated fatty acids showed discrepancies between $13.45\%$ and $39.6\%$ in all infant formulas. As for the content of saturated fatty acids in all baby food products, a non-conformity was detected, except for fruit puree, vegetable and legume puree, and milk-based puree. Furthermore, the measured and labeled values of all infant formulas, biscuits, fruit puree, vegetable and legume puree, meat or fish puree with vegetables were found to differ significantly in terms of total carbohydrates; this difference ranged from $1.5\%$ to $11.98\%$. Further, a statistically significant non-conformity was detected between the measured and labeled lactose content in all breast milk substitutes except for starting formulas (range: 6.82–$8.94\%$). More notably, the fructose and sucrose content were not labeled in all infant formulas. A deviation in the protein content ranging from $0.25\%$ to $0.95\%$ was statistically significant among the starting formulas, cereal meal, cornflakes, fruit puree, and vegetable and legume puree.
## 4. Discussion
Our paper is the first to provide an overview of the energy and nutrient composition of 117 commercially available infant formulas and baby food products in the Lebanese market. Based on our findings, starting formulas and biscuits were found to have the highest content of energy (489.5 ± 14.4 kcal/100 g, 437.28 ± 26.1 kcal/100 g, respectively) and total fat (21.1 ± 2.8 g/100 g, 11.58 ± 4.74 g/100 g, respectively). Moreover, saturated fatty acid content was detected to be the highest in follow-up formulas (79.85 g/100 g) and milky cereals (75.38 g/100 g). Among all saturated fatty acids, palmitic acid (C16:0) accounted for the greatest proportion. Further, the content of trans fatty acids accounted for a maximum level of 0.59 g/100 g in growing-up formulas and 1.65 g/100 g in milk-based puree. Regarding the carbohydrate content, growing-up formulas and cornflakes reported the highest amount (64.88 ± 8.92 g/100 g, 83.78 ± 4.12 g/100 g, respectively). Additionally, growing-up formulas and milky cereals had the highest total added sugar content (4.17 g/100 g, 34.58 ± 0.48 g/100 g, respectively). Moreover, glucose and sucrose were the predominant added sugars in infant formulas, while sucrose was the main added sugar in baby food products.
Our study also found that the fat composition of the majority of infant formulas and baby food products was below the regulations, whereas the protein content was lower in most baby food products and higher than the regulations in most infant formulas. Additionally, our data showed that the majority of the products had discrepancies in reflecting the real nutrient content when compared to the nutrition facts label.
Our results stated also that the contribution to the daily value for the saturated fatty acids, added sugars, and protein exceeded the daily recommended intake for most infant formulas and baby food products.
## 4.1. Comparison of the Composition of Infant Formulas and Baby Food Products with Regional Data
In our study, the minimum and maximum levels of calories detected in infant formulas ranged from 432 to 508 kcal/100 g, higher than the range reported in Pakistan (428–473 kcal/100 g) [20]. Moreover, our findings showed that starting formulas had the highest content of energy (489.5 ± 14.4 kcal/100 g) and growing-up formulas had the lowest (453.7 ± 13.86 kcal/100 g), similar to that reported in a Saudi study (481.68 and 467.06 kcal/100 g, respectively) [21].
The measured fat content detected in infant formulas in our study ranged from 6.4 to 25.2 g/100 g, lower than that detected in Egypt (18.7–26.7 g/100 g) [22], Pakistan in 1985 (18.2–27 g/100 g) [20], and Pakistan in 2021 (9.82–26.63 g/100 g) [23], and higher than the fat content analyzed in Kuwait (0.23–5.2 g/100 g) [24]. Further, our findings were in line with a Saudi study that revealed a higher fat content in starting formulas (23.27 g/100 g) [21]. As for baby food products, our fat content detected in fruit puree (0.1–2.7 g/100 g) and vegetable and legume puree (0.2–1.6 g/100 g) was the lowest; this was in line with an Egyptian study where the fruit, vegetable, and legume purees were 0.15–2.18 g/100 g [25]. Among all fatty acids in infant formulas, palmitic acid (C16:0) was the dominant saturated fatty acid with a range from 11.4 to 68.2 g/100 g, and oleic acid accounted for the highest proportion among monounsaturated fatty acids (5–37.1 g/100 g); our findings were in line with Sudan [26] and Kuwait [27].
In infant formulas, our results showed a total carbohydrate content with an average of 63.18 g/100 g, higher than that of Egypt in 2014 (53.04 ± 2.31 g/100 g) [28], Saudi Arabia (55 g/100 g) [21], and Pakistan (52 g/100 g) [20], and lower than that of Egypt in 2016 (64.92 g/100 g) [22]. Moreover, our carbohydrate content in milky cereals (70.98 ± 3.06 g/100 g) was lower than that declared in Pakistan (74.6 g/100 g) [20].
Protein content in infant formulas was detected with a mean of 13.85 g/100 g, close to the composition detected in a Kuwaiti Study [2016] (13.48 g/100 g) [24], higher than that analyzed in Egypt [2014] (11.07 g/100 g) [28], Egypt [2016] (8.88 g/100 g) [22], and Pakistan [2021] (12.61 g/100 g) [23], and lower than that assessed in Saudi Arabia (15.17 g/100 g) [21] and Pakistan [1985] (19.65 g/100 g) [20]. As for baby food products, the highest mean value was detected in milky cereals (14.96 ± 1.54 g/100 g); this finding was higher than the protein composition detected in Pakistan (12.5 g/100 g) [20]. Results are presented in Table S4.
## 4.2. Comparison of the Composition of Infant Formulas and Baby Food Products with International Data
The findings of our study revealed a level of calories of 437.28 kcal/100 g in biscuits, lower than that reported in Turkey (468.3 kcal/100 g) [29]. In a study conducted in the United Kingdom, the level of energy was lower than that detected in our study for meat or fish puree with vegetables (71.1 kcal/100 g) [30]. Furthermore, our results were very close to a study conducted in Turkey (386 kcal/100 g) for cereal meal (399.7 kcal/100 g) [29].
The mean fat content detected in infant formulas in our study was 18 g/100 g, lower than that detected in Italy ($\frac{26.2}{100}$ g) [31]. As for baby food products, the fat content detected in biscuits (11.58 g/100 g) and cereal meal (3.11 g/100 g) was lower than that detected in Turkey (19.7 g/100 g; 4.3 g/100 g, respectively) [29]. Our findings were in line with a British study that revealed a similar fat content in meat-based puree (2.1 g/100 g) [30], whereas a higher fat content (2.5 g/100 g) was reported for vegetable and legume puree compared to our results (0.85 g/100 g) [30]. Further, the saturated fatty acid content in our study in infant formulas (starting formula: 74.59 g/100 g; extra-care formula: 77.72 g/100 g) was much higher than that detected in Brazil (starting formula: 42.3 g/100 g; extra-care formulas: 41.9 g/100 g, respectively) [32], Spain (37.31 g/100 g and 35.9 g/100 g) [33,34], and the USA (41.05 g/100 g) [35]. Additionally, in the current study, palmitic acid content in infant formulas was much higher than that reported in Brazil (starting formula: 19.18 g/100 g; extra-care formula: 17 g/100 g) [32], Cote d’Ivoire (24.74 g/100 g) [36], Spain (23.09 g/100 g) [33], and the USA (16.2 g/100 g) [35]. The measured trans fatty acids in infant formulas (0.38 g/100 g) were higher than that reported in Spain (0.03 g/100 g and 0.33 g/100 g) [33,34], and lower than that found in the USA (1.3 g/100 g) [35].
In infant formulas, our results showed a total carbohydrate content with an average of 63.18 g/100 g, higher than that of Italy (56.3 g/100 g). Moreover, our carbohydrate content in baby food products was higher than that of Turkey (biscuits: 76.28 g/100 g vs. 67.3 g/100 g) [29], the United Kingdom (meat or fish puree with vegetables: 9.8 g/100 g vs. 7.4 g/100 g; vegetable and legume puree: 8.3 g/100 g vs. 7.4 g/100 g) [30]. However, for cereal meal, our results were in line with a Turkish study (76 g/100 g vs. 76.2 g/100 g) [29]. As for added sugar, their content in biscuits (21.75 g/100 g) was higher than that detected in Canada (19 g/100 g) [37] and lower than that detected in Turkey (31.4 g/100 g) [29], whereas their content in cereal meal (24.84 g/100 g) was higher than both studies conducted in Spain [38] and Turkey [29].
Protein content in infant formulas was detected with a mean of 13.85 g/100 g higher than that analyzed in Italy (10.9 g/100 g) [31]. As for baby food products, the protein content was in alignment with a British study for meat or fish puree with vegetables (3.1 g/100 g vs. 3.2 g/100 g) [30], but higher for vegetable and legume puree (1.53 g/100 g vs. 2 g/100 g) [30]. Results are presented in Table S4.
## 4.3. Associated Health Risks of Infant Formulas and Baby Food Products
According to Codex Alimentarius, starting formulas containing hydrolyzed protein or casein from cows’ milk should ideally include lactose and glucose polymers as their primary sources of carbohydrates [39]. The tested samples were compliant with this notion of Codex, where lactose was found to be the principal constituent. Additionally, in the current study, the majority of analyzed infant formula samples contained added sugars (fructose, glucose, sucrose) in different proportions. As per ESPHGAN, fructose should not be added to infant formulas, used during the first six months of a baby’s life [40], because of the risk of life-threatening symptoms in children with undiagnosed hereditary fructose intolerance [39]. Fructose was detected in all the starting formula samples; however, it was present in a higher concentration in four starting formula samples (3.7, 3.7, 5.3, and 4.8 g/100 g). This is of utmost importance because the samples are meant to be consumed by infants between the ages of 0 and 6 months. Furthermore, the addition of sucrose as an ingredient should be avoided in infant formula unless absolutely necessary [39], whereas, starting, follow-up formulas, and growing-up formulas included trace levels of sucrose, demonstrating that this advice was followed in the current study. The cariogenic potential of sucrose-containing solutions in the breast milk substitutes is concerning [41], as sucrose has been recognized as the most cariogenic among added sugars [42]. The contribution of added sugar to the daily intake was reported to be high in infant formulas and baby food products, specifically, in growing-up formulas, fruit puree, milk-based puree, and milky cereals. This is alarming, given the fact that an excessive intake of sugars encourages weight gain, dental caries, and in general, the development of noncommunicable diseases (NCDs), including obesity [15]. Low- and middle-income countries are currently dealing with the so-called double burden of malnutrition. Although undernutrition is taking the vast majority, a significant growth in obesity and overweight cases associated with NCDs is very common in children under 5 years of age [43]. This is clearly related to the children’s nutritional status, which includes exposure to high fat, calorie dense, and micronutrient-deficient meals [43]. Hence, if the baby food industry is truly dedicated to improving the health of children, the formulation of such foods should be adjusted to incorporate less sugar than is currently available in infant products.
There are no regulations in Codex Alimentarius and EFSA that specify the maximum level for saturated fatty acids, specifically palmitic acid. Notably, the highest proportion among all saturated fatty acids accounted for palmitic acid, in infant formulas and baby food products. Different studies have revealed its negative effect on infant health. A systematic review showed that the use of palmitic acid is associated with the decreased intestinal absorption of fat and calcium, hence a lower bone mass density [44,45]. This can lead to a higher risk of osteoporosis and childhood fractures in the future [46]. Further, a meta-analysis of randomized clinical trials indicated that palm-fed infants have harder stools [45,46,47]. Moreover, a high palmitic acid and low linoleic acid fractions are associated with myocardial infarction, stroke, left ventricular hypertrophy, and metabolic syndrome. It is also recognized that a large amount of saturated fat causes insulin resistance, glucose intolerance, metabolic syndrome, and low-grade inflammation [48].
The protein content in the majority of the analyzed samples of infant formulas and baby food products was non-compliant with the regulations. The contribution of protein to the daily intake was seen to be high among starting, follow-up, extra-care formulas, meat or fish puree with vegetables, and milk-based puree. Referring to the literature, an excessive amount of protein at a young age was linked to a lower calcium intake; additionally, non-breastfed infants who are dependent on high protein infant formulas acquire rapid increases in their body weight and fat mass, which increase the risk of overweight and obesity, diabetes, hypertension, and cardiovascular diseases later in life [49].
## 4.4. Nutrient Content and Labeling Discrepancies in Infant Formulas and Baby Food Products
There are scant data on packages about the types of fats and added sugars found in infant formulas and baby food products. When comparing the nutrition facts label with our measured values, it was determined that the majority of the items had inconsistencies in representing the actual nutrient content, and several of them had undeclared values. The current study’s findings coincided with an Emirati study [41], an Egyptian study [22], and a Spanish study [38], in which a range of differences was noted.
The disparity between the labeled and analyzed glucose levels in infant formulas was detected to be slight, ranging between $0.5\%$ and $2\%$, while some of these measurements had more or less detected glucose than was labeled. Furthermore, there was no indication of the amount of fructose or sucrose contained in the infant formulas on the packaging, despite the fact that both of these sugars were detected. Regarding the baby food products, the total amount of added sugar was not declared. Further, there was no indication of the level of trans fatty acids on the labels, despite the fact that the vast majority of analyzed infant formulas and baby food products contained trans fatty acids (all but three). As for saturated, mono-, and polyunsaturated fatty acids, the discrepancy between the declared and measured value was high, ranging between $2.58\%$ and $45.6\%$. These findings are rather frightening because, despite the fact that the total carbohydrate and fat levels were disclosed, the sugars, fatty acids, and their respective values were not included on the labels. This can lead to confusion for the parents who purchase these products.
## 4.5. Strengths and Limitations
To the best of our knowledge, this study is the first to investigate the nutrient composition of infant formulas and baby food products in a detailed manner, in Lebanon and the Middle East. However, due to the shortages of some items on the Lebanese market, only available infant formulas and baby food products were selected.
## 5. Conclusions
The current study provides information on the nutrient content of foods that are intended for infants and young children in Lebanon. The findings of this study are very alarming, revealing a high content of proteins and added sugars in the majority of infant formulas and baby food products. A non-compliance to the regulations and discrepancies in reflecting the real nutrient content on the nutrition fact label of these products was seen as well. Of utmost concern is that some nutrients exceeded the daily recommended intake. The analysis of the nutritional composition of infant formulas and baby foods that are available on the Lebanese market has brought to light some aspects that require careful evaluation in order to improve the feeding practices of infants and young children. This is a strong call to action for policymakers and high authorities in Lebanon to ensure that children can have the best, healthy, and nutritious start in life.
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|
---
title: Fasting Glucose Level on the Oral Glucose Tolerance Test Is Associated with
the Need for Pharmacotherapy in Gestational Diabetes Mellitus
authors:
- Natassia Rodrigo
- Deborah Randall
- Farah Abu Al-Hial
- Kathleen L. M. Pak
- Alexander Junmo Kim
- Sarah J. Glastras
journal: Nutrients
year: 2023
pmcid: PMC10005728
doi: 10.3390/nu15051226
license: CC BY 4.0
---
# Fasting Glucose Level on the Oral Glucose Tolerance Test Is Associated with the Need for Pharmacotherapy in Gestational Diabetes Mellitus
## Abstract
Gestational diabetes mellitus (GDM) has a rapidly increasing prevalence, which poses challenges to obstetric care and service provision, with known serious long-term impacts on the metabolic health of the mother and the affected offspring. The aim of this study was to evaluate the association between glucose levels on the 75 g oral glucose tolerance test and GDM treatment and outcomes. We performed a retrospective cohort study of women with GDM attending a tertiary Australian hospital obstetric clinic between 2013 and 2017, investigating the relationship between the 75 g oral glucose tolerance test (OGTT) glucose values, and obstetric (timing of delivery, caesarean section, preterm birth, preeclampsia), and neonatal (hypoglycaemia, jaundice, respiratory distress and NICU admission) outcomes. This time frame encompassed a change in diagnostic criteria for gestational diabetes, due to changes in international consensus guidelines. Our results showed that, based on the diagnostic 75 g OGTT, fasting hyperglycaemia, either alone or in combination with elevated 1 or 2 h glucose levels, was associated with the need for pharmacotherapy with either metformin and/or insulin ($p \leq 0.0001$; HR 4.02, $95\%$ CI 2.88–5.61), as compared to women with isolated hyperglycaemia at the 1 or 2 h post-glucose load timepoints. Fasting hyperglycaemia on the OGTT was more likely in women with higher BMI ($p \leq 0.0001$). There was an increased risk of early term birth in women with mixed fasting and post-glucose hyperglycaemia (adjusted HR 1.72, $95\%$ CI 1.09–2.71). There were no significant differences in rates of neonatal complications such as macrosomia or NICU admission. Fasting hyperglycaemia, either alone or in combination with post-glucose elevations on the OGTT, is a strong indicator of the need for pharmacotherapy in pregnant women with GDM, with significant ramifications for obstetric interventions and their timing.
## 1. Introduction
Gestational diabetes mellitus (GDM), defined as any degree of glucose intolerance with onset or first recognition during pregnancy [1], has become the fastest growing subtype of diabetes in many countries including Australia, with a doubling of incidence over the past decade, to now encompass at least $14\%$ of all Australian pregnancies [2,3]. The rise in incidence is multi-factorial, with increasing maternal age and a higher prevalence of obesity central to these trends [4]. The changes to the diagnostic criteria are also implicated in the increased incidence of GDM. Since the gradual uptake of the GDM diagnostic criteria recommended by the International Association of the Diabetes and Pregnancy Study Groups (IADPSG), the incidence of GDM in Australia has more than doubled. There are some benefits of diagnosing and treating GDM such as a reduction in rates of macrosomia, however, there is also a trend towards earlier timing of birth in mothers with GDM [2,5].
Though the treatment of hyperglycaemia in pregnancy improves pregnancy-related outcomes, there is limited evidence to guide decisions about how best to adapt to the demands of the growing patient population [6]. The increasing number of women with GDM necessitates the optimisation of treatment pathways, to provide greater medical and obstetric surveillance and intervention to the women most in need, whilst reducing unnecessary intervention in women with mild GDM. Women with GDM who require pharmacotherapy, such as insulin or metformin, have higher rates of adverse pregnancy outcomes [7]. Therefore, earlier triage of patients with GDM into treatment pathways based on the likelihood of requiring pharmacotherapy may optimise patient models of care.
Currently, the results of the universal 75 g oral glucose tolerance test (OGTT) performed at 24–28 weeks’ gestation provide important information that could be used to stratify women into low- and high-risk models of care. The aim of this present study was to assess the maternal characteristics and perinatal outcomes of women attending a tertiary multidisciplinary antenatal clinic, according to the OGTT result diagnostic of GDM. We hypothesised that women with higher glucose values on OGTT, or multiple glucose levels above the diagnostic target, would be more likely to need pharmacotherapy, and have adverse maternal and neonatal outcomes.
## 2.1. Study Design
A retrospective, cohort study was performed by reviewing the electronic medical record of women with GDM, who attended the multi-disciplinary Specialist Obstetric Clinic at Royal North Shore Hospital, Sydney, Australia from 2013 to 2017. Approval for this study was obtained from the Northern Sydney Local Health District Research Ethics Committee (Study Reference Number RESP/$\frac{15}{107}$). This study followed the precepts delineated in the “Strengthening the Reporting of Observational Studies in the Epidemiology (STROBE)” statement for cohort studies [8].
Maternal demographics were collected including age, ethnicity, body mass index (BMI) recorded at the first visit to the antenatal clinic, antenatal history, past medical history of polycystic ovarian syndrome (PCOS) and family history of diabetes or hypertension.
Gestational age at GDM diagnosis and diagnostic values of the 75 g oral glucose tolerance test (OGTT) results were recorded. GDM was diagnosed in accordance with the Australian Diabetes in Pregnancy Society (ADIPS) guidelines [9], which were based on the 75 g OGTT. Over the course of the study, ADIPS diagnostic criteria shifted due to the IADPSG recommendations, reflected in this study period. The old criteria involved an initial glucose challenge using 50 g of glucose, with a 1 h threshold of ≥7.8 mmol/L triggering the need for a 75 g OGTT. Criteria for diagnosis were as follows: fasting plasma glucose ≥5.5 mmol/L or 2 h plasma glucose ≥7.8 mmol/L. This was recommended till the end of December 2014, with the new ADIPS diagnostic criteria introduced in January 2015; the 50 g challenge test was removed and the 75 g OGTT was recommended for all patients with the following diagnostic criteria: fasting plasma glucose ≥5.1 mmol/L, 1 h plasma glucose ≥10 mmol/L or 2 h plasma glucose ≥8.5 mmol/L.
In our tertiary clinic, all women with GDM are managed by a multidisciplinary team, commencing with a standardised group GDM education session, led by a credentialed diabetes educator and a dietitian. Women were instructed to undertake self-monitoring of blood glucose (SMBG), perform fasting and 2 h postprandial glucose (PPG) levels and apply a low fat, carbohydrate portioned diet (total 175 g daily). In women not meeting treatment targets (until December 2014: fasting <5.5 mmol/L, PPG < 6.7 mmol/L, and after January 2015: fasting <5.0 mmol/L, PPG < 6.7 mmol/L according to ADIPS guideline recommendations), treatment was intensified to include pharmacotherapy. Therefore, in this study, the diet group comprised of women not requiring pharmacotherapy, the metformin group comprised women treated with metformin, to a maximum dose of 2 g daily, and the insulin group was treated with insulin monotherapy (short-acting: aspart, long-acting: isophane or detemir), and the metformin/insulin group was treated with both metformin and insulin.
Foetal growth ultrasound data (estimated foetal weight, abdominal circumference), perinatal outcomes, namely gestational age at delivery, mode of delivery (normal vaginal delivery (NVB), instrumental, including forceps and vacuum delivery, planned or emergency lower segment caesarean section (LSCS)), gender, birth weight, large for gestational age (LGA: defined as >90th centile for gender and gestational age) [10], small for gestational age (SGA: defined as <10th centile for gender and gestational age) [10] neonatal hypoglycaemia, jaundice, respiratory distress, neonatal intensive care unit (NICU) admission and the incidence of hypertensive conditions in pregnancy were recorded from the medical record.
## 2.2. Statistical Analysis
Differences between groups were compared using one-way analysis of variance (ANOVA) for continuous data. Categorical data were analysed using chi-squared test and logistic regression. The association between the 75 g OGTT glucose measures (fasting, 1 and 2 h) and the time to medication (either insulin, metformin or both) was modelled using Cox regression with gestational age as the time scale, with entry on the gestational day of GDM diagnosis. The outcome was gestational day at first treatment, and women with diet-controlled GDM were censored at the day of birth. The model was adjusted for ethnicity (Caucasian, South Asian, South-East Asian, Other), birth year (in year categories), parity (1, 2, 3+), history of PCOS, maternal age (entered as a continuous variable centred at 34), BMI (entered as a continuous variable centred at 24 Kg/m2). The interaction between the glucose measures and BMI (grouped as <25 and 25+) was also investigated, adjusting for the same covariates as above, without continuous BMI. Additionally, a composite glucose variable was created, segmenting the cohort into 6 groups based on their results on all 3 glucose measures (fasting and either 1 h or 2 h criteria, fasting criteria only, 1 h and 2 h criteria but not fasting, 1 h criteria only, 2 h criteria only, other), with criteria based on the IADPSG diagnostic levels.
The association between the 75 g OGTT glucose measures and perinatal outcomes were investigated using a Fine–Gray competing risk model [11]. A separate model was run for each perinatal outcome (preterm birth <37 weeks, early term birth <39 weeks, macrosomia >4 kg, large for gestational age, small for gestational age, birth by caesarean section (CS), birth by CS or instrumental, gestational hypertension at birth, transfer to NICU, neonatal hypoglycaemia, newborn respiratory distress). Birth, without the specified outcome, was considered a competing risk event. Time at risk of the outcomes started at 24 weeks’ gestation as the cohort was not at risk of the outcomes (measured at birth) until they reached at least 24 weeks’ gestation. The models are presented unadjusted, adjusted for ethnicity, birth year, parity, maternal age and BMI (adjusted model 1), and additionally adjusted for treatment mode (diet, insulin, metformin and both insulin and metformin; adjusted model 2).
The regression models produced hazard ratios (HRs) and $95\%$ confidence intervals, with the hazard ratio a measure of the relative risk of an outcome for the next time period for those yet to experience the outcome.
Statistical analyses were carried out using SAS Version 9.4 (SAS Institute Inc., Cary, NC, USA). p-value < 0.05 was considered statistically significant.
## 3.1. Study Population
Between 2013 and 2017, 654 singleton pregnancies were recorded, with mean maternal age of 34.1 years, parity of 1.6 and early pregnancy BMI of 25.4 kg/m2. The mean gestational age of GDM diagnosis was 25.6 weeks. There was no difference in maternal age or parity between the group diagnosed with GDM by the old criteria, compared to those diagnosed by the new criteria. However, women diagnosed with the new criteria had a higher early pregnancy BMI (25.9 vs. 24.6 kg/m2, Table 1). Women diagnosed with GDM by the new versus old criteria had higher fasting and 1 h glucose values on OGTT, however lower 2 h glucose value, compared to women diagnosed with GDM by the old criteria (4.7 vs. 4.6, 9.6 vs. 9.3 and 8.3 vs. 8.6 mmol/L at 0, 1, 2 h timepoint on 75 g OGTT).
Women were categorised into diagnostic groups based on the OGTT results (Table 2). Most women were diagnosed with GDM on one diagnostic timepoint alone ($13.9\%$, $11.9\%$ and $45.1\%$ on a single elevated result at 0, 1 or 2 h timepoint, respectively). There was a change in the proportion of women in each diagnostic group over time (2013–2017); from 2015, there was a significant increase in women diagnosed on the 1 h glucose value, corresponding with the changed diagnostic criteria to include a 1 h glucose value. The mean maternal age and parity were similar across all GDM diagnostic groups (Table 2). A greater proportion of women with low–normal BMI (<25 kg/m2) met the GDM diagnostic criteria based on the 1 h and/or 2 h glucose tests, whereas women with high BMI > 30 kg/m2 were more likely to have the GDM diagnosis based on fasting glucose with or without elevated 1 h and 2 h glucose values ($p \leq 0.0001$). With regards to ethnicity, there was a high proportion of South-East Asian women within groups diagnosed by the 1 h and/or 2 h glucose result. There was a higher representation of Caucasian ethnicity in the group of women diagnosed with GDM by elevated fasting glucose. There was no difference in the proportion of women with a history of GDM, pre-eclampsia, PCOS or a family history of diabetes between combined diagnostic groups.
## 3.2. GDM Diagnostic Group and Treatment Differences
Within this cohort of women with GDM, one in two women managed throughout pregnancy with lifestyle intervention alone (Table 3). In women who required pharmacotherapy, the mean age at the time of intervention was 27.6 weeks. Insulin was the most commonly prescribed medication used to manage GDM, $46.9\%$ of women were commenced on therapy within the 28–32-week gestational period, with $14.5\%$ commencing treatment at 33 weeks’ gestation or later.
Women managed by lifestyle intervention (diet) alone had lower fasting glucose levels on the 75 g OGTT compared to women who required treatment ($p \leq 0.001$, Table 3). In women with fasting glucose levels <4.3 mmol/L on the OGTT, $75.4\%$ were managed with diet intervention alone. In contrast, $73.6\%$ of women with a fasting glucose above 5.1 mmol/L required pharmacotherapy.
The 1 h glucose level on the diagnostic 75 g OGTT was predictive of the need for treatment ($$p \leq 0.016$$, Table 3). Two-thirds of women with 1 h glucose levels greater than 10.8 mmol/L were managed with pharmacotherapy, and specifically, $75.1\%$ of women were treated with insulin, mostly without metformin. Similarly, there were significant differences between the 2 h glucose level on the 75 g OGTT and the need for pharmacotherapy. A total of $36.7\%$ of women with a 2 h glucose level of less than 8.0 mmol/L were managed on diet alone, whereas $8.9\%$ required both metformin and insulin therapy. A total of $48.5\%$ of women with a glucose level of 9.4 mmol/L or more were managed with diet alone, and $8.3\%$ required both metformin and insulin therapy.
Almost $50\%$ of the cohort were diagnosed with GDM based on an elevated fasting glucose level with or without an elevated 1 h or 2 h glucose level. Interestingly, of the women with a diagnostic 2 h glucose level alone, $63.7\%$ were able to manage with diet intervention alone, whereas $27.3\%$ of women with a diagnostic fasting glucose level alone managed with diet alone. Women with ≥ 2 glucose values exceeding the diagnostic targets were the most likely to require insulin therapy, with $63.8\%$ of women with fasting and 1 h or 2 h readings meeting the diagnostic threshold requiring insulin therapy. A total of $34.5\%$ of women with diagnostic 1 and 2 h readings required insulin therapy ($p \leq 0.0001$).
On the diagnostic 75 g OGTT, women with a higher fasting glucose level were 1.77 times more likely to need medication than women with a fasting glucose level was less than 4.3 mmol/L (Figure 1, Supplementary Table S1). Women with a fasting level of 5.4 mmol/L or more were six times more likely to need medication than those with fasting levels less than 4.3 mmol/L (CI 3.76–9.18). After adjusting for ethnicity, birth year, parity, maternal age and BMI, women with a fasting glucose value ≥ 5.4 mmol/L were still 3.9 times more likely to need pharmacotherapy than women with a fasting glucose < 4.3 mmol/L (CI 2.41–6.31). Women with fasting levels between 4.6–5.1 mmol/L had almost three times the risk of needing pharmacotherapy ($95\%$ CI of 1.93–4.33 unadjusted). Following adjustment for covariates this was still significant, with a hazard ratio of 2.62 (CI 1.74–3.95).
The risk of needing pharmacotherapy was analysed by combined groups (Figure 1, Supplementary Table S1). Overall, the group with fasting hyperglycaemia alone (≥5.1 mmol/L) had twice the likelihood of needing treatment (CI 1.37–3.14). Similarly, women with a 1 h OGTT reading of 10.8 mmol/L or more were 2.15 more likely to require treatment after adjustment. An elevated 2 h glucose reading alone did not increase the risk of requiring treatment, however, a combination of 1 and 2 h readings above the diagnostic cut-offs (and no fasting hyperglycaemia) had 1.82 times the hazard of needing pharmacotherapy (CI 1.30–2.56), and combined fasting and post glucose hyperglycaemia had an adjusted hazard ratio of 2.96 (CI 2.02–4.34) (Supplementary Table S2).
Women with lower BMI had a lower risk of pharmacotherapy than women with higher BMI across all glucose categories and groups (Figure 2a–d). There was no significant interaction between BMI groups (≥25 kg/m2 or <25 kg/m2) and the OGTT glucose values.
## 3.3. GDM Diagnostic Group and Third Trimester Foetal Ultrasound
Within the total cohort of women, $58\%$ had a foetal ultrasound performed in the third trimester. There were no significant differences in foetal weight (EFW) or abdominal circumference (AC) by the third trimester scan results, between the diagnostic groups based on the OGTT results (Supplemental Table S3). This may be due to the differences in gestational age at which the scans were carried out on average, which was significantly different between groups ($p \leq 0.05$). Further, women with diet-controlled GDM may not routinely be offered additional growth scans.
## 3.4. GDM Diagnostic Group and Perinatal Outcomes
The gestational age at delivery varied according to GDM diagnostic group, though within all diagnostic groups, the highest proportion of women delivered within the 39th week. ( $$p \leq 0.031$$, Table 4). There was a trend towards earlier delivery in women with ≥2 glucose values diagnostic of GDM (either fasting and 1 h and 2 h, or 1 h and 2 h only groups), with $57.2\%$ of women diagnosed with GDM by combined fasting and post glucose levels giving birth before 39 weeks.
The majority of women ($64.2\%$) had a vaginal birth ($49.2\%$ normal vaginal, $15\%$ instrumental), whilst $30.4\%$ had a planned caesarean section and $5.2\%$ had an emergency caesarean section.
There was no significant difference in the mode of delivery between groups (normal vaginal delivery rate was $49.2\%$ overall), LGA ($9.6\%$), SGA ($11\%$), macrosomia ($6\%$), pre-eclampsia occurred ($1.7\%$) and gestational hypertension ($5\%$). Further, there was no significant difference in neonatal complications, including respiratory distress ($7.6\%$), jaundice ($7.3\%$) or neonatal hypoglycaemia ($8.4\%$) between the groups (Table 4).
As detailed in the statistical methods, three statistical models were utilised to determine the impact of the diagnostic group on perinatal outcomes, with each model using the fasting alone group as the reference category: [1] unadjusted model, [2] model 1 adjusted for ethnicity, birth year, parity, maternal age and BMI and [3] model 2 adjusted for ethnicity, birth year, parity, maternal age, BMI and treatment type. Both unadjusted and adjusted models demonstrated no significant association between the diagnostic groups on OGTT and preterm birth less than 37 weeks (Figure 3, Supplementary Table S4). There was an increased risk of early term birth (<39 weeks) in women with mixed fasting and post-glucose hyperglycaemia (unadjusted HR 1.74 (CI 1.13–2.67)), and it remained significant after adjustment in model 1 (HR1.72 (CI 1.09–2.71)) and model 2 (1.65 (CI 1.06–2.56)). Neither the unadjusted model, model 1 nor model 2 found a relationship between the diagnostic group and macrosomia, large for gestational age, or small for gestational age. Caesarean section was more likely in the maternal group when the diagnostic group included both 1 and 2 h, but not fasting hyperglycaemia, in model 1 (HR 1.85 (CI 1.07–3.19)) and model 2 (HR 1.86 (CI 1.08–3.23)), as well as for 1 h only in model 1 (HR 1.85 (CI 1.05–3.27)) and model 2 (HR 1.91 (CI 1.08–3.40)).
There were no significant differences between the GDM diagnostic category and other neonatal outcomes such as neonatal ICU, hypoglycaemia or respiratory distress.
## 4. Discussion
In this large, single-centre retrospective study, we determined that women with fasting hyperglycaemia ≥ 5.1 mmol/L, at the time of the diagnostic OGTT were more likely to need pharmacotherapy for the treatment of GDM than those with lower fasting levels. Women with fasting levels of OGTT between 4.6–5.1 mmol/L (lower than the current GDM diagnostic thresholds) were still at risk of needing medication. Women with the GDM diagnosis based on elevated 1 h or 2 h glucose levels were more likely to maintain diet intervention alone throughout the pregnancy than those with fasting hyperglycaemia. The 1 h glucose level was associated with a higher risk of treatment, but only at the very highest level of 10.8 mmol/L or more. Almost $50\%$ of women were diagnosed with GDM by the 2 h glucose value on OGTT alone, yet the 2 h value had the least association with the need for pharmacotherapy or maternal and foetal outcomes.
GDM arises from a multifaceted process of β-cell dysfunction, oxidative stress and chronic insulin resistance, which is compounded by placental metabolic hormones and cytokines, thereby transforming the physiological insulin resistance of pregnancy into the pathophysiological condition of GDM [12]. Post-prandial glucose levels can be manipulated by lifestyle modification [13,14], including dietary adjustment and exercise, strongly advocated by diabetes education teams. Fasting hyperglycaemia occurs as a result of diurnal changes in cortisol and growth hormone secretion and increased hepatic glucose production and reduced hepatic insulin sensitivity amplified in pregnancy [15,16,17]. Lifestyle manipulation is unlikely to alter these physiological drivers and is rarely sufficient to overcome persistent fasting hyperglycaemia resulting in the need for pharmacotherapy intervention, as elucidated in this study. Furthermore, women with both fasting and post-prandial hyperglycaemia are likely to have multiple pathways of metabolic dysregulation at play, thereby making pharmacotherapy highly likely to aid the control of hyperglycaemia in this group of women with GDM [17,18].
In this study, it was reassuring that the glucose levels on OGTT were not associated with the majority of maternal and neonatal outcomes studied, suggesting successful treatment to target glucose levels. Women with GDM have demonstrated higher risks of pre-eclampsia, shoulder dystocia, caesarean section, LGA and malformations [19,20]. As demonstrated by the Australian Carbohydrate Intolerance Study in Pregnancy Women (ACHOIS) Trial Group, women treated with dietary advice, blood glucose monitoring and pharmacotherapy had significantly lower rates of serious perinatal complications [6]. Treatment benefit has been further supported by many other studies [21,22]. The diagnosis of GDM has been found to be associated with increased intervention, such as caesarean section [23,24]. Compared to the national average, in which $64\%$ of women had a vaginal birth in 2019 [25], reassuringly in our study, $64.2\%$ of women with GDM had vaginal births, either normal or instrumental.
The results of this study provide important information to guide clinicians and policymakers on ways to triage women to appropriate GDM models of care. Because women with ≥2 glucose levels diagnostic of GDM and/or fasting glucose value ≥4.6 mmol/L were more likely to need medication, these women could be triaged into high-risk antenatal clinics, during which prescribers are present to initiate and titrate pharmacotherapy, and closely monitor the pregnancy. In contrast, women with GDM diagnosed by either 1 h or 2 h glucose level alone, and glucose level <4.6 mmol/L could be allocated to lower risk models of care, given that these women are less likely to require pharmacotherapy intervention, with good maternal and perinatal outcomes. Women with high BMI are more likely to need medication, and hence these women may require closer monitoring. Together, these factors may have the potential to contribute to more personalised risk calculations to inform the models of care most relevant to an individual woman.
A limitation of this study is that it was a single-centre study. However, the centre is a tertiary referral centre, with a diverse and populous catchment. Additionally, the retrospective nature of this study meant that some data, such as ultrasound findings, were not available for the entire group of subjects. Further, this was not a randomised study, and therefore therapy decisions were subject to patient and/or physician preference. The changing diagnostic criteria, over the course of the study period also introduced heterogeneity to the data, although we did base our study groups on the current OGTT diagnostic criteria.
## 5. Conclusions
In summary, this study demonstrates that fasting hyperglycaemia on the OGTT at the time of GDM diagnosis is strongly associated with the need for pharmacotherapy. GDM is a condition where timely treatment is imperative, and attempting diet and lifestyle modification is costly, both in time and resources. Our study provides data to inform clinical triage of patients most likely to require pharmacotherapy from the time of GDM diagnosis, and it informs models of care and treatment pathways that can streamline finite resources to the women most likely to benefit from pharmacotherapy and closer obstetric monitoring.
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|
---
title: Long-Term Exposure to Isoflavones Alters the Hormonal Steroid Homeostasis-Impairing
Reproductive Function in Adult Male Wistar Rats
authors:
- Sara Caceres
- Belén Crespo
- Angela Alonso-Diez
- Paloma Jimena de Andrés
- Pilar Millan
- Gema Silván
- María José Illera
- Juan Carlos Illera
journal: Nutrients
year: 2023
pmcid: PMC10005734
doi: 10.3390/nu15051261
license: CC BY 4.0
---
# Long-Term Exposure to Isoflavones Alters the Hormonal Steroid Homeostasis-Impairing Reproductive Function in Adult Male Wistar Rats
## Abstract
The consumption of isoflavones is gaining popularity worldwide due to their beneficial effects on health. However, isoflavones are considered to be endocrine disruptors and cause deleterious effects on hormone-sensitive organs, especially in males. Therefore, this study aimed to determine if a continuous and prolonged exposure to isoflavones in adult males altered the endocrine axis effect of testicular function. For this purpose, seventy-five adult male rats were administered with low and high mixtures of isoflavones (genistein and daidzein) for 5 months. The determination of steroid hormones (progesterone, androstenedione, dehydroepiandrosterone, testosterone, dihydrotestosterone, 17β-estradiol, and estrone sulphate) was carried out in serum and testicular homogenate samples. Sperm quality parameters and testicular histology were also determined. The results revealed that low and high doses of isoflavones promote a hormonal imbalance in androgen and estrogen production, resulting in a decrease in circulating and testicular androgen levels and an increase in estrogen levels. These results are associated with a reduction in the sperm quality parameters and a reduction in the testicular weight, both in the diameter of the seminiferous tubules and the height of the germinal epithelium. Altogether, these results suggest that a continuous exposure to isoflavones in adult male rats causes a hormonal imbalance in the testes that disrupts the endocrine axis, causing defects in testicular function.
## 1. Introduction
It is well established that exposure to endocrine disruptors can suppose a health risk [1]. These molecules act similarly to endogenous hormones, causing alterations to the metabolism. Isoflavones are considered to be endocrine disruptors since their structure, similar to 17b-estradiol, gives them highly estrogenic properties that affect the reproductive tract, among other organs, and because they are naturally found in plants such as soybeans [2]. On the other hand, several authors associate isoflavones’ effects with a considerable number of benefits [3] that include protection against breast or prostate cancer, a reduction in the incidence of cardiovascular disease, or a relief of menopausal symptoms [4]. A particular benefit that is gaining relevance is the effect of isoflavones on adipogenesis regulation, making them able to reduce body weight [3,5].
Due to all of these beneficial and nutritional properties, soy-based products are rising in popularity, leading to an increase in their consumption worldwide, from the infant to elder populations. Interestingly, the United States Department of Agriculture (USDA) has included the use of soy-based products as an alternative to animal proteins [6,7]. It must be considered that soy-based products that are consumed daily, such as soymilk, soy-yogurts, or infant formulas, contain a range of isoflavones between 25 and 190 mg/100 g [6], which could turn into a continuous and elevated isoflavone intake.
On the other hand, the endocrine axis function is influenced by environmental signals and the developmental period. These factors alter the gene expression and cell signaling that permit the organism to adapt and develop [2]. As isoflavones have the capacity to interfere with the endocrine axis function, their consumption can lead to changes in the cell signaling pathways that are associated with hormonal homeostasis. Therefore, the consumption of isoflavones may cause a deleterious effect on the organism. Most studies have been carried out on females, due to the estrogenic activity of isoflavones. However, the endocrine axis function is different in males and females and, therefore, isoflavones may alter the endocrine axis in a different manner. Indeed, several studies have affirmed that isoflavone consumption produces reproductive disorders in both males and females [2,8,9,10,11]. In women, supplementation with isoflavones was found to affect endocrine profiles, causing alterations to their menstrual cycles [8] and menstrual regularity [11]. Regarding men, recent studies have reported that isoflavone intake has an impact on testicular function and sperm quality [2,10]. Moreover, it has been shown that these disorders in testicular functions may be product of hormonal changes caused by isoflavone consumption. In prepuberal male rats, an administration of low doses of isoflavones leads to alterations in androgen and estrogen serum and testicular levels, which triggers a delay in the onset of puberty.
Furthermore, the rats that were fed with high doses of isoflavones did not reach puberty during the time of experiment [9,12]. Other authors also associated these changes in hormonal levels with testicular histological changes, deficiencies in spermatogenesis, and a reduced content of spermatozoa [10,13,14]. However, these studies differ with regard to the gender and the age of the animal used, the dose of isoflavones, the administration route, and the time of the exposure to the isoflavones. Most of the studies carried out on males were developed with a short-term exposure to isoflavones and during early periods of their development, where the endocrine axis plays a critical role, and minimal changes can alter the axis’ regulation. Therefore, it can be assumed that exposure to phytoestrogens during periods such as puberty may exert an effect on the endocrine axis. However, few studies have considered the effects of a long-term exposure to isoflavones in adult males. Most of the studies carried out on adult males administrated the isoflavones for a short period of time, showing that isoflavones cause several dysfunctions [14,15].
Assuming that isoflavones cause a dysregulation in testicular function, this study aims to elucidate if a long-term exposure to isoflavones can cause deleterious effects on the endocrine axis, as well as induce testicular changes in adult intact male rats that are not exposed to any endocrine changes related to development. For this purpose, adult male rats were administered with low and high doses of isoflavones for 5 months, and their steroid hormone profiles and sperm quality parameters were determined.
## 2.1. Animals
A total of seventy-five 60-day old male Wistar rats (RjHan: WI, Janvier Labs, Madrid, Spain), weighing 306.52 ± 12.71 g at the beginning of the experiment, were used. In order to standardize the experimental conditions, the choice of this strain of rats was considered after previous studies on this same strain, as previously detailed [9,12,13]. The rats were housed in Meraclon cages, with dimensions of 25 cm × 47.5 cm × 20 cm, divided in groups of five animals per cage, and maintained in a temperature-, humidity-, and light-controlled room: 20 ± 2 °C; $45\%$ relative humidity; and 12-h light:12-h dark cycle (from 08:00 a.m. to 08:00 p.m.). All the rats were fed with a standard laboratory pellet commercial diet (Sodispan S.L., Madrid, Spain) and water ad libitum. Their absolute food and water consumption was measured weekly in all the cages, as previously reported [16]. Briefly, their food and water consumption was determined by subtracting the leftover volumes/weights from the initial volumes/weights. Their food consumption was expressed as g/rat/week and their water consumption as ml/rat/week. The required sample size that was needed to simultaneously compare the normal means of the three experimental groups (control plus two treatments) was obtained using the sample size determination module of the statistical package Statgraphics Centurion XVI (Statpoint Technologies, Inc., Warrenton, VA, USA). The experimental protocols adhered to the guidelines of the Council of European Union, and were approved by the Institutional Animal Care and Use Committee of the University Complutense of Madrid, and the Animal Protection Area of the Community of Madrid, Spain (Ref: PROEX $\frac{175}{19}$).
## 2.2. Dietary Treatment
The isoflavones were administered orally over a period of 5 months (20 weeks). The selection of the doses was based on previous studies [9,12]. The rats were randomly assigned to three groups of 25 rats each: control, low mixture of isoflavones (17 mg kg−1 day−1 genistein + 12 mg kg−1 day−1 daidzein), and high mixture of isoflavones (170 mg kg−1 day−1 genistein + 120 mg kg−1 day−1 daidzein) (LC Laboratories, Woburn, MA, USA). The isoflavone mixture was administered by dissolving them in drinking water at the studied concentrations, allowing for a voluntary consumption by the animal. In order to provide the animals with the correct isoflavone dilutions, two weeks before carrying out the experiment, the water consumption was measured daily in all the cages to estimate the average water consumption. Every four days, the drinking water was replaced to provide the animals with fresh isoflavone dilutions. For the control group, drinking water without isoflavones was administered.
Data from the rat’s body weights were collected each week, in order to observe the effect of the isoflavones in this parameter, and to re-calculate the isoflavone dilutions. Every 4 weeks, five animals from each group were sacrificed by cervical dislocation. Prior to sacrifice, the animals were anesthetized, and their blood samples were collected.
## 2.3. Serum Samples
Blood collection was performed randomly on five animals of each group every 15 days, from the dorsal aorta under anesthesia, and prior to sacrifice via cardiac puncture. The animals were anesthetized with isoflurane (IsoVet 1000 mg/g, B Braun VetCare SA, Barcelona, Spain) at $4\%$ for induction and $1.5\%$ for the maintenance of the supplies, in a fresh gas flow rate of 0.6 L of oxygen/min. The blood samples were centrifuged at 1200× g for 20 min at 4 °C. The serum was separated and stored at −20 °C until the hormonal analysis.
## 2.4. Testes Homogenates and Histology
Testes were collected for homogenates and histology at necropsy, and the testes’ weight and length were measured. The percentage of the testicular weight was calculated relative to the body weight. The testicular volume was calculated as (width2 × length)/2, and was expressed as a percentage relative to the body weight. To perform the homogenates, the right testes were homogenized in 4 mL of PBS (pH 7.2) and centrifuged at 1200× g, for 20 min at 4 °C. Supernatants were collected and stored at −80 °C until the hormonal assays. The left testes and epididymis were fixed in a $10\%$ buffered formalin solution (pH 7.4) for 24 h. Then, the samples were trimmed, embedded in paraffin wax, sectioned at 3 µm thickness, and stained with hematoxylin and eosin (H&E) for a light microscopic examination.
The content of the spermatozoa in the testes and epididymis was semi-quantitatively assessed and categorized according to the spermatozoa found in >$75\%$ of the epididymal ducts and testicular tubules as: 0 (an absence of spermatozoa), 1+ (few spermatozoa, where the lumens of the tubules were partially empty), and 2+ (the tubules were filled with abundant spermatozoa) [12,13]. For the measurement of the seminiferous tubule diameter, a total of 10 round cross sections of the seminiferous tubules were chosen in each rat. Then, two perpendicular diameters of each cross-section of the seminiferous tubules were measured at a magnification of ×400 and the mean of these was calculated. Moreover, the germinal epithelium height of the seminiferous tubules was also measured in four equidistant parts in each cross-section of the seminiferous tubules, and the mean was calculated [17].
Finally, the degree of testicular degeneration, characterized by few or no germ cells and sustentacular cells, was established as follows: 0 (an absence of testicular degeneration), 1+ (<$25\%$ of tubules affected), 2+ (25–$50\%$ of tubules affected), and +3 (>$75\%$ of tubules without germ cells).The results that are shown correspond to the 4th week of the experiment (the beginning of treatment) and the 20th week of the experiment (the end of treatment).
## 2.5. Sperm Quality
Epididymal sperms were collected immediately after euthanizing the animals. The right epididymis were incubated in Ham’s F10 medium and a thick cut on the tail of the epididymis was performed to allow the sperm to swim out of the epididymis [18]. The sperm motility was evaluated by placing a drop of sperm suspension between a slide and a cover slip and was observed at 100× using a phase contrast microscope. A total of four different fields were evaluated and expressed as a percentage of the motile sperm of the total sperm counted. The sperm counts were obtained as described by Badkoobeh et al. [ 18]. Briefly, 5 µL of the sperm suspension was fixed in a solution containing $0.35\%$ of formalin, $5\%$ NaHCO3, and $0.25\%$ of trypan blue. Approximately 10 µL of the fixed sperm solution was transferred to a Neubauer chamber to perform cell counts in light microscopy at 400× magnifications. The sperm concentrations were expressed as millions per ml (106/mL). To analyze the sperm viability, 20 µL of the sperm suspension was mixed with a stained solution containing $1\%$ eosin Y and $5\%$ nigrosine, and placed on microscope slides. Then, the slides were viewed in a light microscope at 400× magnifications [18]. Live sperm were not stained, and dead sperm were stained. In total, 200 spermatozoa were counted on each slide and the results were expressed as a percentage of the viable spermatozoa of the total sperm count. To assess the membrane integrity, a hypo-osmotic swelling test (HOST) was used, as previously described by Vaez et al. [ 19]. A total of 20 µL of the sperm suspension was mixed with 200 µL of the hypo-osmotic solution (100 mOsm/kg), consisted in 0.735 g of sodium citrate and 1 g of fructose, dissolved in 100 mL of distilled water, and the mixture was incubated at 37 °C over an hour. Then, a smear of the content was carried out and observed under a phase-contrast microscope at 400× magnifications. In total, two hundred spermatozoa per sample were counted. The spermatozoa with coiled tails were considered to be HOST-positive sperm. The results were expressed as a percentage of the HOST-positive sperm of the total sperm count.
## 2.6. Hormone Determinations
The serum and testes homogenates steroid hormone concentrations (estrone sulphate (SO4E1), 17b-estradiol (E2), androstenedione (A4), testosterone (T) and progesterone (P4)) were measured by a competitive enzyme immunoassay (EIA), previously validated by [9]. All the antibodies were developed in the Department of Animal Physiology (UCM, Madrid, Spain). Dihydrotestosterone (DHT) and dehydroepiandrosterone (DHEA) serum and the testes homogenates concentrations were measured by using a commercial enzyme immunoassay kit (Demeditec Diagnostics GmbH, Kiel, Germany), following the manufacturer’s instructions, and specific for this species.
Briefly, 96-well flat-bottom medium-binding polystyrene microplates (Greiner Bio-One, Madrid, Spain) were coated with the appropriate purified antibody dilution overnight at 4 °C. Afterward, the plates were washed and coated with standard and tumor homogenate samples that had been previously diluted in a conjugate working solution (CWS). After conjugate incubation, the plates were washed, and Enhanced K-Blue TMB substrate (Neogen, Lexington, KY, USA) was added to each well. Finally, colorimetric reaction was stopped by the addition of $10\%$ H2SO4 to each well. The absorbance was read at 450 nm using an automatic plate reader. The hormone concentrations were calculated by the means of a software developed for this technique (ELISA AID, Eurogenetics, Brussels, Belgium). A standard dose–response curve was constructed by plotting the binding percent against each steroid hormone standard concentration. All the hormone concentrations were expressed in ng/gr for the testes homogenates, and in ng/mL for the serum samples, except the E1SO4 and E2 serum levels that were expressed in pg/mL.
## 2.7. Statistics
The statistical analysis was conducted with IBM SPSS Statistics 27 software (UCM, Madrid, Spain). The results were expressed as the means ± SE. The Kolmogorov–Smirnoff test was used to assess the goodness-of-fit distribution of the collected data. Most of the parameters that were studied were noted to be parametric (body weight, testicular volume and weight, sperm count and hormone integrity, histological parameters, and hormonal determinations). For the comparison between the control and experimental groups, the means were analyzed via one-way analysis of variance (ANOVA), followed by a Bonferroni post hoc test. The parameters of sperm motility and viability were noted to be non-parametric, therefore, the Kruskal–Wallis test was used to compare the data. For the comparison between the control and experimental groups, a Mann–Whitney test was used. In all the statistical comparisons, $p \leq 0.05$ denoted significant differences.
## 3.1. Body and Testicular Weight
The results on body weight revealed that, during the first weeks of experimentation, the consumption of isoflavones at low or high doses did not affect body weight significantly. However, after the 12th week of experimentation, the group that were administered with low doses demonstrated a significant loss in body weight ($p \leq 0.05$) compared to the control group, while those receiving high doses showed a significant gain in body weight ($p \leq 0.05$) (Figure 1A). Nevertheless, no differences in the food and water consumption were observed among the three experimental groups (Figure S1).
In addition, testicular weight (Figure 1B) was significantly reduced ($p \leq 0.05$) in the rats administered with low and high doses of isoflavones from 12th week of experimentation. However, there were no significant differences in the testicular volume between the control and experimental groups (Figure 1C).
## 3.2. Testis Histopathological Analysis
A histopathological analysis of the testis of male rats (Table 1), revealed that the content of spermatozoa within the seminiferous tubules and epididymis was abundant in the control and low dose groups, and partially abundant in the high dose groups ($80\%$ of rats had a moderate content of spermatozoa, and $20\%$ had an abundant content of spermatozoa) at the beginning of treatment. At the end of treatment, the low and control dose groups continued to have an abundant content of spermatozoa. Interestingly, in the high dose group, $40\%$ of the rats had no spermatozoa within the testis and epididymis, and the rest had moderate or numerous spermatozoa. However, these differences between the control and high dose groups were not significant ($$p \leq 0.072$$).
Regarding the seminiferous tubule diameter, the results showed that the administration of isoflavones at high doses caused a significant reduction ($p \leq 0.05$) in the tubular diameter compared to the control and low dose group at the end of treatment (Figure 2A). Likewise, the high dose group had a significant reduction ($p \leq 0.05$) in the germinal epithelium height at the end of treatment compared to the control group (Figure 2B). No significant differences were found between the control and experimental groups at the beginning of treatment.
Differences were found in the testicular degeneration between the control and experimental groups at the end of treatment (Figure 3). In total, $40\%$ of the rats from the low dose group and $80\%$ of the cases from the high dose group presented with testicular degeneration. Specifically, for the high dose group, $40\%$ of the rats with degeneration presented with degenerative changes in most of the seminiferous tubules of the testis, and in the other $40\%$ of cases, in more than $25\%$ of the tubules. At the beginning of treatment, a slight testicular degeneration, in less than $25\%$ of the seminiferous tubules, was found in $20\%$ of the rats from the low dose group.
The testicular degeneration of these cases was localized in the seminiferous tubules at the periphery of the tunica albuginea, except in those cases in which most of the seminiferous tubules were degenerated, where it was characterized by an increased thickness of the basement membranes and a total absence of germinal epithelium, being that the majority of the degenerated seminiferous tubules were exclusively lined by Sertoli cells (Sertoli cell pattern).
## 3.3. Sperm Quality
Our results revealed that the long-term consumption of high and low doses of isoflavones alters sperm quality (Table 2). From the start of treatment to the 16th week, there were no differences in the sperm quality. However, from the 16th week, the differences were notable, being statically significant ($p \leq 0.05$) by the end of the study. The sperm motility and sperm count were significantly reduced ($p \leq 0.05$) in the low and high dose groups compared to the control group. However, the sperm viability and membrane integrity were only significantly reduced in the high dose groups.
## 3.4. Serum Steroid Hormone Determinations
Regarding the serum steroid hormone concentrations, it can be observed that the control group presented physiological hormone fluctuations that were altered in the rats that were administered with the isoflavones, and that most of these fluctuations were related to estrogen and androgen levels (Figure 4). The P4 levels (Figure 4A) in the experimental and control groups follow the same pattern, denoting that the P4 concentrations diminished significantly ($p \leq 0.05$) with the low and high doses of the isoflavones during the first weeks of the experiment.
On behalf of the androgen concentrations, no significant differences were found in the DHEA levels, however, the A4 concentrations revealed alterations (Figure 4B,C). The control and experimental groups followed the same pattern: in the control group, two peaks on the serum A4 concentrations in the 8th and 16th weeks of the experiment can be observed. Nevertheless, for the low and high doses of the isoflavones, these two peaks correspond to the 6th and 14th weeks, 1 week before the control group, resulting in significant changes ($p \leq 0.05$) between the groups. Additionally, the T and DHT results showed that the isoflavone consumption at the low and high doses reduced these androgen levels significantly ($p \leq 0.05$) during all the experiments (Figure 4D,E).
In terms of the estrogen concentrations (Figure 4F,G), significantly higher levels of both the estrogens analyzed (E1SO4 and E2) were found in the experimental groups compared to the control group. The control group also showed an increase in the E1SO4 levels at the 16th week, and in the E2 in the 14th week. As for androgens, the rats with the high and low doses of the isoflavones presented the same increases before the control group did (in the 16th week for the E1SO4 concentrations, and in the 12th week for the E2 concentrations).
## 3.5. Testis Steroid Hormone Determinations
Although in the serum hormone concentrations, several changes were observed in the isoflavone groups, in the testis hormone concentrations, the differences that were found were not that significant, except for the androgen and estrogen levels (Figure 5). Regarding the P4, DHEA, and A4 (Figure 5A–C), no significant differences were found between the control and experimental groups.
Interestingly, the testis T levels were reduced in the low and high mixtures of the isoflavones compared to the control group (Figure 5D). This reduction was significant ($p \leq 0.05$) from the 8th week until the end of experiment. In addition, the DHT concentrations (Figure 5E) tended to decrease, but not significantly from the 8th to 16th week of the experiment. However, in the 4th and 20th weeks, the DHT concentrations of the isoflavone groups were significantly increased ($p \leq 0.05$) compared to the control group.
On behalf of the testis estrogen levels, changes were found between the control and experimental groups (Figure 5F,G). At the 4th week of experiment, a significant increase ($p \leq 0.05$) was observed in the E1SO4 levels of the rats undergoing isoflavone consumption, although from the 8th week, these levels were significantly decreased ($p \leq 0.05$) until the end of experiment. However, the E2 levels in the isoflavone groups were significantly higher ($p \leq 0.05$) from the 4th week until the 20th week.
These differences in the T and E2 concentrations between the control and experimental groups are also reflected in the T/E2 ratio (Figure 6). The results showed a reduction in the T/E2 ratio of the isoflavone groups from the 8th week until the end of experiment, denoting a hormonal imbalance.
## 4. Discussion
Extensive studies have contemplated the beneficial effects of the consumption of isoflavones on the relief of menopausal symptoms, lowering the risk of cancer, or decreasing the risk of obesity [3,20]. However, different investigations have elucidated that their intake can cause certain disorders, especially hormonal, that affect the reproductive tract [9,21].
Nevertheless, the effect, beneficial or detrimental, of isoflavones depends on multiple factors, such as age, sex, the dose ingested, or the time of the intake. As they are compounds that are structurally similar to 17b-estradiol [20], it has been observed that, during critical periods of development, the intake of isoflavones causes hormonal disorders that can even delay the onset of puberty [9]. Therefore, in this study, the effect of a long exposure to isoflavones on the reproductive function in adult male rats is determined.
Isoflavones and obesity have been linked due to the estrogenic characteristic of isoflavones. It has been speculated that isoflavones reduce body weight [3,22] by decreasing the activity of the lipoprotein lipase (LPL) [23]. This is partially explained by E2 playing an important role in the regulation of adipocyte development [24]. When E2 binds to the estrogen receptors, they decrease the LPL activity and, therefore, lipogenesis [23]. Interestingly, in vitro results on mouse bone marrow cells have showed that a low exposure to genistein inhibited adipogenesis, whereas higher levels of genistein produced a stimulation of adipogenesis [25]. According to this, our study revealed that the intake of low concentrations of isoflavones produced a significant loss of body weight in male rats from the 12th week of intake. However, exposure to higher concentrations produced a significant gain in body weight. In vivo studies with rats have also revealed controversial results. Some studies have reported that the intake of high doses of genistein did not affect body weight [22], whereas other studies have demonstrated that dietary isoflavones decrease body weight [5]. This discrepancy of results could be due to the time of the exposure and the animal gender. Most of the studies were carried out on female rodents, whose metabolism is different to males, as this study demonstrated. Additionally, in this study, the differences in body weight started to be notable 12 weeks after the beginning of the experiment, therefore, the animals were consuming dietary isoflavones for a long time before observing the effects, denoting an accumulative impact of the isoflavones on males.
Isoflavones also affect testicular weight. Our results revealed that a long exposure to dietary isoflavones produces a significant decrease in the testis weight/body weight ratio of adult male rats. Preliminary studies using prepuberal male rats did not show differences in their testis weights after a short-term exposure to isoflavones [12], however, other studies have shown that different isoflavone diets produced an increase in testis weight [26].
Therefore, the differences found in the testis weights could affect the reproductive function. As endocrine disruptors, isoflavones can affect testicular structures and dysregulate the spermatogenesis processes that lead to the production of deficient sperm [14,27]. The histology results from the control and experimental rats revealed the presence of abundant spermatozoa in the seminiferous tubules in all groups, denoting that spermatogenesis would not be affected by isoflavones administration. However, the results obtained in the study of the sperm quality parameters (the sperm count, sperm viability, sperm motility, and membrane integrity) showed significant reductions in the rats administered with high doses of isoflavones compared to the control group. Interestingly, in the low dose groups, only the sperm motility and sperm count were significantly reduced compared to the control group. According to this, several studies have demonstrated that a long-term exposure to phytoestrogens affects reproductive success by reducing sperm quality parameters such as production and motility [28]. Indeed, it has been suggested that phytoestrogens can be present in the reproductive system, influencing spermatogenesis and affecting the sperm quality in Chinese men [10]. Therefore, a continuous exposure to isoflavones in low and high doses affects sperm quality, which can lead to problems in fecundity [29]. These changes found in the sperm quality parameters could be related to the reduction in the diameter of the seminiferous tubules and the germinal epithelium height. Significant differences in these measures were found in the high dose groups, in which the sperm quality parameters were reduced as well. Preliminary studies revealed that the consumption of isoflavones during puberty affects the number of spermatozoa. Low doses of isoflavones caused a great reduction in the content of spermatozoa in the seminiferous tubules, whereas the rats treated with high doses of isoflavones showed a fewer number of spermatozoa [13]. These results corroborate that isoflavones can exert an effect on the last stages of spermatogenesis processes, by altering the hormonal components and reducing the quantity of the available spermatozoa. Other authors have also demonstrated that the consumption of isoflavones causes a reduction in the seminiferous tubule diameter and germinal epithelium height [17]. Spermatogenesis occurs in the germinal epithelium and is controlled by hormones and other factors. Leydig and Sertoli cells are responsible for producing androgens and estrogens, which regulate the spermatogenesis process [2]. Therefore, an imbalance in the androgen or estrogen production can affect this process [2,17,30].
Our results revealed a hormonal imbalance in the serum and testis steroid hormone levels. It can clearly be noticed that the serum androgens (T and DHT) were significantly decreased, whereas the serum estrogens (E1SO4 and E2) were significantly increased in the experimental groups compared to the control. These results are in line with other authors that have observed that the consumption of isoflavones significantly reduces the androgen circulating levels in males, despite the developing period and time of consumption [7,9]. However, the effects of isoflavones on the circulating estrogen levels are controversial. Our study showed an increase in the estrogen levels in the animals exposed to the isoflavones; according to this, the preliminary studies developed in our laboratory also revealed an increase in these circulating estrogen levels after the administration of isoflavones during puberty [9], the same as other authors that have shown similar results [31,32]. Nevertheless, certain authors did not obtain any differences of the serum E2 concentrations after a short-term exposure to isoflavones, during three developing periods [7]. Despite this, isoflavones exert an effect on hormonal secretion that can trigger dysfunctions in the target organs of these hormones.
It is known that hormonal homeostasis shows periodic fluctuations in order to maintain an internal balance, and gonadal steroids are known to exhibit these circadian oscillations [33]. Interestingly, all the circulating hormones that were determined in this study showed different maximum concentrations at certain times. The A4, T, and E1SO4 serum concentrations revealed two maximum peaks of their concentrations around the 8th and 16th weeks of the experiment in the control animals, whereas hormones such as E2 or P4 showed only one maximum peak of concentration throughout the weeks of experiment. These results prove that gonadal steroids not only showed daily oscillations, but also monthly oscillations, which regulate the hormonal homeostasis. Beyond these results, the animals that were exposed to isoflavones at low or high concentrations exhibited the same A4 and E1SO4 maximum peaks of concentrations, but weeks earlier than the control group. It is demonstrated that low doses of isoflavones delay the onset of puberty in male rats [9,12]. In this preliminary study, the maximum peak of the circulating T that indicates the onset of puberty in male rats was shown 1 week before that in the rats fed with low doses of isoflavones, denoting that isoflavones alter gonadal secretion and exert an effect on development. In the present study, it is notable that the exposure to isoflavones accelerated the natural secretion hormonal oscillations that can cause an acceleration of developmental processes. Recent studies have shown an association between a decrease in the serum T levels and aging [34]. Considering that isoflavones decrease the serum T levels and accelerate the hormonal oscillations, it can be hypothesized that a long exposure to isoflavones from adulthood can accelerate the aging process in males, although further studies are needed. On the contrary, Al-shaikh, [34] revealed that the consumption of isoflavones during aging stages (elder rats) may protect against age changes in the testis. Therefore, the effects of isoflavones may depend on the developmental window in which they are consumed. In males, the production of sex steroid hormones occurs mainly in the testis, thus, the differences found in the secreted levels indicate that there must be alterations in the production of these hormones in the testis. These organs are sensitive to hormonal changes, and it is known that isoflavones are capable of reaching the reproductive organs [7,35], leading to alterations in sex hormone production and secretion. In this study, several changes in the testis hormone production have been found. The results showed a slight, but not significant, increase in the concentrations of sex steroid precursors (P4, DHEA, and A4) in the rats exposed to isoflavones, compared to the control. These precursors are present in Leydig cells and are involved in the production of the estrogens and androgens necessary for the spermatogenesis process [9,14,15]. Increasing the concentrations of the precursors might indicate that the sources of the active estrogens and/or androgens are deficient, thus, there is a need to obtain substrates to produce the sex steroid hormones and, therefore, maintain the hormonal homeostasis.
In this study, a deficiency in androgen synthesis was observed either in the T or DHT levels in the testis homogenates, which decrease in the experimental groups compared to the control. It has been demonstrated that, during developmental periods, isoflavones decrease the T testis concentrations, leading to disorders in reproductive function [9,36]. This decrease in the T testis levels can be related to the sperm quality deficiency found in these animals, since T is crucial for the spermatogenesis process. Zhu et al. [ 36] also observed that isoflavones affect the T synthesis in Leydig cells, which also leads to a dysfunction in the Sertoli cells. This decrease in the T levels of the rats fed with the isoflavones is due to the action of isoflavones on the testis that augment the E2 levels, as shown in the results. Indeed, the results showed that the E1SO4 testicular concentrations decrease significantly in the experimental animals compared to the control. E1SO4 is considered to be an inactive estrogen. The organism sulfoconjugates estrogens in order to inactivate them through the enzyme estrogen sulfotransferase. This enzyme is expressed in the testis and regulates the local exposure to estrogens, protecting the testis against excess estrogen concentrations [37,38]. Therefore, these results denoted that isoflavones influence the testis estrogen levels by preventing their inactivation and promoting an estrogenic testis microenvironment that is adverse for a correct spermatogenesis process.
These changes in the levels of the androgens and estrogens that were observed in the testis of the male rats treated with the isoflavones, caused a variation in the T/E2 ratio, which is essential for correct testicular function. In this study, a significant decrease in the T/E2 ratio was shown during the weeks of the experiment. The T/E2 ratio is widely used in order to provide information regarding the testis functionality, and has been proposed as a metric for determining sexual dysfunctions [39,40,41] or the risk of developing cerebrovascular diseases [42]. Therefore, this study determined that isoflavones cause a decrease in T/E2, which compromises the sperm quality and testis functionality in adult male rats.
## 5. Conclusions
This study demonstrates that the effect of isoflavones depends on the amount of isoflavone intake, in addition to the time of the intake. The results obtained on male rats administered with high doses of isoflavones show greater reductions in reproductive function than that in those administered with a regular intake of isoflavones (low doses). However, in research, the use of doses higher than the normal intake level is required when the lower doses do not provide optimal results, but their effects must be taken into account when translated to humans.
Altogether, these results suggest that continuous exposure to isoflavones in adult male rats causes a hormonal imbalance in the testes, maintaining an estrogenic environment that leads to a disruption in the production and secretion of androgens and estrogens. This alteration in hormonal homeostasis leads to histological changes, such as a reduction in the seminiferous tubules’ diameter and the germinal epithelium height. These changes result in defects in the spermatogenesis processes that will lead to a reduction in the sperm quality.
In conclusion, the long-term consumption of low and high doses of isoflavones in adult male rats compromises their testicular functionality, by causing an imbalance in hormonal homeostasis that leads to a reduction in the sperm quality.
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|
---
title: Association between Antibiotic Exposure and Type 2 Diabetes Mellitus in Middle-Aged
and Older Adults
authors:
- Lei Chu
- Deqi Su
- Hexing Wang
- Dilihumaer Aili
- Bahegu Yimingniyazi
- Qingwu Jiang
- Jianghong Dai
journal: Nutrients
year: 2023
pmcid: PMC10005743
doi: 10.3390/nu15051290
license: CC BY 4.0
---
# Association between Antibiotic Exposure and Type 2 Diabetes Mellitus in Middle-Aged and Older Adults
## Abstract
Background: *Although previous* studies have shown an association between clinically used antibiotics and type 2 diabetes, the relationship between antibiotic exposure from food and drinking water and type 2 diabetes in middle-aged and older adults is unclear. ObjectivE: This study was aimed at exploring the relationship between antibiotic exposures from different sources and type 2 diabetes in middle-aged and older people, through urinary antibiotic biomonitoring. MethodS: A total of 525 adults who were 45–75 years of age were recruited from Xinjiang in 2019. The total urinary concentrations of 18 antibiotics in five classes (tetracyclines, fluoroquinolones, macrolides, sulfonamides and chloramphenicol) commonly used in daily life were measured via isotope dilution ultraperformance liquid chromatography coupled with high-resolution quadrupole time-of-flight mass spectrometry. The antibiotics included four human antibiotics, four veterinary antibiotics and ten preferred veterinary antibiotics. The hazard quotient (HQ) of each antibiotic and the hazard index (HI) based on the mode of antibiotic use and effect endpoint classification were also calculated. Type 2 diabetes was defined on the basis of international levels. Results: The overall detection rate of the 18 antibiotics in middle-aged and older adults was $51.0\%$. The concentration, daily exposure dose, HQ, and HI were relatively high in participants with type 2 diabetes. After model adjustment for covariates, participants with HI > 1 for microbial effects (OR = 3.442, $95\%$CI: 1.423–8.327), HI > 1 for preferred veterinary antibiotic use (OR = 3.348, $95\%$CI: 1.386–8.083), HQ > 1 for norfloxacin (OR = 10.511, $96\%$CI: 1.571–70.344) and HQ > 1 for ciprofloxacin (OR = 6.565, $95\%$CI: 1.676–25.715) had a higher risk of developing type 2 diabetes mellitus. Conclusions: Certain antibiotic exposures, mainly those from sources associated with food and drinking water, generate health risks and are associated with type 2 diabetes in middle-aged and older adults. Because of this study’s cross-sectional design, additional prospective studies and experimental studies are needed to validate these findings.
## 1. Introduction
Antibiotics have been widely used for the treatment of bacterial infections in humans and animals and for the growth of animals since Alexander Fleming discovered that penicillin can be used to treat bacterial infections in 1928. Aureomycin was subsequently found to promote growth in animals in the 1940s [1,2,3]. The overuse of antibiotics in humans and particularly in animals, in which they are used in large quantities, has led to the presence of antibiotic residues in animals. A substantial proportion of antibiotics (30–$90\%$) is excreted in an unchanged form or as active metabolites through urine or feces in animals [4]. Our previous studies have shown that antibiotic residues in animals persist in processed foods (meat foods, livestock and poultry products, aquatic products, milk, etc.) and enter the human diet [5]. Moreover, antibiotic residues in excreted urine and feces, known as metabolite contaminants, can enter the environment (in surface water and sediment) and then human drinking water [6]. Existing studies have detected antibiotic residues in food and water environments [7].
The relationship between antibiotic exposure and type 2 diabetes mellitus (T2DM) has received increasing attention, given the adverse effects of excessive antibiotic use in humans and animals, and the similar effects of antibiotic exposure and use in middle-aged and older adults [8,9]. Some epidemiological evidence suggests that T2DM is associated with alterations in microbiota composition and function [10,11]. Specific antibiotics have been associated with perturbed glucose homeostasis in patients with T2DM [12]. However, these studies have examined the relationships between clinically administered antibiotics and T2DM, whereas the relationship between daily exposure to antibiotics from one’s diet or drinking water and T2DM remains unclear [13,14].
Many indicators are available for assessing antibiotic exposure, such as the concentration, detection rate, daily exposure dose (DED), hazard quotient (HQ) and hazard index (HI). The HI is an index for the quantitative and systematic evaluation of antibiotic exposure levels, which can provide a comprehensive indication of antibiotic exposure levels [6]. This study was conducted to assess the relationship between antibiotic exposure and the risk of T2DM in middle-aged and older people in Xinjiang, by testing urinary antibiotic exposure and then calculating the risk index through a biomonitoring method. Notably, unlike previous clinical studies based solely on questionnaire surveys, this study did not overlook the endogenous antibiotic exposure pathway.
## 2.1. Study Population
Participants were from the National Key Research and Development Program “Xinjiang Multi-Ethnic Natural Population Cohort Construction and Health Follow-Up Study,” which has been conducted by our group since 2018 [15]. In the 2019 baseline survey, subsamples of participants were recruited randomly from three townships in Huocheng County in the Yili region (Langgan Township, Sarbulak Township and Luchaogou Township) according to the main local ethnic groups. The inclusion criteria were as follows: middle-aged and older people who were 45–75 years of age; residents who had lived in the areas for more than 3 years; and those who had a non-acute onset state. The exclusion criteria were severe liver or kidney disease, or mental illness. A total of 659 middle-aged or older adults were enrolled in this study; 134 had incomplete questionnaires or lacked urine or blood glucose information. Thus, 525 middle-aged or older people were included in this study, including 145 Han, 136 Hui, 138 Uyghur and 106 Kazakh individuals. These comprised 345 middle-aged people and 180 older people, of whom 264 were men and 261 were women. All participants signed an informed consent form, and the study was approved by the Ethics Committee of the Xinjiang Uygur Autonomous Region Hospital of Traditional Chinese Medicine (2018XE0108).
## 2.2. Urine Testing and Medical Information Collection
For the field investigation, the study participants were instructed to fast before the physical examination. An amount of 12 mL of morning urine was collected from the participants, and the urine samples were stored in the dark and frozen in a −40 °C refrigerator on site after collection. Urine samples (1 mL) were purified with an Oasis HLB 96-well solid phase extraction plate and analyzed via ultraperformance liquid chromatography coupled with high-resolution quadrupole time-of-flight mass spectrometry with an isotopic internal standard in 1 mL of urine, hydrolyzed by β-glucuronidase. All antibiotics were analyzed with an HSST3 chromatography column for separation. The three phenols were separated with acetonitrile and an aqueous mobile phase in the negative ionization mode, and the other antibiotics were separated with methanol and an aqueous mobile phase with $0.1\%$ formic acid in the positive ionization mode. Each batch was based on 96-well solid phase extraction plates. A total of 96 samples were analyzed: 92 real urine samples, 2 solvent blank samples and 2 spiked urine samples of 10 ng/mL. The solvent blank samples and the urine samples spiked with the standard were analyzed together with the real urine samples. Solvent blanks were used to monitor background interference, and spiked urine samples were used to monitor precision and accuracy. The limits of detection and limits of quantification were defined as signal-to-noise ratios of 3 and 10, respectively. The limits of detection and limits of quantification for all antibiotics ranged from 0.04 to 1.31 ng/mL and from 0.13 to 4.37 ng/mL, respectively.
Physical examinations were performed by trained nurses or physicians. A 20 mL blood sample was collected for each participant with a vacuum blood collection device with an intravenous anticoagulant. Biochemical and routine blood tests were performed with 4 mL blood samples. These tests were performed at the township health center nearest to the survey site. Whole blood samples (3 mL) were transferred to three cryotubes immediately after blood sample collection. The blood samples used to separate plasma and leukocytes were centrifuged (4 °C, 3000 rpm, 10 min) within 2 h after blood sample collection.
The questionnaires were administered by medical students who had received professional training. The baseline questionnaire was mainly the China Kadoorie Biobank study baseline questionnaire, with minor modifications based on the comments of experts from Northwestern Medical College, China [16]. The questionnaire collected information on sociodemographic information; tea and coffee consumption; alcohol intake; smoking status; dietary status (the consumption frequency of pork, beef, mutton, fried food, vegetables and fruits was referred to in the food frequency method questionnaire) [17]; passive smoking and indoor air pollution; personal and family medical history; physical activity; mental health; and female reproductive history.
## 2.3. Antibiotic Health Risk Assessment
The daily exposure dose (DED) is an indicator used to assess the daily antibiotic exposure of an organism according to the following formula for the DED of an environmental contaminant [18,19]: DED=CsVMbP (in μg/kg/day), where *Cs is* the antibiotic concentration in μg/L; V is the daily urine volume in L/day (daily urine volume was set at 1.70 L/day and 1.60 L/day for men and women, respectively); *Mb is* the body weight in kg; and P is the proportion of antibiotic excreted in urine, from the human pharmacokinetic data in an unchanged form and in a glucuronide bound form.
The acceptable daily intake (ADI) is an indicator of exposure applicable to long-term low doses of antibiotics, rather than the short-term high doses used in clinical settings. The current common standard for the ADI, established by the World Health Organization and the Food and Agriculture Organization of the United Nations in 1957, aims to establish a safe limit value to minimize the hazards threatening population health, which in turn is used to assess the risk of exposure [20]. We quantified the ADI of antibiotics by referring to the international ADI standard. With the exception of the three sulfonamides, the ADIs were established on the basis of the microbiological effects of common bacteria in the human gut microbiota. The ADIs for the three sulfonamides were established on the basis of toxicological effects.
The HQ is used to assess the health risk of each antibiotic and is defined as the ratio of daily exposure, i.e., the ratio of DED to the daily available dose ADI [21]: HQ = DEDADI; HI = ∑HQ.
The HI is used to assess the cumulative health risk of combined antibiotic exposures and is based on a dose-additive concept of similar effects induced by multiple antibiotics, defined as the sum of HQs with similar effect endpoints. It is a new index for the quantitative and systematic evaluation of antibiotic exposure levels that provides a comprehensive indication of the level of antibiotic exposure. The HI is currently applied in evaluating the health risk of the long-term low-dose cumulative intake of antibiotics in humans. Because adults can be exposed to multiple antibiotics simultaneously, the HI was determined for 3 sulfonamides according to toxicological effects, 11 antibiotics according to microbial effects, 4 antibiotics for veterinary use and 10 antibiotics for preferred veterinary use, on the basis of different effect endpoints. An HQ or HI ≥1 indicates the presence of a potential health risk.
## 2.4. Statistical Analysis
The 18 antibiotics were grouped according to their antimicrobial mechanism or use, and new variables were generated via the summation of antibiotic mass concentrations in urine. The three new variables were human antibiotics (HAs), veterinary antibiotics (VAs) and preferred veterinary antibiotics (PVAs). The five new variables for antimicrobial mechanisms were tetracyclines, fluoroquinolones, macrolides, sulfonamides and phenicols. The same categories of HI were summed to generate new variables, which were divided into HI for microbiological effects, HI for toxicological effects, HI for veterinary use and HI for preferred veterinary use. Descriptive analysis provided the frequency of antibiotic testing or percentage of selected concentrations (P95, P99). The rank-sum test was used to analyze differences in blood glucose or antibiotic concentrations and other demographic variables, such as sex, age, education, income level, physical activity, diet, smoking and alcohol consumption.
The HI was classified into two groups according to hazard to human health: the HI ≤ 1 group and the HI > 1 group. Participants with fasting blood glucose values ≥7.0 mmol/L and self-reported T2DM were classified as the T2DM group, and those with fasting blood glucose values <7.0 mmol/L were classified as the non-T2DM group [22]. Variables were included in Model A as covariates if the p-value for the demographic variable of the total glucose or total antibiotic concentration derived from the t-test, ANOVA or rank test (Kruskal–Wallis test) was <0.05. Model A included the following covariates: age and income level. If the p value of the demographic variables of the total blood glucose or total antibiotic concentration obtained from the t-test, ANOVA or rank test (Kruskal–Wallis test) was <0.2, these variables were included in Model B as covariates. Model B included the variables from Model A as covariates plus sex, education, frequency of eating pork, frequency of eating mutton, frequency of eating fried food, vegetable consumption and smoking. The covariates associated with the relationship between antibiotic use and T2DM that had been studied in previous clinical settings were included in Model C. Model C included the variables in Model B as covariates plus physical activity, frequency of eating beef, fruit consumption and drinking (i.e., the covariates adjusted for in Model C were the risk factors for type 2 diabetes and all the demographic characteristics we focused on). Finally, the three models were statistically analyzed with binary logistic regression models to assess the relationship between HI and T2DM. All statistical analyses were performed in the statistical software SPSS 26. p-values <0.05 were considered statistically significant.
## 3. Results
All 18 antibiotics were detected in urine, with an overall detection rate of $51.0\%$ and detection rates of individual antibiotics ranging from $0.2\%$ to $20.0\%$. Urinary antibiotic concentrations ranged from below the limit of detection to above 1000 ng/mL (Table 1). Two or more antibiotics were detected in urine in $20.9\%$ of individuals. Almost all non-T2DM populations had lower concentrations of antibiotics at the 95th percentile and 99th percentile than did the T2DM populations, except for tetracycline, norfloxacin, ofloxacin, thiamphenicol and sulfamethoxazole. The composition ratios of antibiotic detection rates were also lower in almost all non-T2DM populations than they were in the T2DM populations, except for chlortetracycline, doxycycline, clarithromycin, sulfadiazine and sulfamethoxazole.
Daily doses of antibiotic exposure were mostly lower in the 95th and 99th percentiles of the non-T2DM population than they were in the T2DM population, except for tetracycline, ofloxacin, sulfamethoxazole and florfenicol (Table 2).
Among 525 middle-aged and older people in Xinjiang, 40 individuals ($7.7\%$) had an HI value ≥ 1 and a health risk. The values of the HQ were mostly higher for the T2DM than they were in the non-T2DM population, except for individual antibiotics such as tetracycline, norfloxacin, ofloxacin, florfenicol and sulfamethoxazole. Notably, the 99th percentile HQ values of oxytetracycline and norfloxacin were relatively high in the T2DM population, at 26.448 and 62.427, respectively, thus indicating that a high oxytetracycline and norfloxacin intake was associated with a high risk of susceptibility to human health effects (Table 3).
Table 4 shows that, in older adults who were 60–74 years of age, compared with middle-aged adults aged 45–59 years, blood glucose values and total antibiotic exposure concentrations increased with age. This trend was also seen with an increasing frequency of vegetable consumption; participants who ate vegetables daily had higher blood glucose values and total antibiotic exposure concentrations than did those who did not eat vegetables daily. Moreover, occasional pork eaters had higher blood glucose values but lower total antibiotic concentrations than did those who did not eat pork, and occasional alcohol drinkers had higher blood glucose values but lower total antibiotic concentrations than did non-drinkers.
The binary logistic regression results indicate that, after adjustment for covariates in Model A, the HI for microbiological effects, HI for PVAs, HQ for norfloxacin and HQ for ciprofloxacin were associated with a higher risk of T2DM in participants with higher exposure risk (HI > 1 or HQ > 1), and this association persisted after adjustment for more covariates in Models B and C (Table 5). Specifically, the risk of T2DM in the group with a microbiological effect HI > 1 was 2.948 times greater than the risk of T2DM in the group with microbiological effect HI ≤ 1 after adjustment for covariates in Model A, with a $95\%$ CI and an OR of 2.948 (1.287–6.756). The confidence intervals were 3.391 (1.417–8.120) and 3.442 (1.423–8.327) after adjustment for covariates in Model B and Model C, respectively. After adjustment for covariates in Model A, the risk of T2DM was 2.928 times higher in those with an HI > 1 for PVAs than it was in the group with an HI ≤ 1 for PVAs, with a confidence interval of 2.928 (1.279–6.706). After adjustment for covariates in Models B and C, the confidence intervals were 3.271 (1.371–7.802) and 3.348 (1.386–8.083). After adjustment for covariates in Model A, those in the HQ > 1 group for norfloxacin had 10.075 times the T2DM risk of those in the HQ ≤ 1 group for norfloxacin, with a credible interval of 10.075 (1.612–62.952), and they still had a higher risk after adjustment for covariates in Models B and C, with credible intervals of 11.243 (1.728–73.142) and 10.511 (1.571–70.344). The risk of T2DM was 4.789 times greater in the ciprofloxacin HQ > 1 group than it was in the ciprofloxacin HQ ≤ 1 group after adjustment for covariates in Model A, with a confidence interval of 4.789 (1.336–17.175), and also after adjustment for covariates in Model B and Model C, with confidence intervals of 5.241 (1.377–19.946) and 6.565 (1.676–25.715).
## 4. Discussion
In this study, we measured the concentrations of 18 common antibiotics in urine and then calculated the DED, HQ and HI, thus indicating the relationship between T2DM and antibiotic risk in middle-aged and older people. The HI for microbiological effects >1, HI for PVA use >1, HQ for norfloxacin >1 and HQ for ciprofloxacin >1 in middle-aged and older people were positively associated with T2DM. These associations persisted after adjustment for several confounding factors known to be associated with T2DM. To our knowledge, this study is the first to use urinary antibiotic biomonitoring to explore different sources of antibiotic exposure, and to assess the health risks and consequent effects on T2DM.
Studies based on the biomonitoring of urinary antibiotic concentrations are lacking. Several studies have been performed in China and Korea [23,24,25,26,27]. Few studies have evaluated the HQ and HI of urinary antibiotics, all of which have been in China (Figure 1) [6,20,28,29,30,31]. Figure 1 shows the comparison of HQs in this study and six previous studies. The HQ levels in Xinjiang middle-aged and older adults were generally exceeded for 11 antibiotics, thus indicating a health risk, particularly for tetracycline ($2.3\%$) and ciprofloxacin ($2.1\%$). Throughout the western and eastern regions of China, the HI for ciprofloxacin was above the human health level threshold in all six studies. The detection rate of a ciprofloxacin HQ > 1 in middle-aged and older adults in Xinjiang was lower than that in several other regions ($2.1\%$). Shanghai school-age children had the highest ($8.07\%$), and the levels of ciprofloxacin HQ > 1 were comparable in Shanghai children and adults, both at $5.6\%$, and were higher than those in pregnant women in Eastern China ($3.7\%$) and Anhui ($3.8\%$). However, the levels of human antibiotic health risk in each of these regions varied, likely because of differences in locations, lifestyle habits and dietary and drinking habits among regions [32,33,34].
All 17 antibiotics have short half-lives of <15 h, except for azithromycin, which has a half-life of approximately 40 h [35]. Previous studies have hypothesized that exposure levels to antibiotics based on a single sampling test may represent previous long-term exposure levels, because antibiotic exposure depends on antibiotic use, and human exposure to antibiotics in daily life occurs primarily through contaminated food and drinking water (HAs, VAs and PVAs). Therefore, the concentration of urinary antibiotics in a single sample should reflect long-term antibiotic exposure to some extent, but not the intensity of antibiotic use or health risk [23]. This hypothesis is supported by our findings, which suggest that people with T2DM have higher biomonitoring urinary antibiotic concentrations, detection rates, daily exposure doses and HQ levels than do people without T2DM, and that people with diabetes have relatively higher HQ values for oxytetracycline and norfloxacin and have a higher health risk. Because the gut microbiome differed between the T2DM population and non-T2DM population, and the gut microbiome components are closely associated with glucose metabolism (e.g., insulin secretion, insulin sensitivity, etc.), the levels of antibiotic exposure should differ between these groups [36,37,38,39,40]. Because oxytetracycline and norfloxacin are common therapeutic agents in diabetes treatment, high and prolonged doses of oxytetracycline or norfloxacin may cause health risks in patients with diabetes [41].
A growing body of evidence from experimental studies suggests a causal relationship between the gut microbiota and diabetes [42]. Age factors, particularly aging, have been shown to be predisposing factors for abnormal glucose metabolism and abnormal regulation [43]. The diversity of the gut microbiota is diminished and is less stable in adults who are older than middle-age [44]. Antibiotics have multiple effects on host physiology, particularly on glucose homeostasis, by disrupting the intestinal microbiota. These effects are consistent with our findings, indicating an increase in antibiotics and glucose with age in people who are older than middle-age. In contrast, different antibiotic exposures arising from different diets, and the recognition of short-chain fatty acids and secondary bile acids by enteroendocrine cells, the vagus nerve and enteric neurons, among other aspects, result in different effects on glucose homeostasis [45,46]. Eating vegetables and pork and drinking alcohol with different frequencies in our study were associated with different trends in blood glucose levels and antibiotic concentrations. In this study, we observed higher blood glucose values in people who ate pork occasionally than those in people who did not eat pork; in people who ate vegetables daily than those in people who did not eat vegetables daily; and in people who drank alcohol occasionally than those in people who did not drink alcohol. Because pork consumption is a known risk factor for developing T2DM [47,48], and pigs are raised with antibiotics that promote growth and sterilization [49], people with T2DM are more likely to be tested for antibiotic exposure and to experience health risks from antibiotic exposure. A low intake of vegetables increases the risk of developing T2DM [50]. Vegetables are grown with added antibiotics [51], thus potentially increasing antibiotic exposure and risk in people with T2DM. Similarly, alcohol consumption is strongly associated with the development of T2DM, and excessive alcohol consumption can even trigger the risk of cancer in people with T2DM [52,53]. In contrast, alcohol production is prone to contamination with phytotoxins and low pH contaminants, such as antibiotics [54]. Therefore, antibiotics are often detected in patients with T2DM.
Although clinical studies have reported a relationship between antibiotics and T2DM [8,9,12,13,14,55,56], no studies have assessed the association between antibiotic exposure in daily life and the risk of T2DM. Previous findings from a clinical perspective are consistent with the results of this study, in that people who used antibiotics for longer periods of time had a higher risk of developing T2DM than did people who did not use antibiotics; moreover, those who used multiple antibiotics had a higher risk of developing T2DM than did those who used a single antibiotic. A significant dose-dependent relationship has been reported between antibiotic exposure and the incidence of T2DM [8,9,13]. Antibiotic exposure significantly increases the risk of T2DM in people above 50 years of age [14]. Moreover, fluoroquinolones are associated with an increased risk of diabetes [8]. However, results have varied among studies; for example, several studies have reported that single antibiotic use is not associated with the risk of developing T2DM [8,9]. Participants receiving more antibiotics had no increased risk of diabetes [57]. The reason for the discrepancy in the results may be due to differences in the doses and frequencies of medication and the variety of medications used for T2DM [58,59]. HI > 1 for microbiological effects in older people, HI > 1 for preferred veterinary use, HQ > 1 for norfloxacin (a PVA) and HQ > 1 for ciprofloxacin (a PVA) were significantly associated with T2DM and were risk factors for T2DM. These findings suggest that the cumulative health risk triggered by long-term exposure to low doses of antibiotics (contaminated food or drinking water) in daily life can affect T2DM development in middle-aged and older people, and that PVAs should be used in a regulated manner.
This study performed biomonitoring to assess the health risks of middle-aged and older people in Xinjiang. However, it has several limitations. First, this study was a cross-sectional study and thus could not demonstrate a causal relationship between antibiotics and T2DM. Antibiotic exposure might potentially be a proxy for some conditions associated with T2DM. Participants with T2DM might have had different levels of exposure to antibiotics in food or drinking water, and thus different levels of assessed health risks, depending on their dietary structure and intake. Second, because people are intermittently exposed to antibiotics in their daily lives, urinary concentrations in people may vary throughout the day. Antibiotics have short half-lives, and we collected morning urine from participants, thus potentially underestimating the exposure and the health risk assessment after multiple exposures to antibiotics. Single urine samples may not reflect the levels of long-term antibiotic exposure, thus potentially weakening the association between the risk of antibiotic exposure and T2DM in middle-aged and older adults.
## 5. Conclusions
We used a biomonitoring approach to assess antibiotic exposure and thus the risk of antibiotic exposure, and we found that exposure to antibiotics in daily life (mainly from food or drinking water contaminated with antibiotics), particularly with an HI > 1 for microbiological effects, HI > 1 for PVAs, HQ > 1 for norfloxacin and HQ > 1 for ciprofloxacin, may be risk factors associated with T2DM in middle-aged and older people. Because cross-sectional studies and primary urine sampling reflect long-term antibiotic exposure, more prospective studies and experimental studies are needed to further explore these associations and their underlying mechanisms.
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|
---
title: Pilot Study on Satisfaction in Children and Adolescents after a Comprehensive
Educational Program on Healthy Habits
authors:
- Noelia Belando-Pedreño
- Marta Eulalia Blanco-García
- José L. Chamorro
- Carlos García-Martí
journal: Nutrients
year: 2023
pmcid: PMC10005745
doi: 10.3390/nu15051161
license: CC BY 4.0
---
# Pilot Study on Satisfaction in Children and Adolescents after a Comprehensive Educational Program on Healthy Habits
## Abstract
Prospective research in the area of Education Sciences and Physical-Sports Education agree on the need to design and implement educational programs that promote emotional competencies (ECs), interpersonal competencies (ICs), an adequate level of healthy physical activity (NAFS) and a good adherence to the Mediterranean diet (ADM). The main objective of the study is to design an intervention program in intra- and interpersonal competencies together with nutritional education and corporality called “MotivACTION”. The sample consisted of 80 primary schoolchildren aged 8 to 14 years ($M = 12.70$; SD = 2.76) (37 girls and 43 boys) from two schools in the Community of Madrid. An ad-hoc questionnaire was created to assess the participant’s perception of the usefulness of the “MotivACTION” educational experience. The program “MotivACTION: Feed your SuperACTION” is designed and implemented based on the development of a workshop organized through the Universidad Europea de Madrid. As the main preliminary results of the pilot study, the schoolchildren who experienced the “MotivACTION” workshop showed high satisfaction with the educational program. They were able to create a healthy menu with the frog chef. They also felt better and happier at the end of it, and they enjoyed practicing physical activity moving to the rhythm of the music while doing mathematical calculations.
## 1. Introduction
Promoting healthy habits in children and adolescents (e.g., nutrition, physical activity, mental health, etc.,) should be a priority for institutions, researchers, and education professionals. Educational programs will have a greater or lesser impact based on participant satisfaction. Furthermore, creating educational programs based on motivational principles and offering emotional tools to cope with healthy habits changes could increase satisfaction and, thus, change behaviors. This study presents the evaluation of the “MotivACTION”, an educational program based on motivational principles and aimed at children and adolescents, founded in emotional education, personal and social values education, nutritional education, and promoting more active lifestyle habits through the proper holistic development of the young.
## 1.1. Theoretical Background
The design of the “MotivACTION program” is based on the postulates of various theories that explain human behavior such as the Self-Determination Theory (SDT) [1,2], the Theory of Planned Behavior (TPB) [3], and the Theory of Emotional Intelligence (TEI) [4]. These theoretical paradigms provide keys to incorporate motivational processes in a learning process where students go beyond memorizing knowledge and learn to develop skills such as “knowing how to be” and “knowing how to do” [5].
SDT [1,2] establishes different motivation types according to whether their origin is internal or external to the individual. From the perspective of SDT, students can experience different types of motivation depending on the level of self-determination (intrinsic motivation, extrinsic motivation and amotivation). In addition, this theory posits that people become more committed to a particular activity if their needs for autonomy, competence, and social relatedness are satisfied. When the pedagogical designs implemented by teachers/educators/facilitators satisfy these basic psychological needs, it is more likely that students will have a more self-determined degree of motivation to participate in learning tasks [6,7] and exhibit more active behaviors at the physical level [8] such as personal care and better levels of daily physical activity and, at the educational level, more proactive and social behaviors toward learning [6,9]. In this sense, the “MotivACTION program” consists of activities organized in small and large groups with a guided discovery methodology, which encourages more self-determined motivation and satisfaction of the basic psychological needs to promote the commitment of the participants.
Furthermore, according to the TPB [3], the intention to do something (to have a certain behavior) is, in turn, influenced by three basic determinants: two of a personal nature (such as motivation) and another that reflects social influence. One of them is the attitude toward the behavior, that is, the positive or negative evaluation a person makes just before “taking action” (manifesting the behavior). The second of these, of a social nature, refers to the subjective norm, that is, the person’s perception of the pressures from peers or other people in the context (negative comments, or positive feedback) that are exerted on her to perform the expected behavior. And the third is perceived behavioral control, that is, to what extent the person feels in control of executing the behavior. Perceived control can directly predict behavior depending on whether it is under voluntary control and whether there are discrepancies between the control the person believes they have and the control they have. So, if the students/participants perceive themselves with the real and necessary ability to act and also feel motivated toward it, they could act. Therefore, from the “MotivACTION program”, situations are worked on in which participants have to put in their effort, that is, to show a proactive attitude toward the desired behavior. In addition, they are encouraged with positive, prescriptive, and interrogative feedback (subjective norm) and have to face challenges/tasks which they feel they can do and enjoy while resolving the situation (perception of behavior control). In this sense, the performance of physical exercise as a healthy behavior integrated into a learning context contributes to the perception of control [10] and better predicts behavior, compared to attitude and subjective norm, the latter being the one that predicts the worst [10].
Moreover, according to Goleman [4], emotional intelligence is understood as the set of socio-emotional competencies related to success in work or any area of personal development. García-Fernández and Giménez-Más [11] proposed a model of emotional intelligence that encompasses the study of both internal or endogenous aspects (particular traits of the individual, which can be innate or acquired through learning or knowledge) and external or exogenous aspects (which are behaviors based on adaptability to the environment). In MotivACTION, practices focus on endogenous factors such as responsibility (capacity to understand the consequences of actions in the future), common sense, and the ability to learn and even unlearn, which does not help us progress toward personal and social goals such as adopting healthy habits. The most encouraged exogenous factors are the ability to adapt to a changing environment (very important in the last two years of the COVID-19 pandemic), empathy (predisposition to understand what others are going through and support them in that process), and the ability to communicate assertively (using kind and positive language toward ourselves and others). These are all strategies that help to face the challenges of adopting healthy habits.
## 1.2. Nutrition Education as a Strategy for Promoting Healthy Habits in Children
Nutrition education (NE) refers to all educational activities that involve students and the entire educational community (socializing agents: teachers, educational institutions, families) through direct education in the classroom and the transfer of this education to the context of personal, family, and social development. The aim is to motivate students to adopt healthy eating behavior and lifestyle choices in general. Thus, the acquisition of integral health and the promotion of healthy habits in educational environments has become one of the most important challenges, mainly due to the fact that childhood obesity has become an epidemic pathology worldwide [12]. In 2021, the WHO [13] reported alarming figures for overweight and obesity in young children: 39 million children under 5 years of age were overweight or obese in 2020; more than 340 million children and adolescents aged 5–19 years were overweight or obese in 2019. To reduce these figures and the future consequences on physical health (development of chronic non-communicable diseases such as obesity, diabetes, hypercholesterolemia), emotional health, and the quality of social relations of schoolchildren and adolescents, educational intervention strategies are needed at the government level aimed at promoting healthy habits through nutritional education and an active lifestyle [14,15]. In this sense, there are various strategies in the field of nutritional health promoted by world bodies such as the WHO [2004] [16] with the Global Strategy on Diet, Physical Activity and Health, with objectives such as: Reducing the risk factors for chronic diseases derived from unhealthy diets and sedentary lifestyles through public health actions; increasing awareness and understanding of the influences of diet and physical activity on health and the positive impact of preventive interventions. In the same line of action, the NAOS Strategy (Nutrition, Physical Activity and Obesity Prevention) of the Ministry of Consumer Affairs (Spanish government) aims to reduce the prevalence of obesity by promoting healthy eating and physical activity. There are other relevant initiatives in Spain, coordinated by the Ministry of Consumer Affairs and AESAN (acronym in Spanish for the Spanish Agency of Food Safety and Nutrition) through the “observatory of nutrition and the study of obesity”, which propose lines of work to promote healthy and sustainable eating in schools and other public centers (i.e., campaign Put more heroes on your plate, and fill your life with superpowers, 2022 [17]).
In terms of NE research, several literature review studies and meta-analyses show the positive effects of multicomponent nutrition education programs (eating behavior strategies, motivational strategies, meal planning strategies, etc.,) [ 18,19,20,21]. It seems that the most successful intervention methodologies include multiple strategies and an “active” approach (methodologies capable of producing effective changes in eating habits and not only disseminating nutritional information) [22]. Furthermore, most of the intervention programs carried out in recent years to counteract childhood obesity have been those carried out in schoolchildren aged 6–12 years, which addressed practical contents such as: (a) Focusing actions on eating behavior, rather than a knowledge-based approach alone; (b) focusing on individual and environmental behaviors related to diet and physical activity level; (c) educational and practical component programs (video lessons or video labs, implementation of content through meaningful activities such as gamified challenges or cooking workshops) [19,23]; (d) didactic content promoting healthy nutrition, physical activity, guiding parents on how to do to improve their relationship with their children [24], among others.
“MotivACTION” is an innovative educational approach that integrates knowledge and practical activities on emotional education, physical education, and education in proper eating behavior. Healthy eating and regular physical exercise are highlighted as necessary actions to maintain or improve well-being and, in turn, promote human health [25,26]. From a nutritional perspective, a healthy diet provides all the types and amounts of foods and nutrients that the body needs to function properly and prevent the development of diseases due to nutritional excess or deficiency. For this to occur, several factors are necessary, including: (a) optimal food security, i.e., that food (in quantity and variety) is available and affordable in the community; (b) that available and affordable food is safe and free from contaminants and substances that can harm the body once ingested [27]; (c) that the individual consumes an adequate variety and quantity of foods, tailored to their nutritional needs; (d) that the process of nutrition (the involuntary process by which nutrients are digested and absorbed) is functioning properly [26]. All these factors influence, in one way or another, preventing or combating the two extremes of malnutrition that coexist today. In this sense, one of the main objectives of “MotivACTION” is for children and adolescents to identify which emotions and day-to-day situations influence their behavior and how this impacts their daily physical activity level and eating behavior in and out of the classroom.
## 1.3. Aim
The main objective of this study is to test the satisfaction of children and adolescents with the design and implementation of a “pilot” educational program called “MotivACTION, feed your superACTION” as an integrative training strategy for proper emotional education, nutritional education, and promotion of active lifestyle habits in children and adolescents.
## 1.4. Hypothesis
After the acute implementation of the MotivACTION educational program, participants will show a high level of satisfaction and achievement. Furthermore, it is hypothesized that the participants will perceive the importance of transferring the learning acquired in the program to everyday life (classroom situations, family life).
## 2.1. Design
The research design corresponds to a protocol or methodological development design of an educational program. A pilot study of an Action Research type (research is conducted at the same time as intervention) was carried out in an educational and social context [28] for the implementation of the program.
## 2.2. Participants
The sample consisted of 80 primary and secondary school students aged 8 to 14 years old ($M = 12.70$; SD = 2.76) (37 girls and 43 boys) from two educational centers and teenagers attending an academic support center in the Community of Madrid (Table 1).
Among the criteria for the selection of participants, the variables of age, sex, educational stage, and socio-economic level. The sampling technique applied was non-probabilistic by convenience [29]. The educational centers, parents/guardians of the students and teenagers gave approval for the minors’ participation in the scientific-technical activity and informed consents were signed.
This study has been designed and implemented considering all the bioethical principles established by the Belmont Report [30] and the Declaration of Helsinki [31]: principles of autonomy, beneficence, justice, and non-maleficence.
## 2.3. Instruments
An ad-hoc questionnaire was created to evaluate the participant’s perception of the educational experience “MotivACTION, feed your superACTION”. This instrument combines the collection of quantitative data through 9 Likert-type questions (e.g., How much do you feel that it has helped you become more aware of the importance of your thoughts and emotions?) rated from 0 (worst or least liked) to 10 (best or most liked). In addition, it included an open-ended question, number 10, to find out participants’ perceptions of the transferability of the workshop in different contexts of their lives: What have you learned in the workshop that you find useful in your daily life in school/institute, in your relationship with your classmates, and in your relationship with your family?
This questionnaire was created to evaluate different participants’ perceptions of the educational program. Thus, the questionnaire is made up of a set of single items that refers to different dimensions or unifactorial constructs. According to Angulo-Brunet et al. [ 2020] [32], it is not suitable to test the psychometric properties of single-item measures through measurement models and internal consistency reliability coefficients. Instead, they propose to test the evidence of the validity of the single-item measures through item content validity and the response process. In our study, evidence on item content validity was tested by a panel of two experts that evaluated if the contents of every single item reflect what the researchers wanted to measure. Validity evidence related to the response process was first obtained through a cognitive interview in the development phase and second, relying on participants’ comments during data collection [32].
To recruit the sample, the schools were contacted by telephone, the head of studies was contacted, and the objective of the action research was communicated to them as part of a scientific transfer and dissemination activity organized by the Universidad Europea de Madrid. Once the approval of the heads of the schools was obtained, a meeting was organized with the teachers in order to count on their collaboration in the development of the “MotivACTION, feed your superACTION” workshop. Likewise, the relatives of the participants were informed of the objective of the study and the type of tests (questionnaire) to be implemented for data collection, and the anonymity of the responses was preserved.
## 2.4. Procedure
The educational program “MotivACTION, feed your superACTION” was carried out under the workshop format of the same name on the occasion of the “International Day of Women and Girls in Science” and as part of other scientific dissemination activities in the Community of Madrid linked to the “European Project Madrid+d”. These dissemination events have been organized and approved by the Universidad Europea de Madrid for the $\frac{2021}{2022}$ academic year. The program was coordinated by a maximum of two university teachers with doctorates in Physical Activity and Sports Sciences and Biology, respectively, with knowledge in Health Sciences (emotional education, motor behavior and nutritional education). In the case of workshops for small groups (6–8 participants), they would be facilitated by one teacher.
## 2.5. Design and Application of the Educational Program
The educational program consisted of two educational workshops called “MotivACTION, feed your superACTION” structured along eight thematic lines (Table 2) related to emotional skills work, awareness of cognitions (thoughts), nutritional education, and physical education (exercise techniques) related to other curricular subjects. The maximum duration of each workshop was 1 h and 30 min.
The “MotivACTION” program was designed based on the scientific evidence on the psychological construct “intrinsic motivation” that is analyzed and developed in Reeve [33] (see chapter 5, page 83), as well as the internal and external motives that determine a person’s behavior. The “MotivaACTION” program is also based on the postulates of various theories that explain human behavior: SDT (it was applied in activities of recognition of different motivational states that students have in their daily lives, or in activities, whose objective was to know the motives of sports practice); TPB (it was carried out in physical activities developed during the workshop in which participants showed a proactive attitude towards behavior, in which they were incentivized through positive, prescriptive and interrogative feedback (subjective norm) and faced physical challenges in which they had to make decisions among the group of peers (perception of behavioral control). The postulates of emotional intelligence (Theory Emotional Intelligence, TEI) were used to work on Dr. Hitzig’s emotional literacy (reflection and debate on what kind of emotions and associated attitudes occur in different situations in the classroom, at playtime, in the canteen, in sports practice with friends, at home in the relationship with the family, among others).
The nutritional part of “MOTIVACTION” is based on the recommendations of the FAO [34] and the Spanish Food Safety and Nutrition Agency (AESAN) [17], with a didactic, fun (using humor to promote positive emotions) role-playing situations, group reflections practical approach to daily life. The topics that the didactic workshop on nutrition covers are: (a) Integrating and enjoying a wide variety of foods and dividing consumption into five to six small meals/day; (b) how to make a nutritious and simple breakfast every day; (c) what types of cereals exist, how and when to consume them; (d) what micronutrients are and how to get them through daily portions of fruits and vegetables; (e) healthy and “attractive” alternatives to “fast food” to reduce high-fat and added-sugar foods; (f) who “lives” in the gut walls: the microbiota and its care; (g) why and when to hydrate throughout the day and when performing a certain amount and intensity of physical activity; (h) reviewing the importance of body composition beyond total weight and what daily actions to take to stay active.
## 2.6. Data Analysis
A descriptive analysis (mean and standard deviation) was performed on the Likert-type responses from 1 to 9 of the ad-hoc questionnaire. Regarding qualitative analysis, a content analysis was carried out through the transcription of the responses given by each participant to question number 10. Excel software in Excel Book (.xlsx) format for Mac was used for the descriptive analysis.
Regarding qualitative analysis, to find common themes and patterns in the responses to item 10, a content analysis was performed with a deductive/inductive approach [35]. Then, the cites that best reflected the personal experiences of the participants in relation to the research objective were identified and coded, creating different categories with representative meanings [36]. For greater reliability, an internal consistency analysis was carried out in which the four authors participated in order to increase their precision standard and corroborate the consistency of the results [35].
## 3.1. Quantitative Results
Table 3 shows the mean and standard deviation of all items of the questionnaire. The findings show that most items in the questionnaire received high mean scores from the participants. It is important to note that participants found the program useful to improve their way of thinking, their emotions, and their fitness level. The items most highly rated by the participants (M > 9.00) were: item [8] “Rate the activity in terms of organization”; item [7] “Rate the activity in terms of format”; item [3] “How much do you feel that becoming more aware of the importance of your thoughts and emotions has contributed to you?” Item [1] “How useful is it for you to work on your way of thinking, your emotions, and your fitness level?” The commitment to continue participating in the program and to recommend to other peers had also high punctuation. Overall, the general satisfaction with the program was 8.76.
## 3.2. Qualitative Results
The qualitative results show the perception about the educational program “MotivACTION, feed your superACTION”. These results refer to the students’ answers to the semi-open question: What did you learn in the workshop that is useful for your day-to-day life at school, in your relationship with your classmates, and in your relationship with your family? ( Item 10 ad-hod questionnaire). Three categories for each of the contexts emerged from the content analyses (Table 4). In the first context (day-to-day life at school), the educational program influenced the attention of the participants to what the teacher is saying, to pass a better time in class and to express what they feel in stressful situations. In the second context (relationships with their families), the participants improve their skills to tell their parents when they feel bad, make healthier plates for lunch and dinner and being more active with their family. In the first context (relationships with their classmates), the educational program improves the quality of time that they spend with their classmates and to be more active with them.
## 4. Discussion
This study aimed to test the satisfaction of children and adolescents with the design and implementation of a “pilot” educational program called “MotivACTION, feed your superACTION” as an integrative training strategy for proper emotional education, nutritional education, and promotion of active lifestyle habits in children and adolescents. Eating practices and young students’ perception of healthy lifestyle habits (eating, physical exercise, satisfactory social relations, perceived well-being, etc.,) seem to be determined also by external factors, such as the food, cultural, school, and social environment and socio-economic status [37,38,39]. In this sense, the quantitative results of the ad-hod questionnaire showed the importance given by schoolchildren to the organizational aspect, to the format in which the information is presented in the “MotivACTION” workshop (activities that apply to daily life, with group dynamics, using humor, peer interaction, music as an element to motivate the rhythmic movement of the different body segments, etc.,). In the same line of study, a recent study based on different nutritional strategies developed in Indonesian adolescents [40] showed consistent results on food awareness and healthier lifestyles when programs with an appropriate organizational structure, facilitated by teachers/school staff, focused on behavior change related to healthy nutrition and physical activity, are carried out as part of a package of interventions for the improvement of the overall health of young students [41,42].
Concerning the items being more aware of the importance of your thoughts and emotions and how useful they think it is for them to work on their thinking, emotions, and physical fitness (3 and 1, respectively), there is a fundamental aspect to pay special attention to in the school and adolescent population: optimal mental health understood as a state of well-being in which a person is aware of his or her capabilities, being able to cope with normal day-to-day stresses, carrying out academic tasks productively, and being able to participate in his or her community (e.g., in the educational environment) in an active manner [43]. In this regard, the WHO adopted the Comprehensive Mental Health Action Plan 2013–2020 in May 2013 [44], establishing as one of its objectives the prioritization of child and adolescent mental health through policies and laws to protect children and adolescents, supporting parents or legal guardians to provide loving care, implementing school-based programs, and improving the quality of community and online environments. WHO [2022] [44] states that school-based social and emotional learning programs are among the most effective advocacy strategies for countries at all income levels. However, Severe Mental Disorders (SMD), such as mood, psychotic and personality disorders, have a negative impact because of the high degree of cognitive, emotional, and behavioral distortion [44], as well as the personal, social, and occupational impairment they entail. Therefore, the design and “preliminary implementation” of the educational program “MotivACTION” is proposed to further promote a positive attitude among young students to enable them to face the challenges of everyday life on a personal, social, and educational level.
Regarding the qualitative results about the perception of schoolchildren on the transfer of what they learnt in “MotivACTION” to their daily life at school, in their relationship with their peers and in their relationship with their family, they express through the written expression in item 10 of the ad-hoc questionnaire, the importance of expressing what they feel in different situations that occur in class, in the relationship with peers, the importance of setting up the “Nutriplate” in the family or how they would like to do physical exercise as a family. These findings are consistent with the previous interventions [21] focusing on environmental change and empowering individuals and communities, with a focus on including the family context, addressing early life determinants, as well as the need to reduce childhood obesity without increasing socio-economic inequalities [45].
Remarkably, that many studies indicate the need to apply educational and pedagogical programs that promote emotional competencies (CE), social competencies (CS), physical competencies (CF) (personal care and physical fitness level), self-concept, interpersonal competencies (CI), and adherence to the Mediterranean diet (ADM) [46,47]. Likewise, a recent qualitative study, supported by the Spanish Ministry of Consumer Affairs, on perceptions of healthy eating practices and lifestyle habits in the adolescent population, shows that adolescents’ perception of a healthy lifestyle is based on the connection between mental health and healthy practices understood as having a varied and controlled diet, doing physical exercise, knowing how to manage stress-causing factors such as social comparison, followed by excessive exams, lack of time to do homework and family demands. With special attention to physical exercise, extracurricular sports activities prevail, since, on the contrary, in their free time and recreational time they tend to choose passive activities, especially case of girls. About the motivations for physical exercise, one of the main factors is the person’s interest (intrinsic motivation) and the promotion of educational initiatives for physical exercise during school hours, such as dynamic playgrounds [48].
## 4.1. Limitations of the Study
Among the limitations of the study is the selection of the sample, which was carried out by accessibility and not in a randomized manner, thus compromising the external validity of the study. Another aspect to consider is the research design, which is a descriptive, cross-sectional pilot study. Quasi-experimental studies with pre- and post-intervention data collection with experimental and control groups are necessary to verify the causal relationships between the variables analyzed. Regarding data collection, other quantitative questionnaires with psychometric properties validated in the population under study (Cronbach’s Alpha or McDonald’s Omega values) should be administered. Future lines of research should have a quasi-experimental and longitudinal approach, pretest, and post-test. It would also be necessary to apply a randomized probabilistic sampling technique.
## 4.2. Future Practical Applications of the MotivACTION Program
Based on these aspects, the educational workshops “MotivACTION, feed your superACTION” emerge as an alternative didactic technique based on the incorporation of education in emotional skills, decision-making for problem-solving, nutritional education, and education in a more active lifestyle inside and outside school hours. In this way, it contributes to promoting pro-social behaviors, healthy nutritional behaviors, and the practice of physical exercise in young people. Therefore, this proposal simultaneously promotes emotional and prosocial behaviors [49] and an increase in daily physical activity among young people [37,50]. Future studies are needed with a longitudinal quasi-experimental design with repeated measures (pretest and posttest), with a non-randomized Control Group (CG) and Experimental Group (EG), analyzed using a quantitative methodology (tests and questionnaires) and qualitative methodology (observational analysis). In addition, the aim is to check other variables such as (a) anthropometry (percentage of body composition and body perimeters), (b) nutritional status variables (food records, adherence to the Mediterranean diet measured with the “Kid-Predimed questionnaire”), (c) real motor competence and perceived motor competence of schoolchildren and adolescents, (d) cognitive variables (assessment of executive functions), (e) perception of satisfaction with their lives (at a personal level, at home and in social relations). However, nutrition education and holistic (physical and mental) healthy lifestyle intervention programs would be effective educational trends, as long as financial support from social policies is available [23].
## 5. Conclusions
The young students had high scores on the items assessing the relevance of the workshop content to their personal lives and socio-educational experiences. As the main preliminary results of the pilot study, the schoolchildren who experienced the “MotivACTION” workshop reported high satisfaction with the educational program. They were able to create a healthy menu with the frog chef. They also felt better and happier at the end of it, and they enjoyed practicing physical activity moving to the rhythm of the music while doing mathematical calculations.
The educational workshop “MotivACTION, feed your superACTION” is presented as an integrative educational strategy to encourage emotional education, nutritional education, and the promotion of active lifestyle habits in children and adolescents.
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|
---
title: 'Prenatal Factors Associated with Maternal Cardiometabolic Risk Markers during
Pregnancy: The ECLIPSES Study'
authors:
- Ehsan Motevalizadeh
- Andrés Díaz-López
- Francisco Martín-Luján
- Josep Basora
- Victoria Arija
journal: Nutrients
year: 2023
pmcid: PMC10005748
doi: 10.3390/nu15051135
license: CC BY 4.0
---
# Prenatal Factors Associated with Maternal Cardiometabolic Risk Markers during Pregnancy: The ECLIPSES Study
## Abstract
To examine the associations of sociodemographic, lifestyle, and clinical factors with cardiometabolic risk and each of its components during pregnancy in a pregnant population from Catalonia (Spain). A prospective cohort study of 265 healthy pregnant women (39 ± 5 years) in the first and third-trimesters. Sociodemographic, obstetric, anthropometric, lifestyle and dietary variables were collected, and blood samples were taken. The following cardiometabolic risk markers were evaluated: BMI, blood pressure, glucose, insulin, HOMA-IR, triglycerides, LDL, and HDL-cholesterol. From these, a cluster cardiometabolic risk (CCR)-z score was created by summating all z-scores (except insulin and DBP) computed for each risk factor. Data were analyzed using bivariate analysis and multivariable linear regression. In the multivariable models, the first-trimester CCRs was positively associated with overweight/obesity status (β: 3.54, $95\%$CI: 2.73, 4.36) but inversely related to the level of education (β: −1.04, $95\%$CI: −1.94, 0.14) and physical activity (PA) (β: −1.21, $95\%$CI: −2.24, −0.17). The association between overweight/obesity and CCR (β:1.91, $95\%$CI: 1.01, 2.82) persisted into the third-trimester, whereas insufficient GWG (β: −1.14, $95\%$CI: −1.98, −0.30) and higher social class (β: −2.28, $95\%$CI: −3.42, −1.13) were significantly associated with a lower CCRs. Starting pregnancy with normal weight, higher socioeconomic and educational levels, being a non-smoker, non-consumer of alcohol, and PA were protective factors against cardiovascular risk during pregnancy.
## 1. Introduction
Significant metabolic and physiological changes sustain a typical pregnancy and promote fetal growth and development [1]. However, inadequate adaptation to these changes (e.g., interrelated cardiometabolic alterations such as maternal obesity, elevated fasting glucose, insulin resistance and/or hyperinsulinemia, dyslipidemia, and elevated blood pressure (BP)) sometimes leads to serious complications that affect the health of both mother and child. It is therefore critically important to study cardiometabolic risks in pregnant women since several maternal sociodemographic and lifestyle-related risk factors can negatively influence the cardiometabolic status of pregnant women [2,3,4].
Regarding lifestyle, maternal diet quality is a potentially modifiable behavior involved in the etiology of cardiometabolic disorders during gestation [5,6,7,8,9]. Reinforcing this evidence, epidemiologic studies have reported that dietary approaches to prevent hypertension, such as a healthy diet comprising a high intake of fruit, vegetables, whole grains, and low-fat dairy products produced beneficial effects on glucose, lipid profile, and BP during pregnancy [6,8]. For example, a Mediterranean-style diet (MedDiet) has been associated with lower prenatal maternal BP [7] and cardiometabolic risk among pregnant women [5].
Evidence also suggests that a lack of physical activity (PA) from the first-trimester increases the risk of pregnancy complications (e.g., gestational hypertension, gestational diabetes mellitus (GDM), pre-eclampsia, and excessive gestational weight gain (GWG) [10]. Another well-established risk factor is smoking during pregnancy. Several studies have also linked prenatal maternal smoking to multiple adverse health outcomes for both mother [11] and child [12]. However, the role of maternal smoking on glucose and lipid metabolism disturbances during pregnancy has been less studied [13,14,15]. Similarly, the available evidence of maternal alcohol consumption is particularly sparse [16,17]; among its main complications are cesarean delivery, stillbirth, high birth weight, and infant mortality.
Unhealthy lifestyles adopted by women of reproductive age also predispose them to overweight/obesity in pregnancy, associated with cardiometabolic risk factors such as insulin resistance [18] and worse lipid profile [19,20]. It has been suggested that inappropriate GWG, especially in later pregnancy, may also increase the risk of adverse obstetric outcomes [21,22].
Previous studies on maternal lifestyle behaviors and cardiometabolic risk during pregnancy have focused on specific cardiometabolic risk markers and only a few studies [5,23] have considered whether combinations of biological risk factors formed a clustered cardiometabolic risk (CCR) score. In this context, a cluster of cardiometabolic factors has been reported to be more strongly associated with adverse pregnancy outcomes than just one factor [24]. Using this factor-cluster approach would help to better identify high-risk women during pregnancy. It is also important to prospectively reassess the cardiometabolic risk of pregnant women in order to determine whether this risk is stable or whether it progresses over the course of pregnancy.
It can generally be stated that cardiometabolic risk markers during pregnancy are influenced by multiple factors specific to each population. However, few studies have been conducted specifically among pregnant populations in the Mediterranean area, where the socio-demographic and Mediterranean lifestyle traits of women can be regarded as protective factors against cardiovascular risks. The key to planning effective strategies to prevent and treat future obstetric complications is to understand which maternal factors have favorable effects on cardiometabolic risk during pregnancy and define critical periods in which this relationship is most affected.
To further knowledge in this area, we aimed to investigate the association between prenatal sociodemographic, lifestyle, and clinical characteristics and clustering cardiometabolic risk and its components in the first and third-trimester of pregnancy in a population of pregnant women from a Mediterranean region in northern Spain.
## 2.1. Study Design
A population-based prospective cohort study of healthy pregnant women who participated in the ECLIPSES study was conducted from the first to the third-trimester of pregnancy. A description of ECLIPSES has been published elsewhere [25]. Eligible participants were healthy adult women over 18 years with ≤12 weeks of gestation. Details of the inclusion/exclusion criteria can be found elsewhere [25].
Of the 793 pregnant women initially enrolled in the study, for the present analysis, all women who had data regarding serum cardiometabolic markers in the first (12 weeks) and/or third (36 weeks) trimester of pregnancy were included. The total study sample therefore comprised 265 pregnant women (Figure 1). All participants signed an informed consent form. The study was approved by the Ethical Committee of the Jordi Gol Institute for Primary Care Research and the Pere Virgili Institute for Health Research (approval ID: $\frac{118}{2017.}$ Date: 28 September 2017) and complied with the tenets of the Helsinki declaration.
## 2.2. Data Collection
Midwives and nutritionists collected the participants’ medical and obstetric history, gestational age, socioeconomic information, and education level. In the first and third-trimesters of pregnancy, lifestyle habits (PA, smoking, diet, and alcohol consumption), BP, and anthropometric measurements were also collected. The socioeconomic level was classified as low, mid, or high according to the Catalan classification of occupations (CCO-2011) [26]. Education level was classified as low (primary), medium (high school), and high (university studies or above). PA was measured using the short version of the International PA Questionnaire (IPAQ-S) [27]. Derived from total metabolic equivalents (METs-min/week) and based on the frequency and duration of walking and moderate and vigorous-intensity activity, this variable was divided into tertiles for analysis. The Fagerström questionnaire [28] was used to assess smoking, with women divided into three groups: current, former, and never smokers.
Eating habits were assessed through a self-administered food frequency questionnaire (FFQ) based on 45 food groups previously validated in our population [29]. Herein, we focused on women’s overall diet quality assessed using the relative rMedDiet score based on the intake of nine food groups [30]. This index, which was previously used in our published paper [30], is a modified version of the original MedDiet Score [31]. The resulting score ranged from 0 to 18 points, with larger values indicating greater diet quality. Since there are no pre-established cut-off points for the pregnant population, we divided the score into tertiles. Alcohol consumption was assessed as ‘yes’ or ‘no’.
Anthropometric measures were weight (kg) and height (cm). BMI was calculated from these measures (weight(kg)/height(m)2). Women were classified following WHO criteria [32] into normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25.0–29.9 kg/m2), or obesity (BMI ≥ 30 kg/m2) in the first-trimester. Total GWG, calculated from the difference between the weights measured in the first and third-trimester visits and taking into account initial BMI, was categorized as insufficient, adequate, or excessive in accordance with 2009 IOM recommendations [33].
## 2.3. Cardiometabolic Risk Markers
Blood samples were collected at weeks 12 and 36 of pregnancy after an overnight fast and stored at −80 °C inside the Biobank until analysis. The fasting serum cardiometabolic biomarkers assessed included glucose, insulin, and lipids, which were analyzed at the accredited Laboratori Clínic ICS Camp de Tarragona-Terres de l’Ebre, Joan XXIII University Hospital in Tarragona (Spain). All samples were thawed and analyzed at the same time to minimize inter-batch variation. Simultaneously, glucose, total cholesterol, HDL cholesterol (HDL-c), and triglyceride (TG) concentrations were measured using standard enzymatic automated methods. Intra- and interassay coefficients of variation (CVs) were below $2.2\%$ for all. LDL cholesterol (LDL-c) was calculated using the *Friedewald formula* (LDL-c = total cholesterol-HDL-c-triglycerides/5). Serum insulin levels were assayed by a chemiluminescent immunoassay method on an ADVIA Centaur analyzer using a commercial kit (ADVIA Centaur IRI, Siemens Healthcare Diagnostics Inc., Tarrytown, NY, USA). Lower and upper detection limits were 0.5 and 300 mUI/L, respectively. The intra- and interassay CV ranges were 3.3–$4.6\%$ and 2.6–$5.9\%$, respectively.
Insulin resistance was estimated by homeostasis model assessment (HOMA-IR) using the following equation: HOMA-IR = fasting insulin (μIU/mL) × fasting glucose (mmol/L)/22.5.
SBP and DBP were measured in both trimesters using an automatic digital monitor (Omron HEM-705CP).
A clustered cardiometabolic risk (CCR) score was created by summing all standardized z-scores (z = value-mean/SD of the whole population) of the seven cardiometabolic markers assessed (BMI, SBP, glucose, HOMA-IR index (log), TG (log), LDL-c, and HDL-c). HDL-c was calculated after values were multiplied by −1 since it is inversely related to metabolic risk. Only SBP was considered in the CCR score since SBP and DBP were highly correlated. A higher CCR score entails greater cardiometabolic risk. The rationale for selecting this CCR score and its components were based on a previous pregnancy study that used a similar risk score and factors [5].
The continuous CCR score was estimated for 264 women and 215 women whose seven health parameters were measured in the first and third trimester of pregnancy, respectively. In this study, the CCR score and each cardiometabolic factor were the primary and secondary outcomes, respectively.
## 2.4. Statistical Analysis
All statistical analyses were performed using the 15.0 version of STATA software (Stata Corp LP, College Station, TX, USA). Descriptive statistics were used to characterize the population. Data are expressed as mean ± SD for quantitative variables and number (%) for categorical variables.
The normality of the data was tested using both statistical (Shapiro–Wilk test) and graphical methods (histograms and scatter plots). Variables non-normally distributed were logarithmically transformed for analyses (insulin, HOMA-IR, and TG). The between-group differences in each cardiometabolic risk variable in both trimesters were analyzed by one-way ANOVA with Bonferroni’s test for post hoc comparison and Student’s T-test, as appropriate. Paired-samples t-tests were performed to evaluate intra-group differences for the cardiometabolic risk variables between the first and the third trimesters.
Multivariable linear regression analyses were performed to evaluate the independent contributions of selected sociodemographic and lifestyle characteristics of the pregnant women on the CCR score and each cardiometabolic risk factor (BMI, SBP, DBP, glucose, insulin, HOMA-IR, TG, HDL-c, LDL-c) in the first and third trimesters of pregnancy. A multivariable linear regression analysis was also performed to evaluate the independent contribution of the first-trimester CCR score to the third-trimester CCR score. We used our prior knowledge to select the following prenatal characteristics: age (<25, 25–29, ≥30 years), social class (lower/medium, high), education level (primary/secondary, university studies), smoking status (non-smoker, current/former smoker), alcohol consumption (no, yes) PA (METs-min/week, tertiles), rMedDiet score (tertiles), and GWG (insufficient, adequate, excessive). Estimates were presented as β coefficient (β) and $95\%$ confidence intervals (CIs). Multicollinearity was assessed by inspecting the tolerance (1/VIF) values and variance inflation factors (VIFs) for this multivariable model. All tolerance values were above 0.7 and all VIFs were below 2.0, which suggests there were no concerns over multicollinearity. Statistical significance was set at $p \leq 0.05.$
## 3. Results
The sociodemographic and lifestyle characteristics of pregnant women are shown in Table 1. The mean age of the women was 29.6 (SD, 4.7), with $57\%$ of them over 30 years old. Their mean initial BMI was 24.1 (3.5) kg/m2, with roughly $36\%$ of them classified as overweight/obese with a BMI ≥25.0 kg/m2. Their mean GWG was 10.4 (3.6) kg. According to IOM recommendations, $37\%$ of the women met the criteria for GWG, while $45\%$ fell below them and $18\%$ exceeded them. A third of the women ($32\%$) had received a university education, $19\%$ of them were from a high social class, and $31\%$ were former smokers or smoked during pregnancy. Mean PA was 475.8 (701.9) METs-min/week and the mean rMedDiet score was 9.4 (2.4).
All cardiometabolic markers and lipid parameters increased between the first and third trimesters, while fasting glucose decreased (all $p \leq 0.05$).
Comparisons between the characteristics of pregnant women in relation to their CCR score and its components between the first and third-trimesters are shown in Supplementary Tables S1 and S2, respectively. Results from multivariate-adjusted regression analyses in the first trimester are shown in Table 2. These cross-sectional analyses showed that, irrespective of other factors: age above 30 years was significantly associated with greater HDL-c levels; university education was associated with lower BMI and SBP; and a higher level of PA was associated with lower LDL-c levels (all $p \leq 0.05$). Multiple regression analysis, on the other hand, showed that obese/overweight status in early pregnancy was, as expected, independently and positively associated with BMI, SBP, DBP, insulin, HOMA-IR, and LDL-c levels (all $p \leq 0.05$).
Prospective multivariate-adjusted analyses (Table 3) showed that the associations between overweight/obesity status and higher BMI and lower HDL-c levels persisted in the third-trimester even after potential confounders were controlled (all $p \leq 0.05$). Similarly, BMI and SBP levels were higher in women with excessive GWG, while HDL-c levels increased. Moreover, multivariate analysis showed that smoking and drinking alcohol in pregnancy were independent factors associated with fasting TG and LDL-c, and both SBP and DBP, respectively, as time progressed. However, BMI, SBP and DBP levels and fasting glucose concentrations showed a significant inverse association with insufficient GWG. Additionally, women with a university education showed smaller increases in BMI during their pregnancy, while high social class was inversely associated with lower fasting glucose, insulin, and HOMA-IR levels at the end of pregnancy (all $p \leq 0.05$).
Figure 2, which shows subgroup analyses by different variables of interest, reveals statistically significant associations between CCR scores and overweight/obesity status (positive), university education (negative), and higher levels of PA (negative) at the beginning of pregnancy (all $p \leq 0.05$). Note that the associations between overweight/obesity status and CCR score persisted into the third-trimester. Moreover, a significant association was found between women with insufficient GWG and those with high social class and lower CCR scores (all $p \leq 0.05$). In the third-trimester, no significant association with other factors was found.
After adjusting for confounding factors, we found that the first-trimester CCR score was significantly and independently related to the third trimester CCR score (β: 0.31, $95\%$CI: 0.19, 0.43; $p \leq 0.001$).
## 4. Discussion
This study describes the association between maternal factors (socio-demographic and lifestyle characteristics) and clustering cardiometabolic risk and its components throughout pregnancy in a Spanish population of healthy pregnant women. Our main findings are that potentially modifiable prenatal factors, such as having a normal weight in early pregnancy, lower GWG, and more PA, as well as higher education and social class levels, were significantly and independently associated with lower CCR. Smoking and drinking alcohol during pregnancy also showed a non-significant trend towards higher CCR at the end of pregnancy. The results of each cardiometabolic biomarker also maintained the same relationship. Interestingly, the women’s CCR score in the first trimester was an independent predictor of their CCR score in the third trimester, which suggests cardiometabolic risk progressed as pregnancy advanced.
We can hypothesize from our findings that BMI at pregnancy baseline is more relevant than GWG when predicting cardiometabolic risk during pregnancy. Indeed, we found that early pregnancy overweight/obesity was the strongest predictor of the CCR score in both early and late pregnancy. Despite the importance of maternal obesity for the subsequent development of cardiovascular and metabolic alterations, to our knowledge, this is the first time that this relationship has been described in pregnant women using a composite risk score. Moreover, overweight/obese women had a less favorable cardiometabolic profile, with higher SPB, DPB, insulin resistance, and LDL-c in the first-trimester than their normal-weight counterparts. As our results and those of other studies conducted in the first-trimester of pregnancy show, being overweight/obese increases the risk of hypertension in pregnant women [34,35].
We found that SBP and DBP in women with insufficient GWG decreased in the third-trimester. These findings are consistent with a recent meta-analysis of observational studies, which showed that excessive GWG is associated with a higher risk of hypertensive disorders during pregnancy [36] and should therefore be avoided.
Our data support previous evidence that showed that overweight/obese pregnant women had significantly higher insulin and HOMA-IR, especially in the first-trimester [37]. However, the effect of GWG on glucose metabolism is less studied and the few data published are somewhat contradictory [38,39,40]. In the present study, women who did not gain enough weight during pregnancy had lower blood glucose levels in the third trimester than those with adequate weight gain. It has been argued that, just like outside pregnancy, an increase in maternal adiposity during pregnancy causes a higher systemic inflammatory response and greater oxidative stress, which in turn promote hyperglycemia and, eventually, insulin resistance [41,42].
Serum lipid concentrations are known to increase as pregnancy progresses [19,43,44]. However, this pregnancy-associated hyperlipidemia appears to be exacerbated in overweight/obese women, probably as a result of insulin resistance [39,45,46,47]. In line with previous studies [45,46], our data suggest that overweight/obese pregnant women are more likely to present a more pro-atherogenic lipid profile. Our data also showed a positive association between excessive GWG and a significant increase in HDL-c in the third-trimester. In accordance with this observation, a recent study suggested that high levels of HDL-c in the third-trimester, especially in women with excessive GWG, may be considered a high-risk indicator of small size for gestational age [48].
From our findings and the above evidence, it is imperative that overweight/obese women of reproductive age should be encouraged to undertake preconception-intensive behavioral lifestyle interventions for weight loss and improve their metabolic status before and during very early pregnancy [49]. As Catalano suggests [50], unfavorable maternal status in terms of weight or cardiometabolic profile in early pregnancy is a harbinger of future abnormalities in late pregnancy and beyond. Those findings are also supported by our study, which found a significant association between first and third-trimester CCR scores.
With regard to lifestyle factors such as diet, there is clear evidence that certain individual nutrients and food groups are associated with cardiovascular risk also in the pregnant population [5,6,7,8]. However, our study did not show a relationship between the quality of the maternal diet (using the Mediterranean diet score) and cardiometabolic risk during pregnancy. Nevertheless, our results support the importance of adhering to this healthy dietary pattern since it protects against maternal obesity, excessive GWG, and other adverse short-term and long-term maternal and child outcomes [51]. A more specific study focused on individual dietary components (nutrients or food groups) could establish a relationship.
Similar to other Spanish studies [52], $13\%$ of the pregnant women in our study consumed alcohol. Our findings support previous results [16] that showed that in the third-trimester, SBP, DBP, and LDL-c were higher in women who consumed alcohol than in those who did not.
Exposure to tobacco smoke during pregnancy also influences lipid-profile parameters. We also found that pregnant smokers had significantly higher third trimester levels of TG and LDL-c, even after adjusting for BMI and GWG, as well as a tendency towards a worse cardiometabolic risk profile. The two epidemiological studies conducted in this field so far have also revealed a more unfavorable lipid profile in pregnant smokers than in pregnant non-smokers [14,15]. Increased lipoprotein lipase (LPL) activity may be responsible for elevated LDL-c levels through the LPL-mediated degradation of TG-rich chylomicrons and VLDL, which, probably induced by nicotine, is markedly higher in smokers [53]. Another effect of nicotine on lipid metabolism is impaired LDL-c clearance [54]. Moreover, nicotine also increases circulating free fatty acid through enhanced lipolysis resulting from sympathoadrenal stimulation [55]. Thus, smoking and the presence of lipid disorders are inadvisable during pregnancy since they may also contribute to deleterious cardiovascular and atherogenic effects.
With regard to maternal PA, our results agree with those of earlier studies which suggest that habitual PA reduces TG and total cholesterol during early pregnancy [56,57], and LDL-c in the last two trimesters [56,57]. This highlights the importance of promoting PA to control lipid disorders, especially in the first-trimester when the fetal organs are formed, and the placenta begins to develop [58].
In the present study, socio-environmental factors, especially higher levels of education (in relation to lower BMI and SBP) and social class (in relation to lower fasting glucose, insulin, and HOMA-IR) were also strongly associated with better cardiometabolic markers and lower CCRs in the first and third-trimesters of pregnancy, respectively. Generally, these findings are supported by those of previous studies [3,59,60]. However, the nature of such associations during pregnancy remains unclear. They probably reflect a combination of social/psychological factors and healthier behaviors (in those with higher education) that result directly or indirectly in cardiometabolic benefits. We found, for example, that pregnant women with higher social and educational levels were older and had lower early pregnancy BMI. In the present study, these factors appear to be associated with a better metabolic phenotype during gestation.
The main strength of our study is the analysis of cardiometabolic health during pregnancy using a clustering of cardiometabolic risk factors, which provides greater overall risk than any individual factor on its own. This approach has rarely been used in previous studies. Moreover, we decided to use a CCR score that combined clinical and biochemical parameters, including adiposity, BP, insulin resistance, and lipids, since these can be measured easily in routine clinical practice and, even more importantly, are all major risk factors for cardiovascular disease. Additionally, the continuous CCR score is statistically more sensitive and less prone to error than categorical forms. Another advantage of our study is its prospective design and relatively large sample size, reinforcing the usefulness of our results. However, certain study limitations should also be considered. Namely, we use the maternal final weight at around 36 weeks to calculate GWG, which may cause misclassification of the GWG category, especially among overweight/obese women, thus reducing the estimated effect. Additionally, the CMR score is specific to this study sample, and we assumed that each component has equal weight in predicting metabolic risk.
## 5. Conclusions
Our findings provide evidence of the effects of sociodemographic, lifestyle, and clinical characteristics during pregnancy on cardiometabolic health in a Spanish Mediterranean population of healthy pregnant women. The most protective modifiable prenatal factors of cardiometabolic risk during pregnancy were being of normal weight, having higher levels of education and social class, engaging in greater PA, being a non-smoker, and not drinking alcohol. These findings will help policymakers to improve metabolic status in women before and/or during very early pregnancy in order to prevent obstetric complications. Further prospective studies are needed to determine whether clustering cardiometabolic risk variables helps to determine the risk of adverse mother and fetus/child outcomes more than individual risk factors.
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---
title: 'Breastfeeding Is Associated with Higher Adherence to the Mediterranean Diet
in a Spanish Population of Preschoolers: The SENDO Project'
authors:
- Asier Oliver Olid
- Laura Moreno-Galarraga
- Jose Manuel Moreno-Villares
- Maria del Mar Bibiloni
- Miguel Ángel Martínez-González
- Víctor de la O
- Alejandro Fernandez-Montero
- Nerea Martín-Calvo
journal: Nutrients
year: 2023
pmcid: PMC10005753
doi: 10.3390/nu15051278
license: CC BY 4.0
---
# Breastfeeding Is Associated with Higher Adherence to the Mediterranean Diet in a Spanish Population of Preschoolers: The SENDO Project
## Abstract
Objective: To assess whether breastfeeding during the first months of life is associated with adherence to the Mediterranean dietary (MedDiet) pattern in preschool children. Design: The Seguimiento del Niño para un Desarrollo Óptimo (SENDO) project is an ongoing pediatric cohort with open recruitment, started in 2015 in Spain. Participants, recruited when they are 4 to 5 years old at their primary local health center or school, are followed annually through online questionnaires. For this study, 941 SENDO participants with full data on study variables were included. Breastfeeding history was collected retrospectively at baseline. Adherence to the Mediterranean diet was assessed with the KIDMED index (range −3 to 12). Results: After accounting for multiple sociodemographic and lifestyle confounders, including parental attitudes and knowledge about dietary recommendations for children, breastfeeding was independently associated with a higher adherence to the MedDiet. Compared with children who were never breastfed, children breastfed for ≥6 months had a one-point increase on their mean KIDMED score (Mean difference +0.93, $95\%$confidence interval [CI]. 0.52–1.34, p for trend <0.001). The odds ratio of high adherence to the MedDiet (KIDMED index ≥8) was 2.94 ($95\%$CI 1.50–5.36) in children who were breastfed for at least 6 months, as compared to their peers who were never breastfeed. Children who were breastfed for less than 6 months exhibited intermediate levels of adherence (p for trend <0.01). Conclusion: Breastfeeding for 6 months or longer is associated with a higher adherence to the Mediterranean diet during the preschool years.
## 1. Introduction
Diet can impact physical, mental, and emotional health. Diet quality among children is a matter of great concern for public health authorities worldwide, as an inadequate diet increases the risk of both undernourishment and obesity, and is associated with overall short- and long-term adverse health outcomes [1,2,3]. A healthy diet needs to provide all the essential macro- and micro-nutrients necessary for children’s growth and development. Nutritional epidemiology is currently focusing more on recommending global food patterns rather than focusing on any specific food or isolated nutrient [4] to better include possible interactions and synergistic effects. In this context, the Mediterranean dietary (MedDiet) pattern is a high-quality food pattern linked to many beneficial health effects [5]. It is characterized by the abundant intake of plant-based foods (vegetables, fruits, legumes, minimally processed grains, and nuts); low consumption of meat; moderate-high consumption of fish; and moderate consumption of dairy products, mainly consumed as yoghurt and cheeses. The global intake of lipids can be high in the MedDiet (around $40\%$ of total energy intake), but the ratio between beneficial monounsaturated and non-beneficial saturated lipids is high, due to the high monounsaturated content of olive oil, which is the main culinary fat used in Mediterranean countries [6,7]. Consistent evidence has reported that the MedDiet is associated with a lower obesity risk o and a lower risk of other non-communicable chronic diseases both in children [8] and adults [9].
The World Health Organization (WHO) currently states that breastfeeding is the recommended diet during an infant’s first 6 months of life, and encourages mothers to keep breastfeeding while introducing nutritionally-adequate and safe complementary foods for the first 2 years of age or beyond [10,11]. Breastfeeding provides numerous benefits to both mother and baby, including improved infant health and development, increased maternal bonding, and reduced risk of chronic diseases. Breastfeeding has shown both short- and long-time benefits in infants. In the short term, children who are breastfed show fewer respiratory infections [12], are less irritable, and achieve longer nocturnal sleep [13]. In the long term, children who were breastfed show a lower risk of asthma and atopy [12], obesity [11], cardiovascular disease, hypertension, and type 2 diabetes.
Another positive effect of breastfeeding, is that it has been associated with better diet quality in children, including higher intake of beneficial nutrients such as zinc or iron, and a reduced intake of saturated fats. Recently published articles have reported, for example, that breastfeeding is associated with lower consumption of ultra-processed foods [14] and higher intake of fruit and vegetables [15,16,17]. Along the same lines, a meta-analysis of participants from European cohorts found that the duration of breastfeeding was directly associated with higher consumption of fruits and vegetables in young children [18,19,20]. Therefore, different studies have focused on the relationship between breastfeeding and the dietary intake of different foods and macro- or micro-nutrients [21,22]; however, to our knowledge, whether breastfeeding is associated with certain global food patterns, such as the Mediterranean dietary pattern, has not been yet assessed.
In this study we aimed to analyze whether breastfeeding was linked to a better adherence to the MedDiet in preschoolers.
## 2.1. Study Population
The Seguimiento del Niño para un Desarrollo Óptimo (SENDO) project is an ongoing Spanish pediatric prospective cohort aimed at understanding the impact of diet and lifestyle on children´s health. It is a multipurpose study focused on the prevention of non-communicable diseases, specifically designed to analyze the health benefits of the Mediterranean food pattern in a Mediterranean setting (Spain). Participants are invited to enter SENDO by their pediatrician or by the research team at their school. The recruitment started in 2015 and is permanently open. The current number of participants is 979. Inclusion criteria require being between 4 to 5 years old at recruitment, and living in Spain. The only exclusion criterion is not having access to an internet-connected device to be able to complete the online questionnaires. Information is collected at baseline and updated annually through self-administered online questionnaires mainly completed by legal tutors or parents. The baseline (Q0) questionnaire collects extensive information on participants’ personal and family history, sociodemographic context, anthropometrics, diet, eating behaviors, lifestyles, physical activity, and personality traits. Participants’ data are electronically entered and exported to a secure web-based database SENDO follows all the rules of the Helsinki Declaration on Ethical Principles for Human Research, and its protocol was approved by the Ethical Committee for Clinical Research of Navarra (Pyto $\frac{2016}{122}$). The parents of the participants signed a paper-based informed consent form and mailed it to the study team before entering the SENDO project.
## 2.2. Assessment of the Exposure
Information regarding children´s breastfeeding history, type, and duration was provided retrospectively by the parents through specific questions included in the Basal questionnaire. Parents were asked whether their children had been given any type of breastfeeding (yes/no). Those with an affirmative answer were also asked about the type (exclusive/non-exclusive), the duration of the breastfeeding (less than 1 month, from 1 to <3 months, from 3 to <6 months, from 6 to <12 months, or longer than 12 months), and how old was the baby was when they completely stopped breastfeeding. The information was recategorized for the analyses as duration of total breastfeeding in three categories: never breastfed, breastfed for <6 months, or breastfed for ≥6 months. Non-breastfed children were used as the reference category to provide a baseline against which the effects of breastfeeding could be measured.
## 2.3. Assessment of the Outcome
Dietary assessment tools in SENDO include a food frequency questionnaire (FFQ) and several dietary scoring systems. Adherence to the MedDiet was assessed with the KIDMED (Mediterranean Diet Quality Index for Children) index [23], one of the most frequently used dietary score systems for measuring adherence to the MedDiet in pediatric populations and adolescents. The KIDMED, an a priori-defined dietary index, consists of 16 items, 12 positive items (score 0 or +1), and 4 negative items (score −1 or 0) to assess the intake of different food groups and components of the MedDiet. Thus, the score can be aggregated to provide a total score ranging from −3 to 12 points [24]. Positive items in the KIDMED score include the daily consumption of one or more than one fruit, the daily consumption of one or more than one fresh or cooked vegetable, the consumption of fish (at least 2–3/week), pulses (more than 1/week), pasta or rice (≥5 days/week), nuts (at least 2–3/week), cereal or cereal products and dairy products for breakfast, yoghurts and/or 40 g of cheese daily, and the use of olive oil. Negative items included the consumption of fast food more than once a week, not taking breakfast, using commercially-baked pastries for breakfast, and the daily consumption of sweets and candy. The KIDMED index has been validated in several studies and has been shown to be a reliable tool for assessing adherence to the MedDiet in children. According to their KIDMED score, participants were considered to have poor adherence (≤3 points), medium (4–7 points), or high (≥8 points) adherence to the MedDiet [25,26].
## 2.4. Evaluation of Covariates
A variety of covariates that could potentially impact the results were considered. The baseline questionnaire collected information on children’s factors (gestation, delivery and perinatal information, sociodemographic variables, dietary intake, and different lifestyle factors such as physical activity or sedentary behaviors) and maternal factors (maternal age, race, and education level).
Participant´s body mass index (BMI) was calculated as the ratio of reported weight (kg) to squared height (m2). Nutritional status was estimated using sex- and age-specific BMI cut-off points based on the International Obesity Task Force (IOTF) reference standards [27]. Dietary information was collected by a previously-validated semi-quantitative FFQ [28]. A team of dieticians derived the content of nutrients in each element in the FFQ using Spanish food composition tables [29] and online databases [30]. The nutrient content was calculated by multiplying the frequency of intake of each food by the edible serving and the nutrient composition of the specified serving size to calculate the total energy intake.
Information on physical activity was collected using a physical activity questionnaire that included a total of 17 different activities and 10 response categories, ranging from never to 10 or more hours/week. The metabolic equivalents of task (METs)-hour/week for each activity was then estimated by multiplying the number of METs of each activity by the weekly participation in that activity, weighted according to the number of months dedicated to each activity [31]. Total physical activity was quantified by adding the METs-h/week carried out during the free time. Screen time was estimated as the average number of hours dedicated to watching TV, using a computer, or playing video games per day.
Parental nutrition knowledge and eating attitudes were evaluated using two scores. The parental knowledge of nutritional recommendations for children was assessed with a Nutrition-knowledge score with questions about the recommended consumption frequency of ten food groups (dairy products, fruit, vegetables, cereals, meat, fish, eggs, pulses, nuts, and olive oil). Parents had to choose among 10 categories of response ranging from “never” to “6 or more times a day”. Each question was assigned 1 point if it complied with the dietary recommendations, and 0 points if not. The score was then expressed as a percentage, with a higher value meaning better knowledge about nutritional recommendations for children. For analysis, participants were categorized as low (<$40\%$), moderate (40–$70\%$), or high knowledge (>$70\%$). Low knowledge was used as the reference category. The parents healthy-eating attitudes towards their child’s habits were assessed with an eight-item questionnaire. Each question was given 1 point if it complied with dietary recommendations and 0 points if not; thus, the score ranged from 0 to 8 points. For analysis, parental eating attitudes were categorized into three categories: neglected or unhealthy (from 0 to 3 points), average or moderate (from 4 to 5 points), and positive or healthy eating attitude (from 6 to 8 points). The lowest category was also used as reference.
## 2.5. Statistical Analysis
Participants’ characteristics were compared by categories of breastfeeding. For descriptive purposes, we used means and standard deviations (SD) for quantitative variables and percentages (%) for categorical variables. Generalized mixed models to account for intracluster correlation among siblings were used.
We first calculated the mean change in the KIDMED score associated with breastfeeding. Then, we calculated the odds ratio (OR) and $95\%$ confidence interval (CI) for medium-high adherence to the Mediterranean diet using never breastfed as the reference category. We calculated crude- and multivariable-adjusted estimates through 3 progressively adjusting models. The first model was adjusted for sex, age, race (categorized as white vs. others), screen time (continuous), physical activity (continuous), and BMI z-score (continuous). The second model was adjusted for all the variables in model 1 plus gestational age (less than 38 weeks, 38 to 40 weeks, or more than 40 weeks), method of delivery (vaginal or caesarean), birth weight (continuous), maternal high education (yes or not), and maternal age (continuous). Finally, the third model was also adjusted for parental attitudes towards their child’s dietary habits (neglected, average, or healthy) and parental knowledge about nutritional recommendations for children (low, medium, or high). Finally, we calculated the marginal effect of breastfeeding, this is, the adjusted difference (and $95\%$ CI) in the proportion of children with medium-high adherence to the Mediterranean diet between categories of breastfeeding.
## 3. Results
From 979 participants recruited in the SENDO project, 941 children with full data on study variables and recruited between January 2015 and June 2022 were included for final analysis (mean age 5.01 y., SD: 0.85, $51\%$ males). The most relevant demographic characteristics of the 941 study participants according to breastfeeding categories are shown in Table 1. A total of $84.1\%$ of the participants had been breastfed (including all types and durations of breastfeeding) and $54.4\%$ had been breastfed for 6 months or more. Children who were breastfed for longer were more often born by vaginal delivery and had a higher birthweight. Regarding lifestyles, children who were breastfed reported less screen time, and mothers who breastfed their children for longer time were slightly younger and had longer gestations. In our study, maternal education was associated with breastfeeding and mothers who breastfed their children for a longer time showed better knowledge about children’s dietary recommendations and presented healthier attitudes towards their child’s dietary habits.
Using the KIDMED index, a score under 3 reflects poor adherence to the Mediterranean diet, a score from 4 to 7 describes an average adherence, and a score ranging from 8 to 12 reflects high adherence The study participants showed a median score of 6.0 points. The mean KIDMED index was 5.5 in children who were never breastfed, 5.7 in those who were breastfed for less than 6 months, and 6.4 in those who were breastfed for over 6 months. The proportions of children with high adherence to MedDiet (KIDMED ≥8 points) were $11.9\%$ among those who were never breastfed, $20.2\%$ among those breastfed for less than six months, and $26.6\%$ for those who were breastfeed for six months or longer.
We found that those children who were breastfed for six months or longer had a significantly higher KidMed index score compared to those who were never breastfed, with an adjusted average increase of nearly one more point in their final KIDMED index (mean difference +0.93 points ($95\%$ CI 0.52 to 1.34) after controlling for all the above-mentioned potential confounders (Table 2).
When the KIDMED score was dichotomized (high vs. low), we observed that compared with children who were not breastfed, those who were breastfed for six months or longer had over twofold higher odds (2.63, $95\%$ CI 1.46 to 4.71) of showing high adherence to the MedDiet (Figure 1) in the crude model. The estimates were consistent in progressively adjusted models. The final multivariable model (fully covariate-adjusted model), which controlled for a range of potential confounding variables, showed that breastfeeding for at least 6 months was associated with almost threefold higher odds (2.94, $95\%$ CI 1.50 to 5.36) of high adherence to the MedDiet (p for trend <0.01) independently of the child age, sex, race, physical activity, screen time, BMI, gestational age, birth method, and birthweight, as well as maternal age, educational level, dietary knowledge, and attitudes towards their child’s dietary habits. After adjusting for all the previously mentioned confounding factors, compared to the category of children who had not been breastfed, in the category of children who had been breastfed for 6 months or more, we found $9.8\%$ ($95\%$ CI: $3.1\%$ $16.3\%$) ($$p \leq 0.04$$) more participants with moderate-high adherence to the MedDiet (Table 3).
Multi adjusted model: Adjusted for sex, race (white or others), screen time (continuous), physical activity (continuous), BMI z score (continuous), gestational age (>38 weeks, 38 to 40 weeks, or more than 40 weeks), way of delivery (vaginal or caesarean), birth weight (continuous), maternal high education (yes or not), maternal age (continuous), parental attitudes towards child’s dietary habits (negative, medium or healthy), and parental knowledge about nutritional recommendations for children (low, medium, or high).
## 4. Discussion
Our study of Spanish preschoolers found a positive linear trend between breastfeeding and adherence to the MedDiet. Furthermore, children who were breastfed for six months or longer showed threefold higher odds of having high adherence to the MedDiet than their peers who were never breastfed. Breastfeeding has been previously associated with lower consumption of ultra-processed foods and higher consumption of fruits and vegetables or specific nutrients, but to our knowledge, this is the first study reporting a direct association between breastfeeding and a healthier overall dietary pattern such as the MedDiet.
Breastfeeding rates vary depending on several factors; in our study, the proportion of children who had been breastfed was slightly higher than the one reported by the Spanish Association of Pediatrics with data of the National Institute of Statistics (around $70\%$ at 6 weeks and $45\%$ at 6 months) [32], but similar to the one found in previous cohort studies [33]. This finding points to the well-known fact that in cohort studies there is self-selection that leads to the sample being composed of participants who are particularly health-conscious and tend to have better adherence to healthier lifestyles [34]. The proportion of children with high adherence to the MedDiet was similar to the results presented in previous studies with Spanish children [35] or children from other Mediterranean countries [36,37].
We observed significant differences in MedDiet adherence in preschoolers who had been breastfed for at least six months. The lack of significant results for those who were breastfed for less than 6 months (compared with those who were not breastfed) may be due to a suboptimal sample size. Nevertheless, we observed a significant linear trend, which suggests a dose-response relationship between the length of the breastfeeding and the odds of having better adherence to the MedDiet in childhood. This, together with the biological plausibility and consistency already discussed, suggests that the reported association may represent a true biological effect. The mechanism that links breastfeeding with later dietary habits is not fully understood. Together with the method of delivery [38], breastfeeding is known to influence the colonization of gut microbiota, which may have some effect on food tolerance or acceptance [39]. Moreover, previous studies suggested that early exposure to different flavors through breastfeeding, impacted by maternal diet, may influence a child’s acceptance of different foods [40,41].
It is important to address possible confounders mediating in this association. An association between breastfeeding and socioeconomic status may, for example, act as a potential confounder. At an ecological level, breastfeeding is more frequent in low income countries, where access to baby formulas is limited [42]. At the individual level, however, breastfeeding is more often observed in highly educated mothers with medium to high economic status [43]; the mother’s type of job and her company’s policy regarding maternal leave may also influence both the initiation and the duration of breastfeeding [44]. Moreover, women who choose to breastfeed tend to follow healthier diets, have better dietary knowledge, and promote an overall healthier diet in their children [45], encouraging consumption of fruits and vegetables and limiting their children’s exposure to unhealthy aliments. However, in our study, the association between breastfeeding and the MedDiet remained significant after the adjustment for maternal age, maternal education level, parental knowledge of children’s dietary recommendations, and parental attitudes towards children’s dietary habits, which suggest that breastfeeding may be an independent predictor of a healthier dietary pattern in childhood.
Our findings are interesting because they also reinforce the importance of breastfeeding to enhance healthy dietary habits later in life. Previous evidence, including a recently published article from the SENDO project [46], found that preterm children and those born by cesarean were at higher risk of being obese, and both prematurity and cesarean delivery have been associated with lower prevalence of breastfeeding initiation [47]. Along with this association, we observed significantly lower proportions of preterm- and cesarean-delivery children in the category of participants with longer breastfeeding duration. In this scenario, our results may be of value because they can help pediatricians and public health professionals to direct their efforts to actively promote breastfeeding in children at higher risk. We also consider it important to address this association in future studies aimed to analyze the relationship between breastfeeding and long-term health outcomes. In the future, when analyzing these kinds of long-term associations, researchers should consider adjusting their analysis for children’s dietary intake, as it is known to be influenced by breastfeeding duration and therefore could act as a confounder.
We must recognize some study limitations. First, the information used was self-reported by participants’ parents. Previous validation studies in the SENDO project have shown high correlations and excellent agreement in parent-reported data, proving that information reported by SENDO parents such as birthweight, birth length, and anthropometric measures at recruitment was valid to be used in epidemiological research [48]. Information on breastfeeding was collected retrospectively, so it is also susceptible to memory bias and errors, as it requires the recall of past experiences and behaviors. Nevertheless, we consulted the medical records of a random sample of 188 children and observed a 96,$8\%$ agreement in breastfeeding history. In those with an affirmative answer ($$n = 97$$), we observed a $73.2\%$ agreement in the duration of the breastfeeding. Moreover, as the validity of the self-reported information on breastfeeding was not associated with the child’s adherence to the MedDiet, in case of an information bias, it would lead to a non-differential misclassification; therefore, in any case, it would bias the estimate through the null, making it more difficult to observe statistically significant differences, but not affecting the validity of the results found. Second, the SENDO cohort is mainly composed of highly educated white families and therefore, it is not representative of the Spanish population. Although this factor may affect the generalizability of our findings, it could also have some positive effects, such as a higher validity of the self-reported information and a reduction in the potential confounding caused by heterogeneous socioeconomic variables [49]. Thirdly, although there is a delay between breastfeeding and the quality of the diet when the children are five years of age, the data for both times was collected similarly and is subject to the limitations of cross-sectional design. We also note that recall bias and social factors may provide better results for both breastfeeding and adherence to the MedDiet. Prospective studies are needed for causality to be inferred. Lastly, although our results were robust through the progressively adjusted models, we cannot totally remove the possibility of residual confounding by some non-considered variables.
## 5. Conclusions
In conclusion, we found that infant breastfeeding is directly associated with a healthier diet quality among preschool children, understood as a higher adherence to the MedDiet. This finding was independent of sociodemographic and lifestyle confounders, including parental attitudes and knowledge about dietary recommendations for children. Children who were breastfed for at least six months had a significant increase in their KIDMED score, compared to those who were never breastfed. Furthermore, the odds of high adherence to the Mediterranean diet were nearly three times higher in children breastfed for at least six months compared to those who were never breastfed.
Public health efforts should be made to follow the World Health Organization breastfeeding recommendations and to encourage mothers to breastfeed for the first six months of life in pursuit of long-term benefits for children’s dietary habits. This study provides additional evidence to support the importance of breastfeeding and its potential benefits on children’s dietary habits, highlighting the importance of early-life interventions to promote healthy dietary habits.
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---
title: Non-Nutritive Sweeteners Acesulfame Potassium and Sucralose Are Competitive
Inhibitors of the Human P-glycoprotein/Multidrug Resistance Protein 1 (PGP/MDR1)
authors:
- Laura Danner
- Florian Malard
- Raquel Valdes
- Stephanie Olivier-Van Stichelen
journal: Nutrients
year: 2023
pmcid: PMC10005754
doi: 10.3390/nu15051118
license: CC BY 4.0
---
# Non-Nutritive Sweeteners Acesulfame Potassium and Sucralose Are Competitive Inhibitors of the Human P-glycoprotein/Multidrug Resistance Protein 1 (PGP/MDR1)
## Abstract
Non-nutritive sweeteners (NNS) are popular sugar replacements used in foods, beverages, and medications. Although NNS are considered safe by regulatory organizations, their effects on physiological processes such as detoxification are incompletely understood. Previous studies revealed that the NNS sucralose (Sucr) altered P-glycoprotein (PGP) expression in rat colon. We also demonstrated that early-life exposure to NNS Sucr and acesulfame potassium (AceK) compromises mouse liver detoxification. Building upon these initial discoveries, we investigated the impact of AceK and Sucr on the PGP transporter in human cells to assess whether NNS influence its key role in cellular detoxification and drug metabolism. We showed that AceK and Sucr acted as PGP inhibitors, competing for the natural substrate-binding pocket of PGP. Most importantly, this was observed after exposure to concentrations of NNS within expected levels from common foods and beverage consumption. This may suggest risks for NNS consumers, either when taking medications that require PGP as the primary detoxification transporter or during exposure to toxic compounds.
## 1. Introduction
For decades, non-nutritive sweeteners (NNS) have been commonly used as food additives in the United States. They are found ubiquitously in products including “diet” or “zero sugar” foods and beverages, medicines, lip balms, chewing gums, tabletop sweeteners, and yogurts. NNS are small molecules with a significantly greater sweetness intensity than traditional nutritive sweeteners such as sucrose and fructose and do not contribute to caloric intake [1]. Six NNS are currently approved for consumption by the Food and Drug Administration (FDA): aspartame, sucralose (Sucr), acesulfame potassium (AceK), saccharin, neotame, and advantame [2]. Following toxicology studies in primarily animal models, the FDA defined an Acceptable Daily Intake (ADI) for each NNS, indicating daily amounts considered safe for human consumption.
Consumption of NNS is highly prevalent among people in developed countries [3]. Analyses of dietary trends show that, in the United States, $25\%$ of children and over $40\%$ of adults consume NNS [3]. Rates of NNS consumption are usually higher among females, people with obesity, and people with a diagnosis of diabetes [3]. Additionally, the majority of adults consuming NNS report doing so on a daily basis [3]. Furthermore, the proportion of US households purchasing foods and beverages containing NNS increased significantly from 2008 to 2018, mainly driven by an increase in beverages containing a combination of NNS and caloric sweeteners [4]. Interestingly, involuntary environmental exposure to NNS is also likely, as trace levels of NNS have been found in treated and untreated wastewater [5]. Indeed, NNS have been detected in blood, feces, and breastmilk samples from control groups in murine studies [6].
While nutritive sweeteners such as sucrose are easily metabolized for energy when consumed, most NNS are highly stable, not metabolized, and excreted intact. Two such NNS, sucralose (Sucr) and acesulfame potassium (AceK), are frequently combined in diet foods and beverages but show different distribution patterns in human tissues. Indeed, AceK is rapidly and nearly completely absorbed into the circulation, and, as it undergoes no metabolism, remains in its original structure as it bathes various organs [1]. Ultimately, $98\%$ of consumed AceK is excreted by the kidneys and removed through the urine. On the contrary, *Sucr is* absorbed at a lower rate, with only 10–$20\%$ of orally administered Sucr recovered in the urine of human subjects [7]. In one study, roughly $2\%$ of Sucr recovered in urine was found as glucuronide conjugates, demonstrating low metabolism rates in the liver. The majority of *Sucr is* excreted unmetabolized in feces. On average, over $90\%$ of orally administered *Sucr is* passed within five days of consumption.
While advertised in weight-loss programs [8], the effectiveness of NNS for successful weight loss and their impact on blood sugar control has been controversial, casting doubts over their potential for benefits beyond reducing dental caries [9,10,11]. NNS have been associated with an increased prevalence of non-alcoholic fatty liver disease (NAFLD), the hepatic manifestation of metabolic syndromes, and diabetes [12]. NNS also alter murine gut hormonal secretion and absorption [13] and promote “obesogenic” microbiome dysbiosis [14]. Importantly, adverse effects of NNS combinations or the use of NNS in specific populations were identified sometimes decades after FDA approval and were not revealed in the original toxicology studies, stressing the need for additional research on NNS safety [12,14]. Our lab recently highlighted a potentially damaging effect of NNS on detoxification pathways, with evidence of dysregulated liver function [6].
The liver is the main site of detoxification for drugs or xenobiotics that enter the circulation, utilizing conjugating enzymes and efflux transporters to neutralize and excrete toxins [15]. Liver metabolites can be either excreted through bile in feces or returned to general circulation and filtered through the kidneys. One essential detoxifying efflux transporter, P-glycoprotein (PGP), is impacted by the consumption of NNS [16]. PGP, also known as multidrug resistance protein 1 (MDR1) or ATP-binding cassette sub-family B member 1 (ABCB1), is an ATP-dependent 170 kDa transmembrane protein that expels a variety of intracellular compounds from tissues for excretion. PGP is also a major determinant of the absorption, disposition, and elimination of many common drugs in key tissues such as the intestines and blood–brain barrier [17]. In the liver, PGP effluxes drugs, toxins, biliary pigments, and metabolic conjugates from hepatocytes into the biliary ducts [18]. Inhibition of PGP by different drugs can lead to a compensatory increase in ABCB1/PGP expression as the tissue attempts to maintain its excretory functioning [19].
Previous work in our lab found prominent effects of NNS-induced toxicity in the livers of offspring from mothers fed a diet with AceK and Sucr [6]. Therefore, we wondered whether NNS consumption alters PGP efflux efficiency, leading to reduced detoxification capacity in the liver. In the present work, we confirmed that combined AceK and Sucr increase ABCB1/PGP levels in a human liver cell line, similar to expected expression levels following exposure to PGP inhibitors [19]. Further, we demonstrated that AceK and Sucr stimulated PGP ATPase activity while inhibiting the efflux of known PGP substrates, thus acting as competitive inhibitors of PGP. These effects were found at concentrations of NNS that have been recorded in human tissue samples after consumption of a single diet beverage [20,21,22]. Finally, AceK and Sucr were found to occupy PGP binding pockets in silico, making similar biochemical contacts as the known competitive inhibitor drugs Verapamil and Vincristine. In conclusion, our findings suggest that consuming AceK and Sucr within recommended levels poses potential risks for those also taking medications transported by PGP. Further research may be necessary to determine safe levels of NNS consumption for people taking these medications.
## 2.1. Cell Culture
HepG2 and HEK-293 cells (ATCC, Manassas, VA, USA) were cultured in Dulbecco’s Modified Eagle Medium (DMEM) and supplemented with $10\%$ FBS (Gibco, Waltham, MA, USA), $1\%$ penicillin/streptomycin (Gibco, Waltham, MA, USA), and 200 mM L-glutamine (Gibco, Waltham, MA, USA). Cells were maintained in a humidified incubator with $5\%$ CO2 at 37 °C.
## 2.2. NNS and Other Reagents
Acesulfame potassium (AceK) and sucralose (Sucr) were kindly provided by Kristina L. Rother (NICHD, National Institutes of Health, Bethesda, MD, USA) or alternatively bought from Sigma-Aldrich (St. Louis, MO, USA—Sucr) or Cayman Chemical Company (Ann Arbor, MI, USA—AceK). Experimental NNS concentrations were determined based on previously reported levels in tissue after dietary exposure for each NNS and can be found in Table 1. For in vitro PGP functional assays, Verapamil HCl was purchased from BioVision, Inc (Milpitas, CA, USA). and Calcein-AM was purchased from Cayman Chemical Company (Ann Arbor, MI, USA). Alternatively, Calcein-AM retention was measured with the Multidrug Efflux Transporter P Glycoprotein (MDR1/P-gp) Ligand Screening Kit (Abcam, Cambridge, UK, #ab284553). For cell-free PGP activity assays, the ADP-Glo MAX assay (Promega, Madison, WI, USA, #V7001) was used for ATPase stimulation. Sodium orthovanadate was also purchased from Promega (Madison, WI, USA).
## 2.3. RNA Extraction, cDNA Synthesis, and RT-qPCR
RNA was extracted with the PureLink RNA Mini Kit with on-column DNAse digestion (Invitrogen, Waltham, MA, USA) according to the manufacturer’s protocol. cDNA was synthesized using qScript cDNA SuperMix (QuantaBio, Beverly, MA, USA). Quantitative PCR (qPCR) was performed using PerfeCTa SYBR Green FastMix, Low ROX (QuantaBio, Beverly, MA, USA) on the QuantStudio3 qPCR systems (Applied Biosystems, Waltham, MA, USA) according to the manufacturer’s protocol. Data were analyzed using the 2−ΔΔCt methods [23]. Primers used in this study can be found in Table S1.
## 2.4. Protein Extraction, SDS Page, and Western Blot
Cells were lysed at 4 °C in Octyl Glucoside buffer (25 mM Tris-HCl pH 7.4, 20 mM NaCl, 60 mM octyl glucoside). Lysates were sonicated and centrifuged at 18,000× g for 20 min at 4 °C. Laemmli buffer (4×) (200 mM Trist-HCl pH 6.8, 277 mM SDS, 5.4 M glycerol, $20\%$ [v/v] beta-mercaptoethanol, 3 mM bromophenol blue) was added to the supernatant of each lysate for a final concentration of 1× and boiled at 95 °C for five minutes before loading on a $6\%$ SDS-PAGE pre-cast gel (Invitrogen, Waltham, MA, USA) for electrophoretic separation of proteins. Then, proteins were transferred onto a 0.45 mm nitrocellulose membrane using 14 h wet transfer (10 V, 0.7 mA) at 4 °C. For the protein loading control, membranes were first washed two times with nanopure water for 2 min, then stained with Total Protein Stain (Invitrogen, Waltham, MA, USA) for 10 min and rinsed in nanopure water an additional three times for 2 min. Membranes were then blocked in freshly prepared non-fat dry milk ($10\%$ [w/v] in TBS-T—25 mM Tris pH 7.5, 150 mM NaCl, $0.05\%$ w/v Tween-20) for 45 min. Membranes were incubated with mouse monoclonal anti-P-glycoprotein antibodies (Sigma-Aldrich, St. Louis, MO, USA, #p7965) at 1:1000 dilution in $10\%$ Milk/TBS-T for one hour at room temperature (RT, 25 °C). After 3 TBS-T washes, membranes were incubated with a goat anti-mouse fluorophore-conjugated secondary antibody (LI-COR, Lincoln, NE, USA) at 1:10,000 dilution in TBS-T for one hour at RT. After 3 washes with TBS-T and one rinse of 5 min with PBS, membranes were imaged on the Odyssey FC Imager (LI-COR, Lincoln, NE, USA). Images of full Western blots can be found in Figure S1.
## 2.5. Calcein-AM Retention Assay
Inhibition of PGP substrate efflux was assessed by Calcein-AM retention using either individually purchased reagents or the Multidrug Efflux Transporter P Glycoprotein (MDR1/P-gp) Ligand Screening Kit (Abcam, Cambridge, UK), which contained identical reagents in proprietary concentrations. Briefly, HepG2 or HEK-293 cells were seeded at a confluence of 8 × 104 cells per well in a black-walled clear-bottomed 96-well plate and grown to roughly $90\%$ confluence. Reagents were prepared in assay buffer (Hanks Balanced Salt Solution (HBSS) supplemented with 20 mM HEPES (from 1M HEPES buffer solution, Gibco, Waltham, MA, USA)) immediately before assay. Cells were washed once with warmed (37 °C) HBSS. Then, the plate was incubated at 37 °C for 15 min with assay buffer containing either diluted NNS, the PGP competitive inhibitor Verapamil (50 μM) (positive control), or equal volume of vehicle (HBSS) (negative control). Reporter substrate Calcein-AM was then added to wells to a final concentration of 0.25 μM, protected from light, and incubated at 37 °C for 30 min. Fluorescence was then measured on the FLUOstar Omega microplate reader (BMG LabTech, Ortenberg, Germany) with excitation/emission spectra at $\frac{488}{532}$ nm. For assay optimization, HepG2 or HEK293 were incubated with increasing concentrations of Calcein-AM, and Calcein retention was measured as fluorescent signal at baseline and every 10 min thereafter for 70 min (Figure S2). HepG2 or HEK293 were then incubated with combined AceK and Sucr, Verapamil, or vehicle control and a select range of Calcein-AM concentrations for 30 min to determine Calcein-AM sensitivity to pharmacological inhibition (Figure S3).
## 2.6. PGP ATPase Activation Assay
P-glycoprotein ATPase activity was assessed using the ADP-Glo Max Assay (Promega, Madison, WI, USA). 5× assay buffer (250 mM Tris-MES pH 6.8, 50 mM MgCl2, 10 mM EGTA, 250 mM KCl, 25 mM sodium azide, 10 mM DTT) was prepared ahead of time and diluted to a final concentration of 1× with nanopure water immediately before each assay. Briefly, PGP membranes, substrates, inhibitor, NNS stock, and reagents were thawed at room temperature (RT, 25 °C) and prepared immediately prior to assay. PGP substrates, NNS, or inhibitors in buffer were added to opaque white 96-well plates. Verapamil (50 μM) is efficiently transported by the PGP and used as a positive control for maximum PGP stimulation. Sodium orthovanadate (100 μM) completely inhibits PGP ATPase inhibition and was used as negative control of background PGP activity. Human MDR1 vesicles (Invitrogen, Waltham, MA, USA) (0.01 mg in 1× assay buffer) were added to the wells and incubated at 37 °C for 5 min. Then, 10 μL of 5 mM Mg-ATP was added to each well as substrates for PGP (final concentration 5 mM Mg-ATP), and incubated at 37 °C for an additional 40 min. After 10 min equilibration to RT, 25 μL of ADP-GLO reagent was added to each well and incubated at RT for 40 min. Then, 50 μL of ATP Detection Reagent was added to each well in a dark room. The plate was briefly spun to mix, covered, and incubated at RT for 1 hr. Luminescence was measured on the FLUOstar Omega microplate reader (BMG LabTech, Ortenberg, Germany). ATPase stimulation from the PGP competitive inhibitor Verapamil was verified prior to treatment with AceK and Sucr (Figure S4).
## 2.7. Molecular Docking
The Cryo-EM structure of the nanodisc reconstituted, drug-free human PGP/ABCB1 was retrieved from the Protein Data Bank (PDB #7A65) [24]. Hydrogen atoms and gasteiger charges were added using the prepare_receptor script from ADFR suite [25] following the AutoDock Vina Documentation (Release 1.2.0) [26]. The reference 3D models of acesulfame, sucralose, and verapamil were retrieved from the ZINC database [27] in mol2 format and processed with the prepare_ligand script from ADFR suite. The resulting pdbqt files for the receptor and ligands were used in docking experiments with AutoDock Vina [26,28]. We used the Orientations of Proteins in Membranes (OPM) database [29] (model 7a65.pdb) to adjust the docking box coordinates and dimensions in the transmembrane region of the receptor. Vina 1.2 was run with an exhaustiveness parameter of 32, and 20 binding poses were collected for each experiment. For each ligand, the corresponding binding poses on the receptor were inspected and checked for consistency against experimental data [30,31,32]. Then, the best binding poses with respect to the Vina built-in scoring function were selected for presentation in this manuscript. PGP and docked ligands were visualized with PyMOL [33].
## 2.8. Statistical Analysis
Data were analyzed with GraphPad Prism software version 9.5.0. Values are presented as mean ± SEM. Ordinary one-way ANOVA were performed with uncorrected Fisher’s LSD with a single pooled variance. Multiple unpaired t-tests were performed with two-stage linear step-up procedure of Benjamini, Kieger, and Yekutieli. Significance is presented as follows: * $p \leq 0.05$; ** $p \leq 0.01$; *** $p \leq 0.001$; **** $p \leq 0.0001.$ EC50 values were determined by Nonlinear fit of dose-response stimulation (Equation: [Agonist] vs. response). The model for the equation is given as: Y = Bottom + X × (Top-Bottom)/(EC50 + X).
## 3.1. Acesulfame Potassium and Sucralose Impact the Expression of Detoxification Actors in Human Liver Cell Line
Our lab previously demonstrated that early-life chronic exposure to combined AceK and Sucr led to whitening in the livers of mouse pups and altered expression of phase II detoxification enzymes in the liver [6]. Furthermore, metabolic analysis confirmed the accumulation of intermediary metabolites not processed by phase II detoxification enzymes, leading to increased oxidative stress [6]. These findings suggested that NNS-fed pregnant mice gave birth to offspring with increased toxicity from inefficient liver detoxification. While this was the first indication that NNS might directly impact liver detoxification processes, the effects observed on the offspring’s liver might have been secondary consequences of NNS exposure on non-hepatic targets, such as significant changes found in their microbiomes. Thus, to validate the direct effects of NNS on liver toxicity, we investigated the direct exposure to NNS on liver cells to determine their impact on liver health and detoxification.
HepG2 liver carcinoma cells were treated with a mixture of AceK and Sucr at a concentration equal to plasma concentrations following ingestion of the FDA-approved acceptable daily intake (ADI) for each sweetener (AceK 2500 nM + Sucr 6000 nM), which replicates the amount of NNS exposure in plasma to mice fed NNS daily in the previous study [6] (Figure 1A). Expression of liver health and detoxification gene mRNA transcripts was measured by RT-qPCR and normalized against beta-actin (ACTB). NNS treatment for 24 h significantly decreased the expression of alcohol dehydrogenase 6 (ADH6) and alcohol dehydrogenase 7A1 (ALDH7A1), while increasing the expression of aspartate aminotransferase (AST) and ABCB1, the gene encoding P-glycoprotein (PGP), henceforth labeled ABCB1/PGP (Figure 1A). Interestingly, in rat colon, increased ABCB1/PGP expression was also reported following the sucralose diet [16].
Thus, we further investigated the impact of NNS on ABCB1/PGP expression in human liver-origin cells. HepG2 cells were treated with a combination of AceK and Sucr at concentrations found in human tissues after consumption of one diet soda (Table 1). Cells were treated for 24 or 72 h, and ABCB1/PGP transcripts were measured by RT-qPCR. ABCB1/PGP mRNA levels were significantly increased following combined NNS exposure at both timepoints (Figure 1B). We next examined whether treating HepG2 with NNS would consequently alter PGP protein levels. Because PGP has a long half-life (~27 h) [19], we treated HepG2 with combined AceK and Sucr for 72 h and probed PGP levels by Western blot. NNS treatment led to increased PGP levels (Figure 1C), consistent with reports of increased PGP levels following treatment with substrate drugs and inhibitors [34].
## 3.2. Acesulfame Potassium and Sucralose Inhibit Efflux of PGP Substrates
ABCB1/PGP expression is frequently altered in response to a change in function. Indeed, vinca alkaloid PGP substrates such as Vincristine have been shown to increase ABCB1/PGP expression in response to its competitive inhibition [35]. To understand whether NNS inhibit the efflux of PGP substrates, we performed fluorescence-based PGP inhibition assays (Figure 2A). Calcein-AM is a substrate for PGP which, under normal conditions, is continually effluxed out of cells by PGP in a cyclical fashion. When treated alongside a potent PGP inhibitor such as Verapamil, Calcein-AM is retained intracellularly due to competitive, non-competitive, or uncompetitive PGP inhibition. Cytosolic esterases within the cell then convert trapped Calcein-AM into its fluorescent metabolite Calcein, which cannot be transported by PGP. Thus, PGP inhibition is represented by an increased fluorescent signal.
As our primary focus is understanding the effects of NNS consumption on liver detoxification, hepatic cells were selected for our initial efflux inhibition studies. HepG2 cells were treated with a range of concentrations of individual AceK and Sucr, and Calcein retention was measured by the resulting fluorescent signal intensity. Verapamil, a calcium channel blocker and potent competitive inhibitor of PGP, was used as a positive control for PGP inhibition [36,37]. AceK and Sucr treatment led to significant increases in Calcein retention, thus indicating inhibition of Calcein-AM efflux (Figure 2B). Inhibition of Calcein-AM efflux was observed at AceK and Sucr concentrations as low as 300 and 750 nM, respectively. These are well within concentrations expected in human plasma after drinking one NNS-sweetened beverage (Table 1).
While HepG2 are a useful model for human liver cells, they express abundant levels of other related ABC transporters such as MRP2. Both Calcein-AM and Calcein are substrates for MRP2 [38,39,40], thus creating a doubt as to which transporters *Calcein is* expelled by in these cells and, thus, which transporter is inhibited by NNS. On the other hand, HEK-293 cells generated from a human embryonic kidney express high endogenous levels of PGP while exhibiting negligible levels of MRP2 [41]. Therefore, in these cells, Calcein efflux can be attributed to PGP activity. We treated both HepG2 and HEK-293 cells with a combination of AceK and Sucr and measured the fluorescent signal from Calcein retention (Figure 2C). Interestingly, Calcein retention was more pronounced in HEK-293 cells than in HepG2, suggesting specificity for NNS inhibition to PGP.
## 3.3. Sucr and AceK Stimulate PGP Efflux Activity
Many PGP inhibitors, including Verapamil, are also high-affinity substrates for PGP [24]. Thus, they prevent the efflux of other substrates, such as Calcein-AM, by binding PGP with higher affinities. The transport of these high-affinity competitive inhibitors requires ATP consumption, which catalyzes the conformational shift associated with the substrate efflux [42] (Figure 3A). PGP in the substrate-bound conformation is inward-facing, while the outward-facing conformation allows substrate release. Therefore, measuring ATP consumption by PGP is a common method for determining the binding kinetics of putative substrates and competitive inhibitors [43,44]. Interestingly, organochlorine compounds, the family that includes Sucr, are often PGP substrates [10]. AceK, however, as a highly water-soluble potassium salt, does not fit the typical profile for PGP substrates.
To verify if Sucr and AceK are substrates of PGP and act as competitive inhibitors, we measured PGP ATPase activity. Measurement of ATP consumption on artificial lipid vesicles exclusively containing recombinant human PGP provides a minimal system to assess PGP activity. Individual AceK and Sucr at a range of concentrations were incubated with PGP membranes. ADP produced by the hydrolysis of ATP by the ATPase domains of PGP was measured by ADP-Glo Max assay (Promega, Madison, WI, USA), which utilizes a luciferase/luciferin reaction to produce an ADP-dependent luminescent signal (Figure 3A). Both AceK and Sucr increased ATPase activity in a dose-dependent manner, with EC50 values for AceK and Sucr being 0.00023 nM and 0.056 nM, respectively (Figure 3B). Thus, AceK and Sucr are effluxed by PGP and are substrates of this transporter. We therefore demonstrated that AceK and Sucr are transported by PGP at concentrations within expected plasma levels after the ingestion of one NNS-sweetened beverage (Table 1), suggesting that they are competitive inhibitors of PGP.
We next confirmed that the inhibitory effect of Sucr and AceK on PGP was through competitive and not allosteric inhibition. If Sucr and AceK were also acting as allosteric inhibitors, their co-incubation with a competitive inhibitor such as Verapamil would lead to decreased PGP activity. Thus, AceK and Sucr were incubated with PGP membranes supplemented or not with 50 μM Verapamil. As expected, Verapamil alone significantly stimulates ATPase activity (Figure 3C). As previously observed, individual AceK and Sucr treatments stimulated PGP ATPase activity, confirming NNS transport in a dose-dependent manner. However, co-incubation of Verapamil with AceK or Sucr did not lead to significant changes in PGP activity relative to Verapamil alone, demonstrating that they do not inhibit PGP allosterically in the presence of potent substrates.
## 3.4. AceK and Sucr Show Unique PGP Binding Patterns In Silico
To further explore the interactions between NNS and PGP and elucidate their inhibitory mechanism, we performed virtual docking experiments of AceK and Sucr in the PGP transmembrane domain, which includes multiple cavities, i.e., pockets, in which substrates bind before efflux (Figure 4A, green box). Previously acquired crystal structures of PGP in complex with inhibitors, such as Vincristine, demonstrated that competitive inhibitors mainly bind in a flexible, hydrophobic pocket within the transmembrane domain [24,45] (Figure 4A, burgundy “pocket 1”). Docking of Verapamil was also performed as a positive control for high-affinity binding, and sucrose (table sugar) was also used in docking experiments as a compound structurally similar to sucralose. Indeed, sucrose lacks three chloride substitutions and is ideal for assessing the specificity of sucralose binding to PGP. As expected, Verapamil docked solely in the high-affinity binding pocket (Figure 4B and Figure 5A). AceK showed a lower preference for binding within the high-affinity pocket and docked in multiple locations within the transmembrane domain (Figure 4C and Figure 5B). In contrast, Sucr docking positions were mostly found within the same binding pocket as Verapamil (Figure 4D and Figure 5C). Furthermore, despite the high structural overlap, Sucrose showed no preference for PGP pockets (Figure 4E and Figure 5D), suggesting Sucr had a preference for the high-affinity binding pocket of PGP due to its chloride groups. A full report of docking positions, along with calculated metrics, including binding affinities and root mean square deviation (RMSD), can be found in Table S2. As expected, Verapamil showed the highest calculated binding affinity of all tested compounds, below −6.5 kcal/mol (Figure 5E). *Sucr* generally showed higher average calculated binding affinities for each pocket than sucrose or AceK (Figure 5E). Calculated binding affinities for AceK were relatively low across all regions in which it docked on PGP, which included two poses outside the transmembrane channel (Figure 5E).
Polar contacts for all docking poses were also inventoried for Verapamil, Sucr, and AceK (Figure 6). In agreement with the literature on PGP substrates [30,31,32], residues contacted by all compounds in common include Gln347, Gln725, Gln990, and Tyr310 (Figure 6I). The distribution of polar contacts in the PGP binding pockets between each compound is further illustrated in Venn diagram format (Figure 6J) and showed that in contrast to AceK, Verapamil and Sucr share most amino acid contacts with PGP.
## 4. Discussion
Initially characterized in 1976 [46], P-glycoprotein (PGP) is an essential mediator of drug metabolism and poses a challenge in treating cancers by causing multidrug resistance in tumors, which gives PGP its alternative name Multidrug Resistance Protein 1 (MDR1). Furthermore, PGP has many endogenous substrates and participates in the distribution of steroid hormones, biliary pigments, cytokines, and short-chain lipids [47,48,49,50]. Due to the substrate overlap with other members of the ATP-binding cassette transporter superfamily, deletion of PGP is non-lethal in animal models [51,52]. However, the absence of PGP, or mutations in ABCB1, the gene encoding PGP, can dramatically alter the pharmacokinetics of PGP substrates [53]. For example, because PGP is expressed at the maternal–fetal interface, mutations on the gene encoding PGP can lead to lower placental PGP expression and increased fetal exposure to drugs or environmental pollutants during pregnancy [54,55,56,57,58,59]. Similar effects can also be observed when PGP is pharmacologically inhibited. For instance, a higher incidence of birth defects was reported among infants born to mothers who took PGP substrate or inhibitor drugs during pregnancy, presumably resulting from higher fetal exposure to teratogens [60]. Additionally, altered PGP expression or PGP inhibition contributes to altered pharmacokinetics and drug–drug interactions [61]. In an era where many common prescription drugs are PGP substrates, including antidepressants (amitriptyline, citalopram, sertraline), antibiotics (levofloxacin, erythromycin), antivirals (saquinavir, ritonavir), and more [17,60,62], it is essential to know which compounds inhibit or stimulate PGP to prevent toxicity and improve treatment efficacy. Particularly, dietary compounds, such as non-nutritive sweeteners (NNS), have been suggested to be PGP substrates [10,35].
Commonly consumed in both Western and Asian countries, NNS are increasingly present in food, beverages, and other products that are marketed as “diet” or “zero” options, as well as many with no such indications [4,63,64]. By increasing palatability at a low cost and without raising overall calories, NNS are highly attractive to manufacturers. However, consumers are not able to reliably identify products that contain NNS and may be ingesting them regularly without knowing it [65]. It is estimated that over $40\%$ of the adult population in the United States consumes NNS [3]. Additionally, prescription drug use in the United States continues to rise, and many of these medications are PGP substrates. Prescription drug use includes about $13\%$ of adults in the United States taking antidepressants, over $80\%$ of patients in the United Kingdom or $40\%$ of patients in the United States with type 2 diabetes mellitus prescribed metformin, and up to $80\%$ of people in the United States taking an antibiotic prescription within the course of a year [66,67,68,69]. Therefore, the potential of drug–NNS interactions is intriguing.
Amongst the NNS that are approved for consumption by the Food and Drug Administration (FDA) and the Joint FAO/WHO Expert Committee on Food Additives (JECFA), sucralose (Sucr) and acesulfame potassium (AceK) are now used more frequently due to a decline in popularity for earlier sweeteners such as saccharin and aspartame. Along with their predecessors, these two NNS have also come under scrutiny for their potential health risks. A large population-based study recently linked high rates of AceK consumption with increased risks of breast and metabolic-related cancers [70]. Previously, sucralose has been linked to both dysregulation of the microbiome and increased PGP levels in the large intestine of rats [16]. Further study into the impact of NNS on PGP was necessary to elucidate the mechanisms for this interaction. Importantly, for the first time, we investigated the effects of combined AceK and Sucr on PGP, which more closely replicates real-world exposures from food and beverage products that frequently contain combined sweeteners (Gatorade G2, Powerade Zero, Diet Mountain Dew). Furthermore, our experiments modeled the exposure to AceK and Sucr in simplified systems, showing a direct link between NNS and PGP dysregulation. Our results also showed that longer NNS exposure times (~24–72 h) increased ABCB1/PGP expression, while acute exposure (~30 min) caused competitive inhibition of PGP efflux function. This shows that NNS consumption may have unexpected consequences even in short-term situations such as drinking an occasional diet soda.
A recent study from our lab also suggested the potential inhibition of PGP by NNS [6]. Mouse pups from mothers fed NNS during pregnancy and lactation showed decreased plasma levels of endogenous ATP-binding cassette transporter substrates, including PGP substrates bilirubin and biliverdin [6]. This suggested a failure of hepatic PGP to regulate proper dispositions of unconjugated bile pigments. We posit that the NNS exposure from the mothers’ diets inhibited natural PGP efflux, leading to the accumulation of PGP substrate compounds and liver toxicity, evidenced by metabolomics data and gross whitening of the pups’ livers. Due to the design of this previous study, however, we were not able to make this claim conclusively. The NNS exposure in the study in mice was not explicitly targeted to the liver, and indeed, significant dysregulation of pups’ microbiome was also revealed, preventing us from concluding that NNS actions on hepatic PGP alone led to the liver dysfunction. Additionally, those experiments only used mixed AceK and Sucr, preventing the determination of the role of individual NNS in PGP deregulation. Finally, NNS exposure in mice began at conception and continued through lactation, a period totaling roughly 40 days. As such, the effects of such long-term exposure on hepatic PGP may have extended beyond acute functional inhibition. This initial evidence that NNS might not only bind to PGP but also impact its activity was validated in the current study.
Here, we opted for a more targeted approach to directly assess the impact of NNS on PGP in liver cells and demonstrated that concentrations of AceK and Sucr found in human plasma after drinking one NNS-sweetened beverage competitively inhibit PGP function. These effects would be present both when consumed individually (e.g., Splenda sweetener packets—Sucr only, Diet Coke—AceK only) or combined (e.g., Gatorade G2, Powerade Zero, Diet Mountain Dew). Despite a lack of evidence for the safety of combinations of food additives, NNS are frequently combined in food and beverage products because of their complementary effects on taste. A deeper exploration of NNS combinations is therefore warranted and is especially relevant in the detoxification pathways, which utilize transporters with wide substrate recognition.
Prior to this study, Sucr had already been suggested to be a substrate of PGP due to its structure as an organochlorine molecule, many of which are PGP substrates [10]. Our study confirmed Sucr as a substrate through functional assays and in silico docking experiments and emphasizes that Sucr enters the transmembrane channel of PGP and binds within efflux-stimulating substrate pockets. Sucr docks mainly in the same high-affinity, high-turnover pocket as Verapamil, a known potent PGP substrate. AceK, a structurally dissimilar NNS not previously implicated as a PGP substrate, shows similar impacts on PGP function as Sucr experimentally. AceK effectively inhibits Calcein-AM efflux and stimulates PGP ATPase function at concentrations equal to or lower than Sucr, respectively. In docking experiments, AceK shows more promiscuous interactions within the transmembrane region while still being capable of making polar contacts with key amino acid residues that trigger efflux. The binding of Sucr and AceK into PGP suggests that they might compete with PGP substrates and act as inhibitors at low picomolar concentrations.
Based on our cellular efflux assay, we imagine that when AceK and Sucr are consumed along with PGP substrate medications, they may be preferentially effluxed by PGP, leading to the retention of other PGP substrates, altered drug distribution, and increased cellular toxicity. As novel PGP inhibitors, it is crucial to determine the extent of the NNS–drug interactions to understand how they may impact pharmacological therapeutic interventions throughout the body. This is not only critical in the liver, but in all tissues expressing PGP. The activity of PGP at blood–tissue barriers has been studied extensively, particularly at the intestine, liver, kidneys, and blood–brain barrier. Unaccounted functional inhibition of PGP at any of these sites could lead to higher rates of drug exposure than intended and compromise therapeutic efficacy [71,72,73]. Adverse effects of such drug-drug and drug–food interactions have been documented for decades, including prior to the identification of PGP as the transporter responsible for this phenomenon [74,75]. For example, increased plasma levels of orally administered digoxin or fexofenadine were observed after co-administration of Verapamil, a calcium channel blocker used as an anti-hypertensive and now widely known as a PGP competitive inhibitor [36,74,75]. Many PGP-mediated food–drug interactions are known, including the disruption of PGP substrate drug uptake by components of grapefruit juice and soybean products [71,72,73,76]. Although knowledge of such interactions is available to prescribing physicians, a recent study reported that patient counseling may be inadequate [77]. Risks of interactions with foods and herbal supplements may be greater as they are freely available without prescriptions. Herbal remedies are more likely to be considered “safe” by consumers, despite potentially having multiple biologically active compounds [78]. To our knowledge, our studies are the first to show that the NNS AceK and Sucr may belong among this group of foods that interact with PGP substrate drugs. Further study is necessary to uncover the extent of the potential of AceK and Sucr to interfere with the expected absorption and distribution of PGP substrate drugs. This will be of particular interest to populations that may have a higher risk of exposure to both NNS and PGP substrate drugs. In Western countries, women and people with type 2 diabetes are known to consume NNS at higher rates [3]. Among people taking PGP substrate drugs, women are more likely to take certain antidepressants [66] or metformin for reproductive conditions [79].
While our experiments present initial evidence of AceK and Sucr deregulation of PGP, some limitations prevent drawing conclusions about clinical risks at this time. Our in vitro experiments on ABCB1 and PGP expression utilized HepG2, an established cell line of human liver cancer origin. While HepG2 cells endogenously express functional PGP, primary human hepatocytes or more advanced model systems such as organoids would provide important physiological evidence of NNS deregulating PGP expression in healthy liver tissue. Furthermore, our experiments did not test the effects of NNS on the efflux of other PGP substrate drugs. Thus, future experiments should assess the impact of NNS on the transport of PGP substrate drugs in vitro and in vivo, ideally in multiple tissue types where PGP plays a critical role in drug distribution (e.g., intestine, blood–brain barrier, placenta) [17]. This will improve our understanding of how dietary NNS exposure could interfere with therapeutic interventions. Future studies should address whether the dietary consumption of AceK and Sucr pose challenges in complex health conditions where PGP activity is relevant such as advanced liver diseases or certain cancers. Furthermore, future work should investigate the impacts of NNS on PGP-mediated drug transport during pregnancy, where PGP plays a critical role in protecting the developing fetus from toxic exposures [60,80,81].
## 5. Conclusions
Using a combination of human hepatic- and kidney-origin cells, cell-free biochemical assays, and molecular docking, we have demonstrated that sucralose and acesulfame potassium act as competitive inhibitors of P-glycoprotein at concentrations as low as levels found in human plasma after drinking one non-nutritive sweetener (NNS)-sweetened beverage. Thus, previous findings from NNS-fed mice and our present results support a role for NNS in impairing proper PGP function and cellular detoxification. Due to the crucial role of PGP in various tissues, including drug absorption, xenobiotic defense and clearance, and distribution of endogenous substrates such as steroid hormones and cytokines, it is imperative to fully characterize the consequences of sucralose and acesulfame potassium exposures among target populations. Future work must elucidate the impact of these popular NNS on the absorption and clearance of common PGP substrate drugs in relevant tissue types, including the intestine, blood–brain barrier, and placenta. Clinical studies will reveal whether NNS consumption poses additional risks to patients prescribed PGP substrate drugs. By enhancing our fundamental understanding of the biological effects of NNS, we hope to improve the guidelines for food consumption during pharmacologic interventions and prevent harmful behaviors in patients.
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|
---
title: Development of an HPLC-PDA Method for the Determination of Capsanthin, Zeaxanthin,
Lutein, β-Cryptoxanthin and β-Carotene Simultaneously in Chili Peppers and Products
authors:
- Jiayue Xu
- Jialu Lin
- Sijia Peng
- Haoda Zhao
- Yongtao Wang
- Lei Rao
- Xiaojun Liao
- Liang Zhao
journal: Molecules
year: 2023
pmcid: PMC10005789
doi: 10.3390/molecules28052362
license: CC BY 4.0
---
# Development of an HPLC-PDA Method for the Determination of Capsanthin, Zeaxanthin, Lutein, β-Cryptoxanthin and β-Carotene Simultaneously in Chili Peppers and Products
## Abstract
For the better standardization and widespread application of the determination method of carotenoids in both chili peppers and their products, this work reports for the first time the simultaneous determination of five main carotenoids, including capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene in chili peppers and their products, with optimized extraction and the high-performance liquid chromatography (HPLC) method. All parameters in the methodological evaluation were found to be in good stability, recovery and accuracy compliance with the reference values; the R coefficients for the calibration curves were more than 0.998; and the LODs and LOQs varied from 0.020 to 0.063 and from 0.067 to 0.209 mg/L, respectively. The characterization of five carotenoids in chili peppers and their products passed all the required validation criteria. The method was applied in the determination of carotenoids in nine fresh chili peppers and seven chili pepper products.
## 1. Introduction
The production of chili peppers (*Capsicum annuum* L.), which are commonly grown as spice vegetables, reached 38.027 million tons in 2019 and climbed by $28.12\%$ from 2010 [1]. A fresh chili pepper’s high concentration of vitamin C and E, calcium, phosphorus, iron, carotenoids and capsaicin made it the “greatest nutritional vegetable” [2], and chili products are widely available in the market and in commerce. Additionally, due to high added value, deep-processed chili products (such as carotenoids, capsaicin, etc.) have received a lot of attention with the rapid development of active ingredient extraction technology [2,3]. For instance, the carotenoids found in chili peppers, primarily capsanthin, capsorubin, β-carotene, zeaxanthin, β-cryptoxanthin, violaxanthin, lutein, and antheraxanthin [4] all have high biological activity, which has been thoroughly investigated. The primary carotenoid in chili peppers, capsanthin, is the most widely used pigment in nature. It has numerous health benefits for humans, including antioxidant properties, cancer- and radiation-fighting properties, immune modulation, regulation of body lipid metabolism, and the molecular prevention and treatment of chronic cardiovascular diseases [5,6]. Finding the added-value components, such as the primary carotenoids in chili peppers and their products, has become more important recently. However, the majority of investigations concentrated on the determination of carotenoids in either fresh chili peppers or chili pepper products [7,8,9]. There was no study that could be used to influence the trade and industry of chili peppers and their products by simultaneously determining the carotenoids in them. Additionally, the existing techniques for extracting and determining the carotenoids in chili peppers or chili pepper products were more difficult and inconvenient [10]. Therefore, it is required to develop a single practical extraction measurement technique for the carotenoids in chili pepper and their products.
Recent studies have focused on the five primary processes that go into determining carotenoids: sample pretreatment, extraction, saponification, separation and identification. To increase the effectiveness of extraction, sample pretreatment included sample drying (sun drying, air drying, freeze drying, osmotic dehydration and chemical dehydration) [11,12,13]; there are two types of extraction: traditional solvent extraction and new solvent extraction. The latter is more eco-friendly and effective and uses techniques such as microwave-assisted extraction [14], ultrasound-assisted extraction [15], super-critical fluid extraction [16] and enzyme-assisted extraction [17]; saponification is utilized in an alkaline solution to increase the quantification of carotenoids after eliminating chlorophyll and esterified lipids [18]; and high-performance liquid chromatography (HPLC) with a photo-diode array (PDA) detector is a popular and effective separation technique [19]. Mass spectrometry is employed as a detector to provide more precise information about the extract’s structure while correctly identifying each carotenoid [20].
This study focused on optimizing and establishing a practical method for the simultaneous determination of five main carotenoids, including capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene, in industrialized and natural chili pepper and its products using HPLC. The method was validated by linear range, limit of detection and limit of quantification, stability, recovery and accuracy. This study also described the five carotenoids in nine fresh chili peppers and seven commercially available chili pepper products.
## 2.1.1. Wavelength
The wavelengths of capsanthin, zeaxanthin, lutein, β-cryptoxanthin, and β-carotene in the 400–500 nm range were scanned, as shown in Figure S1. In 400–500 nm, the absorbance of capsanthin, β-carotene, zeaxanthin and lutein had a large absorption at around 450 nm, and β-cryptoxanthin had a large absorption at 470 nm, which was different from the maximum absorption wavelengths of the other four carotenoids and the theoretical maximum absorption wavelength, which might be related to the purity of the standards. In summary, combining the results of the current studies, the optimal wavelength was determined to be 450 nm [21,22].
## 2.1.2. Mobile Phase Gradient
In Figure S2, the chromatograms of capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene using six mobile phase gradient methods are shown. Resolution (R) was used to describe the degree of separation of different carotenoids: $R = 1.0$ instructed that the degree of separation could reach $98\%$; usually R ≥ 1.5 instructed that components could be completely separated [23,24]. Comparing several methods utilizing a five mobile phase gradient, method 6 was able to effectively separate the five carotenoids, with the R-value of β-cryptoxanthin being 1.49 and the R-values of the other four carotenoids being >3.5. The target peaks were all sharp and symmetrical, and there were no consecutive peaks, trailing peaks or forward peaks. They were evenly distributed in the middle part of the chromatogram, with high response values. The analysis time was appropriate and there were no solvent peaks and spurious peaks in the chromatograms. The results demonstrated that gradient 6 was ideal in terms of compatibility and peak generation, which were significant characteristics previously highlighted in other studies [7]. Additionally, a comparable gradient was examined in another study with apparent separation, but the targeted peak response was lower than our results and the running time was approximately twice as long as that in our study, further demonstrating the superior efficiency and advanced nature of our method [25].
## 2.1.3. Mobile Phase
As shown in Figure S3, when the mobile phase was acetone–water, five carotenoids were well separated, with sharp and symmetrical target peaks and high response values. The analysis time was less than 18 min when the targeted peaks were positioned in the middle with improved separation. When the mobile phase was MeOH–water or acetonitrile–water, only β-cryptoxanthin and β-carotene were separated and the chromatogram had spurious peaks, and the peaks of capsanthin, zeaxanthin and lutein had incredibly low response values. Zeaxanthin, lutein and capsanthin could not be separated from each other when the mobile phase was tetrahydrofuran–water. As a result, acetone–water was the optimal mobile phase, which was also used in other studies to determine carotenoids in vegetables [26], but the running time for a comparable study to determine the content of carotenoids in chili pepper products was about 35 min, which was twice as long as our time [25].
## 2.1.4. Flow Rate of Mobile Phase
According to Figure S4, all peaks were sharp and symmetrical with high response values under the three flow rates of the mobile phase. At the flow rate of 0.5 mL/min, the R-value of zeaxanthin and luteolin was 1.34, with a minor internal peak overlap and late peak appearance. At a flow rate of 1.0 mL/min, compared to a flow rate of 1.5 mL/min, the peak areas of the five carotenoids were significantly higher. In summary, 1.0 mL/min was selected as the mobile phase flow rate, which was also set as the typical fluid rate in earlier studies [9].
## 2.1.5. Column Temperature
Carotenoids are extremely sensitive to temperature due to their linear and inflexible structure [27]. As shown in Figure S5, the shapes of the target peaks were sharp, symmetrical, and had high response values at each of the three column temperatures. Zeaxanthin and lutein had a slightly worse R-value of 1.47 at the 25 °C column. In addition, the analysis time was somewhat shortened with the rise in column temperature. The best column temperature was determined to be 30 °C in order to maintain the stability of carotenoids. In the study of Wissanee Pola [28], the greater carotenoid contents accumulated at 30 °C combined with considerably evaluated genes related to carotenoid biosynthesis, indicating that 30 °C would be the proper temperature for more carotenoid detection in HPLC.
In conclusion, the optimal HPLC conditions for determining five carotenoids in chili pepper (spectrum results as shown in Figure 1) were a column temperature of 30 °C, a detection wavelength of 450 nm, a sampling volume of 10 μL, a flow rate of 1 mL/min and mobile phases of acetone (solvent A) and water (solvent B), with an elution gradient of 0–5 min, $75\%$ A; 5–10 min, 75–$95\%$ A; 10–17 min, $95\%$ A; 17–22 min, 95–$100\%$ A, 22–27 min, 100–$75\%$ A.
## 2.2. Extraction Condition Optimization
Carotenoid extraction would be affected by improper extraction parameters. As shown in Figure 2, we investigated the optimal extraction methods by single-factor experiments to obtain a higher content of carotenoids. [ 1] Acetone/anhydrous ether (1:1, v/v, mixed solvent A) was selected as the extraction solvent because of its higher extraction efficiency, which was consistent with study [29]. In particular, acetone/anhydrous ether (1:1, v/v, mixed solvent A) provides better extraction of capsanthin, which was most important in red chili peppers. [ 2] At 20 °C, the extraction effect was at its weakest, likely as a result of the extraction and penetration effect of the extraction solvent being ineffective at such a low temperature [30]. As previously indicated, higher temperatures (>50 °C) could cause the carotenoids to decompose, which is why the content of carotenoids significantly decreased between 40 °C and 60 °C [27]. Additionally, the carotenoid content showed no impact at extraction temperatures of 30 °C, 40 °C and 50 °C, and the content was somewhat better at 40 °C. Finally, 40 °C was decided upon as the extraction temperature. According to the study of Merve Cican [31], β-carotene with higher contents was extracted at 40 °C for 10 to 20 min. [ 3] Moreover, 20 min was selected as the optimal extraction time, when the carotenoid content was highest and the extraction time was relatively low compared to others [8]. [ 4] Carotenoids are generally stable in an alkaline environment. Hence, it is advantageous for HPLC analysis to saponify with alkaline solutions following sample extraction to successfully eliminate hydrolyzed carotenoid esters [11]. With an increase in KOH-MeOH concentration, the content of capsanthin and zeaxanthin increased to its highest, at $20\%$ KOH-MeOH, and then decreased; the content of lutein decreased slowly at low alkali concentration and then increased rapidly to be extracted completely when KOH-MeOH was $40\%$; while the extraction effect of β-cryptoxanthin and β-carotene was not greatly affected by the alkali concentration. Comprehensively, $20\%$ was the optimal KOH-MeOH concentration, where there was the highest content of capsanthin (the main carotenoid). Additionally, in other studies [8,9], capsanthin was extracted from chili peppers using $30\%$ or $40\%$ KOH-MeOH without further optimization, and the targeted peaks of lutein and zeaxanthin were so small that they were challenging to detect [8]. [ 5] Heating could accelerate the saponification reaction, improve extraction efficiency and reduce carotenoids loss, and 25 °C was selected as the saponification temperature since carotenoids, such as capsanthin and zeaxanthin, were heat-sensitive and decomposed extremely at temperatures of 50 °C to 80 °C. [ 6] Then, 60 min was determined to be the optimal saponification time when the carotenoid content was at its highest; this was less than that of another study, which used 12 h to soap [8]. As the saponification time increased, the carotenoid content first increased rapidly (20–60 min) and slowly decreased over the course of 60–90 min. This indicates that the saponification is complete at 60 min and the reaction tends to be completed. The degradation of chili pepper carotenoids could be induced by either a higher temperature or a longer heating time.
## 2.3. Methodological Evaluation
Table S2 displays the repeatability test results during four sessions of three groups of mixed standard samples at various concentrations throughout a 14-day period. The RSD% of retention times of capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene were 0.04~$0.22\%$, 0.05~$0.15\%$, 0.05~$0.29\%$, 0.07~$0.17\%$ and 0.03~$0.08\%$, respectively, which were lower than $0.3\%$. The RSD% of peaks areas of capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene were 0.16~$1.46\%$, 0.10~$0.75\%$, 0.31~$1.39\%$, 0.30~$0.89\%$ and 0.14~$1.01\%$, respectively, which were lower than $1.5\%$. The results were all lower than those in other studies that determined carotenoids in chili pepper or other vegetables [32,33], which showed that the apparatus and samples were stable among different measurements in a period of time, which was necessary for the validation of the simultaneous HPLC method.
Each carotenoid was subjected to a linear regression analysis using the standard area value. As shown in Table 1, the R coefficients for the calibration curves of the five carotenoids were greater than 0.998, and the concentrations of capsanthin, zeaxanthin, lutein, β-cryptoxanthin, β-carotene standard solutions, measured in the test, all had good linear relationships within the range of 0.1 to 50 mg/L.
The five carotenoids used in the method had LODs and LOQs that varied from 0.020 to 0.063 and 0.067 to 0.209 mg/L, respectively. The LODsample and LOQsample of fresh and dried samples of chili peppers and their products are shown in Table 1 for samples weighing 1 g. The LODsample of fresh chili peppers ranged from 0.497 to 1.565 mg/kg, whereas that of dried, fried, and fermented chili peppers was between 0.500 and 0.875 mg/kg, 0.101 and 0.317 mg/kg, and 0.100 and 0.314 mg/kg, respectively. The LOQsample ranged from 1.664 to 5.191 mg/kg for fresh chili peppers, 1.675 to 5.225 mg/kg for dried chili peppers, 0.443 to 1.052 mg/kg for fried chili peppers, and 0.334 to 1.042 mg/kg for fermented chili peppers. The LOD and LOQ values were all lower than those in study [34], which proved the superiority of our method with HPLC combined with PDA. For obtaining lower LOD and LOQ, HPLC could be combined with MS, a typical detector with a lower detection limit. For example, the LOD and LOQ of carotenoids in kumquat were 0.07 and 0.22 ppm for β-carotene, 0.1 and 0.33 ppm for β-cryptoxanthin, 0.06 and 0.18 ppm for lutein, 0.08 and 0.3 ppm for zeaxanthin, respectively, obtained by HPLC-DAD-APCI/MS [35].
The five carotenoids were recovered in fresh chili peppers, dried chili peppers, fried chili sauce and fermented chili sauce at rates ranging from 87.80 ± $1.26\%$ to 107.47 ± $3.77\%$, and more than half of the samples were recovered at 100 ± $5\%$ (Table S3). The samples of dried and fermented processed chili that had fluctuating recoveries may have been affected by the structural composition of the substrate or the influence of food processing technology. The studies of Berhan et al. [ 33], in determination of lutein, zeaxanthin, α-carotene, β-carotene and β-cryptoxanthin in soybean flour samples by HPLC, achieved sample recoveries from 83.12 to $106.58\%$, which also indicated that the recoveries in processed samples varied somewhat from $100\%$.
To further support this new method’s great accuracy, the intraday and interday RSDs, measured by the new method within three days, are shown in Table S3 and ranged from 0.08 to $2.27\%$ and 0.89 to $7.54\%$, respectively. These values were both lower than $10\%$ [36]. What is more, because of the chili pepper processing method and uneven sampling, the interday RSDs fluctuated slightly significantly more than intraday RSDs, which were also found in other studies [36].
## 2.4. Application of the Method to Fresh Chili Peppers and Chili Pepper Products Analysis
The following four kinds of chili peppers and products were used to validate the improved extraction and HPLC method: fresh chili peppers from Henan, China, dried chili peppers; fried chili sauce; and fermented chili sauce. As shown in Figure 3, the retention times of five carotenoids in samples and standards were identical, and the peaks of each carotenoid were easy to identify in samples thanks to their sharp, symmetrical shapes and high response levels devoid of base substance interference. With strong separation and specificity, the optimized extraction and HPLC method were able to simultaneously determine the capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene in chili pepper and its products.
Furthermore, the optimized and validated HPLC method was applied for the determination of five carotenoids in nine fresh chili peppers, as shown in Figure 4. Although the contents of the five carotenoids in nine varieties of chili peppers were different, the capsanthin and zeaxanthin accounted for about $70\%$ in red chili peppers, which makes red chili peppers good materials to extract capsanthin. Additionally, because different chili pepper cultivars contain variable contents of carotenoids and capsanthin, they can be used in various processing methods. For example, the capsanthin contents of “Tianse 2016” and “Tianxian 1934” were ranked as the top two; they had capsanthin contents 2.82 and 1.63 times greater than the average capsanthin content in nine samples, respectively, indicating that they are more likely to extract capsanthin. Similarity, the varieties of long and line chili peppers have fewer chili seeds and more chili meat, which was beneficial for extracting more carotenoids without the disturbance of chili seeds. Regarding other varieties of long, line or upright chili peppers with less carotenoids, they can be consumed fresh or made into items such as dried chili peppers and fermented chili sauce. As a result, one of the key criteria for evaluating the effectiveness of chili peppers as extraction materials was their content of carotenoids or capsanthin.
What is more, as shown in Figure 4, the optimized and validated HPLC method was applied for the determination of five carotenoids in seven common chili pepper product samples, including dried chili peppers, dried chili powder, hotpot chili sauce, oiled chili, fried chili sauce, chili oil and fermented chili sauce. The RSDs were 0.81–$7.67\%$ (<$10\%$), indicating good accuracy in the results. Additionally, it was discovered that the carotenoid content was dependent on chili pepper varieties and processing methods. Dried chili peppers had more carotenoids because drying was a method that enriched carotenoids by reducing the water in chili peppers. However, dried chili powder’s carotenoid content was lower than that of dried chili peppers because it was processed along with other food such as peppers. Furthermore, oil was used in the processing of oiled chili, fried chili sauce and hotpot chili sauce, which increased the content of carotenoids in those products because carotenoids were easily dissolved in the oil phase [25]. However, the carotenoid content of chili oil, in contrast to oiled chili and other fried chili sauces, was reduced since it was treated with chili peppers and then filtered. As for fermented chili sauce, the long processing time, acid environment and water phase all affected carotenoid stability and dissolution, so the carotenoid content was lowest in fermented chili sauce [25].
## 3.1. Reagents and Materials
Capsanthin (purity $99\%$) was purchased from Sigma Aldrich (St. Louis, MO, USA). Zeaxanthin, lutein, β-cryptoxanthin and β-carotene (purity $99\%$) were purchased from Yuanye Bio-Technology (Shanghai, China). HPLC-grade tetrahydrofuran, acetonitrile, methanol (MeOH), acetone, methyl tert-butyl ether (MTBE) and isopropanol were purchased from Thermo Fisher Scientific (Waltham, MA, USA). Analytical-grade N-hexane, MeOH, ethanol, acetone, ethyl acetate, petroleum ether and anhydrous ether were purchased from the Sinopharm Group (Beijing, China). Potassium hydroxide (KOH) was purchased from Lanyi Chemical Company (Beijing, China). Ultrapure water was generated by a Milli-Q integrated water purification system (Millipore, Billerica, MA, USA).
Nine varieties of fresh and red chili peppers, which were selected to be investigated because of the higher and richer carotenoids, were supplied from Comprehensive Experiment Stations of China Agriculture Research System, which were located in Henan, Yunnan, Gansu, Guizhou, Neimen, Shandong (six provinces in China). Dried chili peppers, chili oil, fried chili sauce and fermented chili sauce were all purchased in a market (China).
## 3.2. Apparatus and Software
The weighing experiments were conducted with an analytical scale (BSA 224S-CW, Sartorius, German). Sample pre-treatments were carried out with beater (L18-Y68, Jiuyang Company, Jinan, China), grinder (F203, Krups, Solingen, Germany) and rotary evaporator (Hei-VAP Expert HL/G3, Stinson Technology Company, New York, NY, USA). Sonication treatments were carried out with an ultrasonic bath (PS-40A, Jintan Liangyou Instruments Company, Changzhou, China) of 40 kHz frequency. The centrifuge was Avanti JXN-30 (Beckman Coulter Company, Brea, CA, USA). Wavelength scanning was performed with a UV spectrophotometer (Lambda, PerkinElmer, Woburn, MA, USA).
HPLC determination was performed with an HPLC system equipped with a quaternary pump, degasser membrane, thermo-stated column compartment, autosampler, a PDA detector and the analytical software, Empower 3.0 (Waters e2695, Milford, MA, USA). The HPLC column was a Spherisorb ODS-2 C18 (250 mm × 4.6 mm, 5 μm) from Agela (Jinan, China).
## 3.3. Standard Solutions and Samples
Stock solutions of 1 mg/mL of capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene were prepared by weighing the corresponding mass and subsequently dissolving in anhydrous MeOH to 1 mL. Mixed stock solutions of 100 mg/L were prepared by mixing 100 μL of each standard stock solutions, respectively, and diluting with MeOH. The above solutions were stored below −20 °C and used as soon as possible. Standard solutions were filtered through a 0.22 μm PTFE membrane before the HPLC determination.
One kilogram of fresh chili was washed, diced, and uniformly combined. It was then separated into four equal parts, and two portions (in total around 500 g) were chosen to be mashed into homogeneous paste with a beater; dried chili peppers were ground into a powder (≥40 mesh) with a grinder; chili oil, fried chili sauce and fermented chili sauce were used directly and mixed thoroughly. The above samples were placed in a clean, sealed bag and stored at −20 °C.
## 3.4.1. Optimized Parameters
The standard of the China GB/T 21266-2007 [37], NY/T 1651-2008 [38] and GB 5009.83-2016 [39] methods were used, and modifications were conducted. One gram of each sample (0.2 g chili powder sample) was placed in a centrifuge tube, and then 25 mL acetone and 25 mL anhydrous ether were added and mixed thoroughly. The centrifuge tube was sonicated for 20 min at 40 °C/50 W and centrifuged at 10,000 g/4 °C for 5 min to obtain the supernatant. The precipitate was thoroughly mixed with 25 mL of acetone and 25 mL of anhydrous ether before being extracted again. The supernatants were collected together twice.
Then, 50 mL of KOH-MeOH ($20\%$, w/v) was added to the combined supernatant, shaken well and allowed to rest for 1 h at room temperature (shaken 2~3 times for 30 s each time during resting to ensure sufficient saponification). The saponified solution’s aqueous phase was eliminated, and the organic phase was then rinsed with distilled water until it reached a pH level that was neutral. The saponified solution was then evaporated using a rotary evaporator to dryness at ≤35 °C. Before HPLC analysis, dry material was dissolved with acetone to 5 mL, which was then filtered through a 0.22 μm PTFE membrane into a brown liquid phase vial and stored at −20 °C. Sample solution dilution times were adjusted based on the individual samples so that the sample concentration was located in the middle of a standard curve.
Different extraction parameters were studied, including the solvent selection (MeOH, ethanol, acetone, N-hexane, acetone/anhydrous ether (1:1, v/v, mixed solvent A), MeOH/ethyl acetate/petroleum ether (1:1:1, v/v/v, mixed solvent B)), extraction temperature (20, 30, 40, 50 and 60 °C), extraction time (10, 20, 30, 40 and 50 min), KOH- MeOH concentration ($10\%$, $20\%$ and $40\%$), saponification temperature (25, 50 and 80 °C), and saponification time (20, 60 and 90 min).
## 3.4.2. One Factor Design
Through single-factor experiments, the parameters were optimized by analyzing the separation of peaks, peak areas, response values and the retention times of capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene in dried chili samples. To rule out the interference of solvent peaks, blank trials were conducted in the interim.
## 3.5.1. Optimized Parameters
The primary HPLC parameters were as follows: column temperature was 30 °C, detection wavelength was 450 nm, sampling volume was 10 μL, flow rate was 1 mL/min and mobile phases were acetone (solvent A) and water (solvent B), with elution gradients of 0–5 min, $75\%$ A; 5–10 min, 75–$95\%$ A; 10–17 min, $95\%$ A; 17–22 min, 95–$100\%$ A, 22–27 min, 100–$75\%$A.
The optimization of HPLC parameters was studied, including column temperature (25, 30, 35 °C), flow rate (0.5, 1, 1.5 mL/min), wavelength (400–500 nm), mobile phase A (MeOH, tetrahydrofuran, acetonitrile and acetone), and the elution gradients of mobile phase A (acetone) and mobile phase B (water) (Table S1).
## 3.5.2. One Factor Design
The wavelength at which all the five standards (diluted standard stock solution using acetone into 10 mg/L) had greater absorption was selected as the detection wavelength. Other parameters were optimized by analyzing the separation of peaks, peak areas, response values and retention times of capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene in mixed standard solution (diluted mixed standard stock solution using acetone into 10 mg/L) through single-factor experiments. To rule out the interference of solvent peaks, blank trials were conducted in the interim.
## 3.6. HPLC Determination
The content of each carotenoid (Xi) in the sample was expressed in mg/kg was calculated as following: Standard curve calibration, by [1]As=aCs+b *Obtain a* and b, then [2]C=A−A0CsaCs+b The content of each carotenoid Xi in the sample was calculated by mass fraction in mg/kg, calculated according to formula as below:[3]Xi=C×V×fm×11000
Type:Xi—the content of each carotenoid in the sample, μg/g.A—peak area of each carotenoid in the sample solution. A0—peak area of each carotenoid in the sample dilution solution. As—peak area of each carotenoid in the standard solution. Cs—concentration of each carotenoid in the standard solution, mg/L.C—concentration of each carotenoid in the sample solution, mg/L.V—extract liquid in mL.f—dilution ratio of sample solution.m—mass of sample in g.
Two significant digits were retained for the determination.
## 3.7. Validation Procedures
According to the European Normative, the method was validated by a validation model in accordance with decision $\frac{2002}{657}$/EC [40].
For repeatability, mixed standard stock solution was diluted into 1, 5, and 20 mg/L by acetone, which was measured four times over 14 days. Then, the retention times and peak areas were compared among the measurements of four sessions.
For linearity analysis, mixed standard solutions at concentrations of 0.1, 1, 2, 5, 10, 20, and 50 mg/L were prepared by dilution in acetone. These were injected from a low concentration and followed by analysis and the establishment of standard curves. Linear regression was accomplished by taking the peak area Y as the ordinate and the concentration X as the abscissa. A linear equation was developed by plotting the standard curve of each carotenoid. The standard curves were evaluated based on the linear regression equation and the R coefficients for the calibration curves.
For limits of detection and quantification (LODs and LOQs) analysis, mixed standard solutions at concentrations of 0.01, 0.02, 0.05, 0.10, 0.20, 0.50, and 1.00 mg/L were prepared by dilution in acetone. The LODs and LOQs of the method and LODsample and LOQsample of the test sample were, respectively, calculated as LODs = [(W × 3) × 100]/[(S/N) × 10−6][4] LOQs = [(W × 10) × 100]/[(S/N) × 10−6][5] LODsample = [(W × 3) × V × f × 100]/[(S/N) × m × 10−6][6] LOQsample = [(W × 10) × V × f × 100]/[(S/N) × m × 10−6][7] Type:W—detection of ion concentration, mg/L.S/N—instrument signal to noise ratio.100—conversion factor.10−6—conversion factor. V—constant volume of sample, mL.f—sample dilution ratio.m—sample weight, g.
For recovery and accuracy analysis, we selected four samples of fresh chili peppers, dried chili peppers, fried chili sauce and fermented chili sauce. The detailed experimental steps were: four sample extracts were diluted with acetone to below the LODs, and then the four dilutions were used to dilute the mixed standard stock solutions to 1, 5 and 20 mg/L. For each level, the same apparatus and operators were used for six repetitions ($$n = 6$$) on three consecutive days. Recovery was calculated using the following formula:[8]Recovery %=X1−X0m×$100\%$
## 3.8. Application of the Method to Fresh Chili Peppers and Chili Pepper Products Analysis
The optimized and validated simultaneous HPLC method was applied for the determination of five carotenoids in nine fresh chili peppers and seven common chili pepper products. An in-depth comparison was conducted by analyzing the carotenoid content of samples of fresh chili and chili pepper product samples.
## 3.9. Statistical Analysis
All experiments were conducted in triplicate, and the results were expressed as means ± standard deviation. The results were analyzed using statistical software, and means were accepted as significantly different at a $95\%$ confidence interval ($p \leq 0.05$). The results were plotted using Origin Pro 2018 software.
## 4. Conclusions
This work reports for the first time the simultaneous determination of five main carotenoids, including capsanthin, zeaxanthin, lutein, β-cryptoxanthin and β-carotene, in chili peppers and their products using a modified HPLC method. The results demonstrated that the HPLC-PDA method, with a broad detection range and low detection limit, which is accurate, stable, reproducible and sensitive, was easy to be applied. With fewer steps and straightforward experimental solvents, it provided an effective and trustworthy method for the qualitative and quantitative analysis of common carotenoids in both fresh chili pepper and its products.
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|
---
title: Bifidobacterium bifidum CCFM1163 Alleviated Cathartic Colon by Regulating the
Intestinal Barrier and Restoring Enteric Nerves
authors:
- Nan Tang
- Qiangqing Yu
- Chunxia Mei
- Jialiang Wang
- Linlin Wang
- Gang Wang
- Jianxin Zhao
- Wei Chen
journal: Nutrients
year: 2023
pmcid: PMC10005791
doi: 10.3390/nu15051146
license: CC BY 4.0
---
# Bifidobacterium bifidum CCFM1163 Alleviated Cathartic Colon by Regulating the Intestinal Barrier and Restoring Enteric Nerves
## Abstract
Cathartic colon (CC), a type of slow-transit constipation caused by the long-term use of stimulant laxatives, does not have a precise and effective treatment. This study aimed to evaluate the ability of *Bifidobacterium bifidum* CCFM1163 to relieve CC and to investigate its underlying mechanism. Male C57BL/6J mice were treated with senna extract for 8 weeks, followed by a 2-week treatment with B. bifidum CCFM1163. The results revealed that B. bifidum CCFM1163 effectively alleviated CC symptoms. The possible mechanism of B. bifidum CCFM1163 in relieving CC was analyzed by measuring the intestinal barrier and enteric nervous system (ENS)-related indices and establishing a correlation between each index and gut microbiota. The results indicated that B. bifidum CCFM1163 changed the gut microbiota by significantly increasing the relative abundance of Bifidobacterium, Faecalibaculum, Romboutsia, and Turicibacter as well as the content of short-chain fatty acids, especially propionic acid, in the feces. This increased the expression of tight junction proteins and aquaporin 8, decreased intestinal transit time, increased fecal water content, and relieved CC. In addition, B. bifidum CCFM1163 also increased the relative abundance of Faecalibaculum in feces and the expression of enteric nerve marker proteins to repair the ENS, promote intestinal motility, and relieve constipation.
## 1. Introduction
Cathartic colon (CC), a type of slow-transit constipation, is generally caused by the long-term use of stimulant laxatives such as anthraquinones [1]. However, the clinical treatment for CC remains the same as that for common constipation, with no specifically effective treatment. In severe cases, relief can only be provided through surgery.
Previously, we successfully constructed a CC constipation model using senna extract and found that CC mice have symptoms of slow-transit constipation, along with damage to the intestinal mechanical barrier and enteric nervous system (ENS) [2]. Bifidobacterium bifidum CCFM1163 was found to alleviate CC after the application of different probiotic interventions. Thus, this study aimed to determine the potential mechanism of B. bifidum CCFM1163 in alleviating damage to the intestinal barrier and ENS and provide a theoretical basis for the development and application of bifidobacterial products that can prevent and treat constipation.
An intact intestinal barrier effectively defends against the invasion of foreign pathogenic bacteria in the intestinal lumen and plays a key role in maintaining intestinal homeostasis and health. The intestinal barrier is divided into biological, chemical, mechanical, and immune barriers from the lumen to the outside of the intestine [3]. Several animal and clinical studies have demonstrated that probiotics can positively affect the intestinal barrier. For example, Bifidobacteria increase the relative abundance of Lactobacillus and decrease the relative abundance of pathogenic bacteria (Alistipes, Odoribacter, and Clostridium) in the host intestine, thereby affecting the biological barrier and relieving constipation [4]. Another clinical study found that B. bifidum CCFM16 modulates the host biological barrier and effectively relieves chronic constipation in adults [5]. Moreover, B. bifidum can promote intestinal motility in constipated mice by influencing gastrointestinal active peptide levels and 5-hydroxytryptamine (5-HT) receptor expression in the chemical barrier [6]. Several studies have demonstrated that probiotics alleviate disease by affecting the intestinal mechanical barrier. For example, probiotics enhance the intestinal mechanical barrier by directly upregulating the gene expression of tight junction (TJ) proteins and MUC2 (mucin2) [7,8] and competitively excluding the binding of intestinal pathogens to the mucosa [9,10]. The fermentation supernatant of probiotic bacteria has the same effect. For instance, *Lactobacillus rhamnosus* fermentation supernatant modulates 5-HT receptor 4 (5-HT4R) and the gut microbiota, which in turn promotes the production of intestinal mucin [11]. A recent study demonstrated that B. longum reduces inflammation and relieves constipation by downregulating interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) expression in colonic tissues [12].
The ENS comprises enteric neurons and enteric glial cells (EGCs). *Protein* gene product 9.5 (PGP9.5) is a specific marker of enteric neurons. Moreover, EGCs secrete self-marker proteins, such as glial fibrillary acidic protein (GFAP) as well as S100β, and Sox10 proteins, which are frequently used to identify EGCs. Studies have revealed that ENS injury causes intestinal motility disorders and that the beneficial effects of probiotics on intestinal motility are partly mediated by ENS [13,14,15]. On the one hand, probiotics can increase the expression of GFAP, S100β, and Sox10 in the intestinal mucosa, as well as that of neurotransmitters in the submucosal nerve plexus [16,17]. On the other, they can modulate enteric neurons and certain neuronal subtypes and promote intestinal motility by upregulating Toll-like receptor 2 (TLR2) expression in enteric neurons [18,19].
Based on these studies, this study aimed to determine the effect of B. bifidum CCFM1163 on CC and its possible mechanisms through the biological, chemical, mechanical, and immune barriers and the ENS. This study provides a new theoretical basis for the creation of functional probiotics with independent intellectual property rights, and new ideas for the development of functional foods to improve national health.
## 2.1. Bacterial Treatment
The three strains of B. bifidum (B. bifidum 45M3 [CCFM1163], B. bifidum M3 and B. bifidum M7) used in this experiment were isolated from healthy human feces and stored in the food microbial strain bank of Jiangnan University. The strains were retrieved from storage tubes at −80 °C and cultured after two generations of activation in modified de Man, Rogosa, and Sharpe broth with $0.05\%$ (w/v) L-cysteine at 37 °C under anaerobic culture conditions. The cultured suspension was collected after centrifugation (8000× g, 4 °C, 15 min) and resuspended after discarding the supernatant followed by washing twice with phosphate-buffered saline (PBS) buffer. A quantity of 1 mL was used to determine the bacterial concentration by gradient dilution method, and the remaining suspension was stored at −80 °C. Before oral administration, the cells were diluted with PBS to a concentration of 5 × 109 CFU/mL.
## 2.2. Animal Experiments
Eight-week-old male C57BL/6J mice were bought from the Model Animal Research Centre of Vital River (Shanghai, China). The animal experiments involved in this research were conducted at the Experimental Animal Center of Jiangnan University and approved by the Ethics Committee of Experimental Animals of Jiangnan University (JN.No 20210530c1201226[138]). All animals ate standard feed and drank water freely. After one week of adaptation, the mice were randomly divided into the following groups: normal control group (NC), cathartic colon group (CC), mosapride-treated group (MOSA), berberine-treated group (BERB), B. bifidum CCFM1163-treated group (BB1), B. bifidum M3-treated group (BB2), and B. bifidum M7-treated group (BB5) ($$n = 6$$/group). Senna extract (Xuhuang Biology Co., Ltd., Xi’an, China) was orally administered to all mice except NC mice, and the gavage volume of each mouse was 200 μL per day [20]. The specific method used is illustrated in Figure 1A. Subsequently, a few indices were measured in the following 1 week to determine the indications of constipation symptoms in the mice. After confirmation, the positive drug treatment groups were administered 200 μL of mosapride and berberine solutions (0.2 and 10 mg/mL, respectively) per day; bacterial treatment groups were administered 200 μL of different bacterial suspensions; and the NC group was administered 200 μL of sterile PBS by gavage once a day for 2 weeks. The timing of the animal treatment is shown in Figure 1B.
## 2.3. Determination of Constipation-Related Indicators
Gut transit time. The gut transit time refers to the total time it takes for the chyme to pass from the stomach through the intestine and then reach the anus and finally be excreted as feces. It reflects the peristaltic capacity of the entire gastrointestinal tract. The Evans blue test was used to evaluate gut transit [21]. To ensure the accuracy of the assay results, mice were fasted for 12 h and watered freely before the assay. In the beginning, mice were administrated 0.2 mL of Evans Blue semiliquid solution ($2.5\%$ Evans Blue and $1\%$ methylcellulose) by gavage. The time interval between finishing the gavage and the expulsion of the first blue pellet was recorded as the gut transit time for each mouse.
Small intestine transit rate. Gum arabic was added to water as a thickener, heated, and boiled until the solution was clear. Activated carbon was then added to the boiling mixture to ensure uniform mixing. After cooling, the solution was diluted and fixed with water to 1000 mL to obtain an activated carbon solution. The mice were fasted for 12 h and watered freely before the assay. At the beginning, each mouse was administered 0.2 mL of activated carbon solution. Mice were free-ranged for 30 min and sacrificed, and their small intestinal segments were removed. The distance between the front section of the activated carbon and the total length of the small intestine were measured [22]. The small intestine transit rate was calculated by the following equation:Small intestine transit rate (%) = Length of activated carbon propulsion (cm)/Total length of the small intestine (cm) × $100\%$[1] Fecal water content. Feces were collected individually and weighed before and after freeze-drying [23]. The fecal water content was calculated according to the following equation:Fecal water content (%) = (Wet weight of the feces (g) − Dry weight of the feces (g))/Wet weight of the feces (g) × $100\%$[2]
## 2.4. Histopathological Analysis
Terminal colon tissue (0.5 cm) was collected, immediately rinsed with pre-cooled saline, and then placed in $4\%$ paraformaldehyde solution for fixation to avoid secondary damage to tissues. The fixed tissues were rinsed and dehydrated in $70\%$, $80\%$, and $90\%$ ethanol solution (v/v) for 30 min and subsequently placed in a mixture of alcohol and xylene (alcohol: xylene = 1:1) and rinsed thrice. The sections were then transferred to a mixture of xylene and paraffin wax (xylene: paraffin wax = 1:1) for wax immersion. After paraffin embedding, 5 μm sections were created. The sections were placed on slides and stained with hematoxylin and eosin (H&E) following standard procedures. Finally, after sealing and complete solidification using neutral gum, the sections were photographed in a digital tissue section scanner (3DHistech, Budapest, Hungary). The histopathological scoring table of the colon is presented in the Supplementary Materials.
## 2.5. Immunofluorescence
The samples were embedded and sectioned using the previously described method. The sections were maintained at 60 °C for 60 min, immersed in xylene for 10 min, and then sequentially into $100\%$, $95\%$, $85\%$, and $75\%$ ethanol (v/v) for 5 min. The mixture was then soaked for 5 min in deionized water thrice. The sections were added to 10 mmol/mL citrate buffer for 15 min to ensure the complete submersion of the tissue. Subsequently, the sections were soaked for 5 min in triethanolamine-buffered saline (TBS) and washed thrice. After air-drying, 50 μL of blocking buffer was added to block the sections for 30 min. In total, 50 μL of primary antibody (1:5000, ab7260, Abcam, Shanghai, China) was then added to each section and stained overnight at 4 °C. The next day, the same washing process was performed with TBS to remove liquid from the tissue. After drying, 20 μL of fluorescently labeled secondary antibody (1:500, ab150077, Abcam, Shanghai, China) was added and the sections were incubated for 60 min in the dark. After washing, DAPI was added to each section for 5 min, followed by washing with TBS. Finally, sealing buffer was added and coverslips were placed to seal the slides. Observation in a digital tissue section scanner (3DHistech, Budapest, Hungary).
## 2.6. Real-Time Polymerase Chain Reaction
Colon tissues were placed in enzyme-free centrifuge tubes containing enzyme-inactivating zirconia beads, and 1 mL TRIzol (Invitrogen, Carlsbad, CA, USA) was added for total RNA extraction. The extracted RNA was reverse transcribed into cDNA using a reverse transcription kit (Vazyme Biotech Co., Ltd., Nanjing, China). PCR systems were prepared according to the instructions of the qPCR mix (Bio-Rad, Hercules, CA, USA), and the PCR systems were used in a BioRad-CFX384 fluorescent quantitative gene amplification instrument (Bio-Rad, USA). Real-time qPCR was performed to detect the transcript levels of PGP9.5, S100β, GFAP, MUC2, zonula occluden-1 (ZO-1), Occludin, Claudin-1, Claudin-4, TNF-α, IL-1β, IL-6, tryptophan hydroxylase 1 (Tph1), 5-HT2B, 5-HT4, aquaporin 4 (AQP4), AQP8, G protein-coupled receptor 41 (GPR41), and GPR43 genes in the mouse colon. The primer sequences are provided in the Supplementary Materials.
## 2.7. Enzyme-Linked Immunosorbent Assay
The tissue was first rinsed with pre-cooled PBS to remove any residual blood and then placed in a centrifuge tube containing sterilized zirconia beads. PBS was added in a weight-to-volume ratio of 1:9 and tissue disrupted. The tissue was centrifuged at 4 °C at 5000× g for 10 min, and the supernatant was removed for the test. The concentrations of TNF-α, IL-1β, and IL-6 in colonic tissues were measured using a mouse ELISA kit (R&D, Minneapolis, MN, USA). A double antibody sandwich ELISA (Enzyme-linked Biotechnology Co., Ltd., Shanghai, China) was used to detect 5-HT in the tissues.
## 2.8. Short-Chain Fatty Acid (SCFA) Analysis
The contents of acetic acid (AA), propionic acid (PA), and butyric acid (BA) in feces were analyzed by gas chromatography–mass spectrometry (GC-MS). The stool samples were weighed and placed in 2 mL centrifuge tubes and homogenized with a tissue homogenizer after adding 500 μL of saturated sodium chloride and 40 μL of $10\%$ sulfuric acid sequentially. Diethyl ether (1 mL) was added to the homogenized sample in a fume hood, mixed thoroughly, and centrifuged at 4 °C at 14,000× g for 15 min, and the supernatant was removed. The sample was then transferred to a centrifuge tube and allowed to stand for 15 min after adding 0.25 g anhydrous sodium sulfate to remove water from the sample. After centrifugation, 500 μL of the sample was analyzed using a gas chromatograph–mass spectrometer (GC-MS) (QP2010 Ultra; Shimadzu, Kyoto, Japan). The GC-MS analysis parameters were obtained from a previous study [24].
## 2.9. Gut Microbiota Analysis
Microbial genomic DNA was extracted from stool samples using a FastDNA®Spin Kit for Stool (MP Biomedicals, Santa Ana, CA, USA). The V3-V4 regions of the 16S rRNA gene were amplified using universal primers (341F and 806R). The PCR products were purified according to the instructions of the TIANgel Mini Purification Kit (Tiangen, Beijing, China), and DNA was quantified and mixed using the Qubit dsDNA Assay Kit (Life Technologies, Invitrogen, Carlsbad, CA, USA). The amplicons were sequenced on the MiSeq PE300 platform using a MiSeq kit (Illumina, San Diego, CA, USA).
Data processing and bioinformatics analysis were carried out using the QIIME2 platform. The β-diversity was visualized by principal coordinate analysis (PCoA) using an online website (https://www.microbiomeanalyst.ca/, accessed on 10 January 2023) [25]. Linear discriminant analysis effect size (LEfSe) was used to calculate the differential abundance of microbial taxa, and taxonomic cladogram trees were drawn using an online website (http://huttenhower.sph.harvard.edu/galaxy/, accessed on 11 January 2023). Functional prediction of gut microbes was performed based on the PICRUSt [26].
## 2.10. Statistical Analysis
This experiment was performed using GraphPad Prism 9.0.0 (GraphPad, San Diego, CA, USA) statistical software for the statistical analysis of the data. The statistical methods were mean ± standard error of the mean or the median ± interquartile range. A parametric analysis of differences between groups was performed using a one-way analysis of variance (ANOVA) with Dunnett’s multiple comparison test. Correlation analysis and visualization between indexes were performed using ChiPlot (https://www.chiplot.online/, accessed on 12 January 2023).
## 3.1. B. bifidum CCFM1163 Relieved CC Symptoms in Senna Extract-Treated Mice
Although senna extract-treated mice had lower fecal water contents and longer gut transit times than the NC group, the small intestine transit times were not markedly different among the groups ($p \leq 0.05$). These data demonstrated that the animal CC model can be successfully constructed using senna extract (Figure 2A–C). In addition, H&E staining (Figure 2D) and the histological injury score of the colon (Figure 2E) of the senna extract-treated group exhibited destroyed epithelium, decreased goblet cells, crypt loss, and the infiltration of inflammatory cells into the mucosal layer and even the submucosal layer. In addition, the results of the immunofluorescence analysis and mRNA expression levels of enteric nerve-specific markers suggested a notably decreased mRNA expression level of PGP9.5 in CC mice ($p \leq 0.05$, Figure 2F) and lower gene expression levels of GAFP and S100β in NC mice ($$p \leq 0.10$$, $$p \leq 0.07$$, Figure 2G–I). These results suggest that senna extract could damage the mechanical barrier and intestinal nerve in the mouse colon during the construction of an animal CC model.
BERB-, B. bifidum CCFM1163-, BB2-, and BB5-treated groups had significantly decreased gut transit times compared with the CC group. The B. bifidum CCFM1163- and BERB-treated groups displayed the best effect and restored the gut transit time to a normal pattern (Figure 2A). Additionally, although B. bifidum CCFM1163 increased the fecal water content, it was statistically different from the other groups ($p \leq 0.05$, Figure 2C). According to the evaluation index of positive results for the relief of constipation in the Technical Specification for Evaluation of Health Food, 2022 edition, B. bifidum CCFM1163 is known to relieve CC. We further performed a histopathological assessment of the distal colon tissue. Colonic tissue damage was observably repaired in CC mice treated with drugs and B. bifidum compared with those in the CC group, with a recovery of mucosa and crypt structure (Figure 2D–E). Injury to mouse colon tissue was quantified using the pathological score. As illustrated in Figure 2E, the colon histopathological scores of MOSA-, BERB-, B. bifidum CCFM1163-, BB2-, and BB5-treated groups were $52.8\%$, $58.3\%$, $26.7\%$, $47.2\%$, and $55.6\%$ of the CC group, respectively. B. bifidum CCFM1163 intervention reversed the enteric nervous damage caused by senna extract compared with that in the CC group. This was mainly manifested by a remarkable increase in the mRNA expression levels of PGP9.5, GFAP ($p \leq 0.05$), and S100β ($$p \leq 0.13$$, Figure 2F–I) in colonic tissues. Considered together, these results suggest that B. bifidum CCFM1163 relieved CC and repaired the intestinal mechanical barrier and enteric nervous damage caused by senna extract.
## 3.2.1. B. bifidum CCFM1163 Can Repair Intestinal Mechanical Barrier Damage by Promoting the Expression of TJ Proteins
To investigate the effects of different bacterial strains on the intestinal mechanical barrier in mice, we determined the transcript levels of MUC2 and four TJ proteins in the colon. As illustrated in Figure 3A, the mRNA expression levels of MUC2, ZO-1, Occludin, and Claudin-1 were markedly decreased in the CC group ($p \leq 0.05$). These findings demonstrated that the application of senna extract to construct a constipation model was accompanied by damage to the intestinal mechanical barrier, mainly in the form of the thinning of the intestinal mucus layer and an increase in intestinal permeability. Compared with the CC group, almost all B. bifidum strain treatment groups notably increased the mRNA expression levels of ZO-1, Occludin, and Claudin-1 in the colon, as well as the mRNA expression level of MUC2 in the colon. These results demonstrated that B. bifidum has extremely beneficial effects in improving intestinal permeability and promoting colonic mucus secretion. These effects were not observed for the positive control drug. These results suggest that B. bifidum CCFM1163 alleviates CC while repairing the damage to the intestinal mechanical barrier caused by it.
## 3.2.2. B. bifidum CCFM1163 Can Alleviate Intestinal Immune Barrier Inflammation by Reducing IL-6 and IL-1β Levels
The effects of senna extract treatment on the gene and protein expression levels of proinflammatory cytokines in mice are presented in Figure 3B. At the mRNA level, the relative expression levels of TNF-α, IL-1β, and IL-6 in the CC group were 0.60, 3.79, and 1.49 times higher than those in the NC group. At the protein level, the relative expression levels of pro-inflammatory cytokines in the CC group were 1.03, 5.54, and 9.63 times higher than those in the NC group. These data suggest that the application of senna extract also caused an inflammatory response in the host during the construction of a CC model. B. bifidum and drug intervention reduced the inflammation level in the organism to varying degrees; however, the effect of B. bifidum CCFM1163 in reducing the inflammation level was comparable with that of the positive drug Mosapride. These findings suggest that B. bifidum CCFM1163 reduces host intestinal inflammation and improves the immune barrier of the intestine while relieving CC.
## 3.2.3. B. bifidum CCFM1163 Can Regulate Intestinal Chemical Barrier by Altering 5-HT and AQP Expression and Increasing SCFA Content in Feces
We determined the 5-HT content and mRNA expression of TPH1, 5-HT2B, 5-HT4, AQP4, and AQP8 in the colon. As presented in Figure 3C, the 5-HT content and gene expression levels of Tph1 and AQP8 were markedly reduced in CC mice ($p \leq 0.05$). These results suggest that senna extract inhibits the expression of Tph1, thereby reducing the neurotransmitter 5-HT content in the colon and that abnormal changes in 5-HT may be one of the neuropathological bases for slowed colonic transmission in CC mice. Moreover, after measuring the metabolites of the gut microbiota, the levels of AA, PA, and BA were found to be markedly downregulated in the intestine of mice treated with senna extract (Figure 3D). The specific receptor for SCFAs (GPR41) also exhibited a notably downward trend (Figure 3E). After different B. bifidum and drug interventions, the gene expression level of Tph1 in the colon markedly increased in all B. bifidum-treated groups ($p \leq 0.05$). There was also a corresponding statistically significant increase in 5-HT content in the colon, but only in the B. bifidum CCFM1163-treated group ($p \leq 0.05$). Furthermore, B. bifidum CCFM1163 markedly reduced the expression level of AQP4 and increased that of AQP8 in the intestine ($p \leq 0.05$). Meanwhile, the levels of SCFAs in the intestines of mice were upregulated. Only the levels of PA and BA in the BREB-treated group and BA in the MOSA- and BB2-treated groups were not statistically different from those in the CC group ($p \leq 0.05$). The above results suggest that B. bifidum CCFM1163 repaired damage to the intestinal chemical barrier while relieving CC.
## 3.2.4. B. bifidum CCFM1163 Can Improve Gut Microbial Dysbiosis
We observed a significant alteration in the structure of intestinal flora in the CC mice (Figure 4A–C). This is mainly reflected in the observably lower diversity of gut microbiota (e.g., considerably lower Chao1 and Shannon indices) and the CC group with its specific flora structure (β-diversity). At the phylum level, senna extract treatment statistically reduced the relative abundance of Bacteroidetes in mice intestine while increasing the relative abundance of Proteobacteria ($p \leq 0.05$). Both B. bifidum and drug interventions increased the relative abundance of Bacteroides and decreased the relative abundance of Proteobacteria. Notably, B. bifidum CCFM1163 remarkably increased the relative abundance of Actinomycetes (Figure 4D–G). At the genus level, the biomarkers for the CC group were Citrobacter, Bacteroides, Escherichia-Shigella, Parabacteroides, Blautia, and Enterococcus, whereas those for the NC group were Alloprevotella, Lactobacillus, Coriobacteriaceae, Alistipes, Adlercreutzia, and Desulfovibrio. Muribaculaceae, Faecalibaculum, Bifidobacterium, Turicibacter, Romboutsia, and Enterorhabdus were markedly enriched in the B. bifidum CCFM1163-treated group (Figure 4H–I). These findings revealed that senna extract treatment altered the structure of the gut microbiota of mice and that damage to the intestinal biological barrier was repaired to varying degrees after B. bifidum and drug interventions.
To further investigate the effect of B. bifidum CCFM1163 on the function of the fecal flora, the functional profiles of the microbiota were predicted based on PICRUSt analysis. Mice in the CC group exhibited different functional gene composition profiles compared with NC mice. Specifically, carbohydrate metabolism, xenobiotic biodegradation and metabolism, and lipid metabolism were upregulated, whereas nucleotide metabolism and biosynthesis of other secondary metabolites were downregulated in the CC group compared with the NC group (Figure 4J). Seventeen pathways were identified in the CC and B. bifidum CCFM1163-treated groups, among which, amino acid metabolism, nucleotide metabolism, and biosynthesis of other secondary metabolites were upregulated, whereas xenobiotics biodegradation and metabolism, and lipid and carbohydrate metabolism were downregulated in the B. bifidum CCFM1163-treated group (Figure 4K). Overall, B. bifidum CCFM1163 treatment reversed the alteration in fecal flora function in the senna extract-treated mice.
## 3.2.5. Correlation Analysis Revealed That CC Relief Is Associated with Changes in Gut Microorganisms
We established correlations between the gut microbiota, CC apparent indices, and test indices based on the above results. The outcomes illustrated in Figure 5 indicate that IL-1β, AQP4, Citrobacter, Bacteroides, Escherichia-Shigella, Enterococcus, and Erysipelatoclostridium indices displayed a markedly positive correlation, and PGP9.5, S100β, MUC2, ZO-1, Occludin, Claudin-1, 5-HT, Tph1, AQP8, GPR41, AA, PA, BA, Butyricimonas, Turicibacter, and Muribaculum indices displayed a markedly negative correlation with intestinal transit time ($p \leq 0.05$). The S100β, MUC2, 5-HT, Tph1, BA, Adlercreutzia, and Muribaculum indices displayed a significantly positive correlation, and the IL-1β, IL-6, Citrobacter, Bacteroides, Escherichia-Shigella, Enterococcus, and Erysipelatoclostridium indices displayed a significantly negative correlation with the fecal water content rate ($p \leq 0.05$). In view of CC relief by B. bifidum CCFM1163, the correlation between each index after B. bifidum CCFM1163 treatment was analyzed. As previously described, B. bifidum CCFM1163 treatment notably increased the relative abundance of Bifidobacterium, Faecalibaculum, Romboutsia, and Turicibacter in the intestine ($p \leq 0.05$). ZO-1, Claudin-4, AQP8, and PA were significantly and positively correlated, whereas AQP4 and GPR43 were significantly and negatively correlated with Bifidobacterium (Figure 5). Similarly, S100β, GFAP, ZO-1, AQP8, and PA were positively correlated with Faecalibaculum. ZO-1, Occludin, AQP8, AA, PA, and BA were positively correlated with Turicibacter. ZO-1, Occludin, Claudin-4, AQP8, and BA were positively correlated, whereas AQP4 was negatively correlated with Romboutsia ($p \leq 0.05$).
In summary, these results suggest that a possible pathway, through which B. bifidum CCFM1163 alleviates CC, involves altering the gut microbiota, primarily by significantly increasing the relative abundance of Bifidobacterium, Faecalibaculum, Romboutsia, and Turicibacter in the feces. On the one hand, B. bifidum CCFM1163 increased SCFA content in feces, especially PA, thereby repairing the mechanical barrier of the intestine (increasing the expression of three TJ proteins, improving the absorption and secretion of water in the intestine, ultimately reducing the gut transit time, increasing fecal water content, and relieving CC constipation). On the other, it increased the relative abundance of Faecalibaculum in stools, increased the expression of enteric nervous marker proteins S100β and GFAP, repaired the ENS, and promoted intestinal motility, thus relieving constipation.
## 4. Discussion
In this study, a CC mouse model was used to systematically elucidate the pathogenesis of CC from both the intestinal barrier and ENS aspects for the first time, confirming the alleviating effects of B. bifidum CCFM1163 on CC. Based on this, we aimed to elucidate the mechanisms of CC alleviation by B. bifidum CCFM1163 and provide a theoretical basis for the development of probiotic formulations for CC.
Senna extract-treated mice had constipation symptoms of dry stool and impaired gut motility, confirming the successful establishment of the CC model in the treated animals. B. bifidum CCFM1163 treatment statistically reduced the total gut transit time, whereas no significant change was observed in the transit time of the small intestine. These results confirmed that B. bifidum CCFM1163 relieved CC symptoms owing to shortened colonic transit times. A previous study found that the long-term use of stimulant laxatives can damage the ENS, leading to impaired colonic motility, which is consistent with our results [27]. Moreover, B. bifidum CCFM1163 supplementation observably repaired the damaged tissue and reduced histological scores in the colon. This suggests that B. bifidum CCFM1163 effectively relieved CC symptoms. This study is the first to provide direct evidence of the role of B. bifidum CCFM1163 in alleviating CC.
A few studies have reported on the association between gut microbiota and ENS. Research on antibiotic-induced bacterial depletion mice has found that microbiota plays a vital role in the maintenance of ENS by regulating enteric neuronal survival and promoting neurogenesis [28]. Another study found that adding probiotics to the diet increases the expression of EGC marker proteins and neurotransmitters [17]. BERB has been reported to have enteric nerve repair effects [29] and therefore served as a positive drug control in this experiment. Our observations were consistent with those of two previous studies; B. bifidum CCFM1163 intervention notably increased the expression levels of enteric neurons and EGC marker proteins in the colon, and its effect was slightly better than that of BERB. In addition, B. bifidum CCFM1163 displayed anti-inflammatory effects in CC mice and played a role mainly in reducing IL-6 and IL-1β levels. IL-6 has been proven to exert either pro-inflammatory or anti-inflammatory properties, depending on its concentration and in combination with other inflammatory cytokines. Notably, the combination of high concentrations of IL-6 and IL-1β reduces neurogenesis [30]. These findings suggest a potential mechanism by which B. bifidum CCFM1163 affects the intestinal barrier and ENS.
Three mechanical barriers exist in the large intestine. Abundant goblet cells in the large intestine secrete mucin, organizing the mucous layer that covers the intestinal epithelium, which is the first mechanical barrier [31]. MUC2, the major mucin secreted in the intestine, plays an important barrier function, and mice lacking MUC2 develop spontaneous colitis [32]. Only a few bacteria have the enzymes required to metabolize mucin. Among Bifidobacterium species, only members of B. bifidum have been shown to degrade mucin. These enzymes can be used to produce SCFA during the fermentation process [33]. This may be the reason why B. bifidum CCFM1163 did not increase the expression level of MUC2 but increased the content of SCFA. The glycocalyx on intestinal epithelial cells provides the second mechanical barrier in the colon. The third mechanical barrier is the cell junction, which includes TJ proteins ZO-1, Occludin, and Claudin-1. B. infantis reduces colonic permeability and enhances the mechanical barrier by secreting an extracellular protein that promotes the expression of ZO-1 and Occludin [34]. We also confirmed that B. bifidum CCFM1163 exerts a protective effect on the intestinal barrier by increasing the expression of TJ proteins to repair intestinal mechanical barrier damage.
Enterogenous 5-HT is mainly generated by enterochromaffin cells under the action of TPH1 and promotes intestinal motility and secretion by binding to 5-HT-specific receptors [35]. However, different results have been reported regarding this issue. A few studies have reported that 5-HT is necessary for normal gastrointestinal motility [36,37], whereas others have indicated that 5-HT does not play a key role [38,39]. The cause of this phenomenon is the decreased level of 5-HT receptor 5-HT2B in the colonic interstitial cells of Cajal, which impairs the responsiveness of diabetic mice to 5-HT. Impaired colonic motility in diabetic mice was improved by activating the 5-HT2B receptor. In contrast, normal mice injected with 5-HT2B receptor inhibitors exhibited a significant increase in colonic transit time [40]. To determine whether 5HT is involved in the regulation of intestinal motility in the CC model, we measured the 5HT content and the expression of related receptors. Interestingly, in this study, the 5-HT levels were notably reduced in the CC group; however, no significant differences were observed in the 5-HT2B levels between the CC and NC groups. In addition, B. bifidum CCFM1163 promoted 5-HT secretion and increased 5-HT2B expression. These results demonstrate that B. bifidum CCFM1163 may activate 5-HT2B receptors by promoting 5-HT secretion, which, in turn, improves colonic motility in CC mice.
Generally, the increased expression of AQP4 and AQP8 is observed in mouse constipation models [41]. Nevertheless, we observed that the expression level of AQP8 was markedly reduced in the colon of CC mice compared with that in NC mice. Although the reason for these differences remains to be determined, they could be owing to AQP4 and AQP8 being expressed at different locations in the intestinal epithelium. AQP4 is immunolocalized to the basolateral membrane of colonic epithelial cells and can regulate water absorption in the intestine, whereas AQP8 is mainly located in the apical membrane and intracellular epithelial cells and regulates the transport of water [42,43]. H&E staining revealed that the colonic epithelial surface structure was severely damaged in the CC group, which might be responsible for the significant decrease in AQP8 expression in the apical membrane. However, the basal structure of the epithelium in the CC group was not damaged; therefore, the expression of AQP4 was not affected. Considered together, B. bifidum CCFM1163 may promote water secretion in the intestine by increasing the expression of AQP8, thus increasing fecal water content and alleviating CC.
As previously observed, the effect of probiotics on constipation relief is well established; however, controversy exists regarding the effect of these probiotics on SCFA production. Certain studies have reported a change in AA, PA, and BA [44,45], whereas others have not [46,47], which could be attributed to the characteristics of the strains. Our findings supported this hypothesis. Different strains of B. bifidum have different effects on SCFA. The effect of B. bifidum CCFM1163 on SCFA was greater than that of the other two strains, and it notably increased the contents of AA, PA, and BA in mouse feces. In addition, we discovered that the transcript level of the SCFA receptor GPR41 increased markedly after B. bifidum CCFM1163 intervention but not of GPR43. The selective signaling mechanisms of GPR41 and GPR43 differ markedly. GPR43 signaling involves L cell-derived peptide tyrosine, whereas GPR41 signaling involves submucosal neurons. The greatest FFA3 efficacy was observed in the terminal ileum and colon, in contrast with more uniform FFA2 signaling [48]. Correlation analysis also revealed that GPR41 levels were significantly positively correlated with ENS indicators. Considering that PA exhibits similar affinities for GPR43 and GPR41, we hypothesize that B. bifidum CCFM1163 activates the GPR41 receptor by increasing the level of PA in the colon, thereby regulating the ENS. We intend to explore this possibility in future research.
Recent evidence has revealed that the gastrointestinal microbiota plays a key role in gut motility. In clinical studies, fecal flora composition has been found to be associated with colonic transit time. The relative abundances of Roseburia, Bacteroides, Lactococcus, and Actinobacteria were related to faster gut transit time, whereas Faecalibacterium was directly associated with slower gut transit time [49]. Here, we found that the abundance of Actinobacteria and Bacteroides was reduced in model mice, and this trend was reversed by B. bifidum CCFM1163 treatment. We also observed an enrichment of certain pathobionts (Escherichia-Shigella and Erysipelatoclostridium) in the feces of model mice. It was documented that Bifidobacterium promotes intestinal motility by decreasing the abundance of Alistipes, Odoribacter, and *Clostridium and* increasing the abundance of Lactobacillus [4]. Moreover, Bifidobacterium can directly affect the biological barrier by reducing the abundance of potentially pathogenic bacteria, which may be related to the accompanying increase in SCFA levels [50]. In a separate study that used the same probiotic, this result was confirmed and was accompanied by an increased relative abundance of fecal bifidobacterial [51]. Interestingly, the fermentation end products of Bifidobacterium are AA and lactic acid, and lactic acid is readily converted to PA by other bacteria. Therefore, a notable increase in the content of Bifidobacterium in the B. bifidum CCFM1163-treated group may be one of the reasons for the higher levels of AA and PA in the feces.
Regardless of these interesting findings, a few questions remain to be addressed. First, we hypothesized that B. bifidum CCFM1163 acts by increasing the level of SCFAs in feces, but we did not verify this experimentally. Second, we only demonstrated that although B. bifidum CCFM1163 repairs damaged ENS, its possible pathway is unclear. Finally, further clinical trials are required to apply the results of animal experiments to clinical treatments.
## 5. Conclusions
In conclusion, this study determined that B. bifidum CCFM1163 effectively alleviated CC, and its main pathway involves changing the gut microbiota, significantly increasing the relative abundance of Bifidobacterium, Faecalibaculum, Romboutsia, and Turicibacter in the feces. This increased the SCFA content in feces, repaired intestinal mechanical barrier damage to promote intestinal transit, and regulated AQP8 expression in the intestine to increase fecal water content. Additionally, B. bifidum CCFM1163 increased the relative abundance of Faecalibaculum in the stool, increased the expression of S100β and GFAP, repaired the ENS, promoted intestinal motility, and relieved constipation.
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|
---
title: 'Effects of Climate, Sun Exposure, and Dietary Intake on Vitamin D Concentrations
in Pregnant Women: A Population-Based Study'
authors:
- Ya-Li Huang
- Thu T. M. Pham
- Yi-Chun Chen
- Jung-Su Chang
- Jane C.-J. Chao
- Chyi-Huey Bai
journal: Nutrients
year: 2023
pmcid: PMC10005797
doi: 10.3390/nu15051182
license: CC BY 4.0
---
# Effects of Climate, Sun Exposure, and Dietary Intake on Vitamin D Concentrations in Pregnant Women: A Population-Based Study
## Abstract
Background: Vitamin D deficiency (VDD) is a global micronutrient issue that commonly occurs in pregnant women, leading to adverse health outcomes. We examined the role of sunlight-related factors and dietary vitamin D intake on vitamin D concentrations among pregnant women in different climate zones. Methods: We conducted a nationwide cross-sectional survey in Taiwan between June 2017 and February 2019. The data of 1502 pregnant women were collected, including sociodemographic information and characteristics related to pregnancy, diet, and sun exposure. Serum 25-hydroxyvitamin D concentrations were measured, and VDD was assessed as a concentration of less than 20 ng/mL. Logistic regression analyses were used to explore the factors associated with VDD. Furthermore, the area under the receiver operating characteristic (AUROC) curve was used to analyze the contribution of sunlight-related factors and dietary vitamin D intake to vitamin D status stratified by climate zones. Results: The prevalence of VDD was $30.1\%$ and was the highest in the north. Sufficient intake of red meat (odds ratio (OR): 0.50, $95\%$ confidence interval (CI): 0.32–0.75; $$p \leq 0.002$$), vitamin D and/or calcium supplements (OR: 0.51, $95\%$ CI: 0.39–0.66; $p \leq 0.001$), sun exposure (OR: 0.75, $95\%$ CI: 0.57–0.98; $$p \leq 0.034$$), and blood draw during sunny months (OR: 0.59, $95\%$ CI: 0.46–0.77; $p \leq 0.001$) were associated with a lower likelihood of VDD. Additionally, in northern Taiwan, which is characterized by a subtropical climate, dietary vitamin D intake (AUROC: 0.580, $95\%$ CI: 0.528–0.633) had a greater influence on vitamin D status than did sunlight-related factors (AUROC: 0.536, $95\%$ CI: 0.508–0.589) with a z value = 51.98, $p \leq 0.001.$ By contrast, sunlight-related factors (AUROC: 0.659, $95\%$ CI: 0.618–0.700) were more important than dietary vitamin D intake (AUROC: 0.617, $95\%$ CI, 0.575–0.660) among women living in tropical areas of Taiwan (z value = 54.02, $p \leq 0.001$). Conclusions: Dietary vitamin D intake was essential to alleviate VDD in the tropical region, whereas sunlight-related factors played a greater role in subtropical areas. Safe sunlight exposure and adequate dietary vitamin D intake should be promoted appropriately as a strategic healthcare program.
## 1. Introduction
Vitamin D deficiency (VDD) has become an urgent micronutrient issue globally [1] because of its high prevalence [2], and it has become a potential cause of non-communicable [3,4] and infectious [5,6] diseases. Although VDD has been addressed as a global public health problem in all age groups, the population-representative data regarding vitamin D were limited to several risky groups [7]. Pregnant women are a vulnerable population affected by VDD [1], which can lead to adverse pregnancy outcomes [8,9]. Moreover, VDD may result in health disparities [10], which leads to the increment of stillbirths and pregnancy-related deaths [11]. Hence, improving vitamin D status is necessary to upgrade the reproductive health and well-being of mothers and their infants.
The major factors for VDD are sun exposure and dietary vitamin D intake [12]. However, obtaining vitamin D through sun exposure can be inefficient or unsafe because of the skin cancer risk from ultraviolet radiation [13]. Additionally, the dermal synthesis of vitamin D was suggested to be influenced in different climate zones using an in vitro model [14]. The adequate achievement of vitamin D intake from diet alone is hard [15]. Therefore, vitamin D supplementation is a crucial nutritional priority recommended by many physicians to achieve optimal serum concentration [16] that could prevent short and long-term maternal and infant health complications [17].
Vitamin D status has been explored in the literature. However, population-based research on pregnant women in East *Asia is* still limited. To our best knowledge, relevant information regarding the potential effect of the climatic zone has not been explored. Taiwan is an East Asian island characterized by two climatic zones [18]. Based on this unique advantage, Taiwan has the opportunity to assess whether sunlight-related factors and dietary vitamin intake contribute differently to vitamin D levels among people living in different parts of the country. Exploring the prevalence of VDD and its potential risk factors among pregnant women in *Taiwan is* an important task to address the research gap and for future policy planning. This study aimed to assess the determinants of VDD and to examine the contribution of sunlight-related factors and dietary vitamin D intake to vitamin D status in different regions of Taiwan using a nationally representative survey.
## Study Population
A national cross-sectional nutritional survey of pregnant women was conducted from June 2017 to February 2019 across Taiwan. A multiple-stage cluster sampling approach was used, including [1] the selection of eight layers according to geographical location (northern, central, southern, and eastern Taiwan) and [2] the random selection of hospitals (large and small sizes) from the list based on the number of women availing pregnancy-related services per year and the probability proportional to size in each layer and [3] the whole selection of participants arriving in the selected hospitals for antenatal examination with the expectation of 150–300 women from one or two hospitals in each layer enrolled based on the potential number of annual outpatients in each hospital [19]. The distribution of eleven selected hospitals across Taiwan was in Figure 1.
We calculated a sample size of 1062 based on 200,000 deliveries by pregnant women during the study period, with a $3\%$ margin of error and a $95\%$ confidence interval (CI). We recruited participants aged ≥15 years who were legal residents of Taiwan and who underwent antenatal examinations at the selected hospitals. A satisfactory sample of 1502 pregnant women was included in the final analysis after the exclusion of nonsingleton pregnancies, participants unable to understand and speak Mandarin, and incomplete questionnaires. All participants provided written informed consent before taking the survey.
## 3. Data Collection
During study periods, all pregnant women making an antenatal visit were enrolled consecutively. At recruitment, collection of questionnaires, physical examination and blood sample were performed. Information was obtained from standardized face-to-face interviews by trained interviewers using the structured questionnaires. Variables regarding participants’ sociodemographic status, histories of diseases before and during pregnancy, pregnancy-related factors, and intake histories of prenatal and natal dietary supplements were collected by the self-reported baseline questionnaire. The dosage of supplements during pregnancy was asked and recorded in brand, exact dosage and frequency per week. Food frequency questionnaires was also used to record the intake frequency during past 3 months in 66 items of foods including egg, milk, meat, fish and vegetables. After interview of questionnaires, a 24 h dietary recall was recorded by trained dietitians. Food models were used to assist participants in recalling the food portion sizes and details of the dietary information. Then, we estimated participants’ energy intake and nutrient intake from foods. The intakes of several nutrients (e.g., vitamin D) were labeled the sources of foods or supplements respectively. We used the online software Cofit Pro (Cofit Health Care, Taipei, Taiwan) to analyze participants’ nutrient intake using the 2015 version of the Taiwan Food and Nutrient Database.
At the time of recruitment, pre-pregnancy body weight was self-reported by pregnant women, and their current body height and weight were measured. Blood samples were drawn, centrifuged, then froze (−80 °C) and analyzed in batches.
## 3.1. Sociodemographic and Pregnancy-Related Characteristics
Pregnant women were queried regarding their age (years); residential area; education level; household monthly income; religion; gravidity; parity; number of fetuses in the current pregnancy; gestational age; and body height (cm) and weight (kg) before pregnancy, which were used to calculate pre-pregnancy body mass index (BMI, kg/m2). Additional information related to pregnancy was extracted from the prenatal visit records of participants. The residence was categorized as living in Taiwan’s northern, central, southern, or eastern regions.
## 3.2. Dietary Characteristics
Pregnant women were asked whether they consumed sufficient amounts of the four groups of the following food items: [1] dairy products (e.g., fresh milk, yogurt, cheese, cream cheese, and powdered milk); [2] eggs; [3] red meat (e.g., pork, beef, and mutton); and [4] nut fruits (e.g., stone fruit, nuts, pistachios, and almonds). Women also reported their frequency of using vitamin D and/or calcium supplements during pregnancy as “never”, “less than 1 day per week”, “2–5 days/week”, and “almost daily”. Then, this factor was recoded into two categories of usage, “yes” or “no”, due to the small sample size. The 24 h dietary intake was recorded to assess the intake of total energy (kcal), raw protein (g), raw fat (g), total carbohydrates (g), and vitamin D content (mg) and the use of vitamin supplements. The percentages of calories from protein, fat, and carbohydrates were also calculated [19].
The dosages of supplements were calculated if participants provided the exact dosage. However, these parameters were frequently missing, as were the brands and models of vitamins. Therefore, in the present study, we only analyzed the usage frequency of vitamin D-only or D-based supplements.
## 3.3. Sunshine-Related Factors
Sun exposure was estimated using the question, “Were you exposed to outdoor sunlight last month?” and the answers were categorized as “no” if exposed to sunlight for less than 10 min per day and “yes” if exposed to sunlight for more than 10 min per day. The seasons of blood draw were categorized according to the month of blood sample collection, as follows: sunny months (June to November) and rainy months (December to May) established according to the rainfall report of the Central Weather Bureau, Taiwan. Participants also reported whether they had to stay indoors (e.g., bedridden) for any reason during their pregnancy (“yes” or “no” response) and the number of methods used for sun protection (e.g., sunscreen, parasols, hats and outerwear with UV-block) and how often they are used.
## 3.4. Vitamin D Deficiency Assessment
As 25-hydroxyvitamin D [25(OH)D] has the long half-life (15 days) and relative stability of concentration in the blood [20], the circulating 25(OH)D is the useful biomarker of vitamin D in the human body [21]. The plasma 25-hydroxyvitamin D [25(OH)D] concentration was measured using an electrochemiluminescence immunoassay, as described previously [19]. Although there is no consensus in the definition of the suboptimal vitamin D level, VDD was defined as a 25(OH)D level of <20 ng/mL, which is a common threshold for people in at-risk groups, including pregnant women [22,23,24]. The cutoff point of less than 20 ng/mL was also recommended for use for VDD by Institution of Medicine, Academy of Medicine and American Academy of Pediatrics.
## 4. Ethical Consideration
This study was funded by the Health Promotion Administration, Ministry of Health and Welfare in Taiwan (C1050912) and was approved by the institutional review board of the government and selected hospitals (IRB number: N201707039).
## 5. Statistical Analysis
First, descriptive analysis was performed to explore the distribution of independent variables. We performed chi-square tests (for categorical variables) and t tests or Mann–Whitney tests (for continuous variables) to compare the distribution of independent variables between pregnant women with and without VDD. Second, logistic regression analysis was used to determine the factors associated with VDD. Two models were constructed. Model 1 comprised variables associated with VDD that had $p \leq 0.1$ in bivariate analysis, including age, residential area, parity, gestational age, pre-pregnancy BMI, egg intake, red meat intake, fat, vitamin D content, vitamin supplements, sun exposure, remaining indoors during pregnancy, and the season of blood draw. Gravidity and carbohydrate intake were removed from model 1 because they were highly correlated with parity (rho = 0.82) and fat intake (rho = −0.89), respectively (Table S1). Model 2 comprised factors associated with VDD that had $p \leq 0.1$ in model 1, including age, residential area, gestational age, red meat intake, vitamin D content, vitamin supplements, sun exposure, remaining indoors during pregnancy, and the season of blood draw. Odds ratios (ORs) and $95\%$ CIs were reported, and $p \leq 0.05$ was considered statistically significant.
Further sensitivity analysis was performed and stratified by residential area (north vs. south and other regions) to examine the contribution of modifiable factors to vitamin D status. Two models were constructed for each layer, including one model adjusted for sunlight-related factors (season of blood draw and sun exposure) and one model adjusted for dietary vitamin D intake (red meat and supplements). The area under the receiver operating characteristic (AUROC) curve was computed to compare the models. It is favored due to the characteristics of invariant and independent from the prevalence of the condition. All analyses were performed using R software (version 4.1.3; R Foundation for Statistical Computing, Vienna, Austria).
## 6.1. Characteristics of Study Participants
The data contained several missing values, but the distribution of variables before and after removing the missing information was the same. Therefore, the entire data of the 1502 pregnant women were used for analysis. Overall, the mean 25(OH)D concentration was 25.5 ± 8.9 ng/mL, and the prevalence of VDD was $30.1\%$ (weighted). Compared with women without VDD, those with VDD were younger ($$p \leq 0.017$$); lived in the north ($p \leq 0.001$); had uniparity ($$p \leq 0.01$$); were in the first trimester of gestation ($p \leq 0.001$); consumed high quantities of carbohydrates ($$p \leq 0.013$$) but insufficient eggs ($$p \leq 0.034$$), red meat ($p \leq 0.001$), fat ($$p \leq 0.023$$), and vitamin D and/or calcium supplements ($p \leq 0.001$); had little sun exposure ($$p \leq 0.001$$); remained indoors during pregnancy ($$p \leq 0.018$$); and had blood drawn during the rainy months ($$p \leq 0.004$$). These data are displayed in (Table 1).
## 6.2. Associated Factors of Vitamin D Deficiency
As displayed in Table 2, the likelihood of VDD was significantly lower in pregnant women who were older (OR: 0.95, $p \leq 0.001$); lived in central (OR: 0.66, $$p \leq 0.010$$), southern, or eastern Taiwan (OR: 0.20, $p \leq 0.001$) or in the eastern and outlying islands (OR: 0.33, $p \leq 0.001$); were in the second trimester (OR: 0.72, $$p \leq 0.046$$) or the third trimester (OR: 0.60, $$p \leq 0.002$$); consumed sufficient red meat (OR: 0.50, $$p \leq 0.002$$); took vitamin D and/or calcium supplements (OR: 0.51, $p \leq 0.001$); received sun exposure (OR: 0.75, $$p \leq 0.034$$); and had blood drawn during the sunny months (OR: 0.59, $p \leq 0.001$).
In the sensitivity analysis, among participants living in northern Taiwan, dietary vitamin D intake (AUROC: 0.580, $95\%$ CI: 0.528–0.633) had a greater influence on vitamin D status than did sunlight-related factors (AUROC: 0.536, $95\%$ CI: 0.508–0.589). By contrast, among participants living in the south and other parts of Taiwan, sunlight-related factors (AUROC: 0.659, $95\%$ CI: 0.618–0.700) were more influential than dietary vitamin D intake (AUROC: 0.617, $95\%$ CI: 0.575–0.660). The differences in regional models were significant, with z value = 51.98, $p \leq 0.001$ for northern Taiwan and z value = 54.02, $p \leq 0.001$ for the remaining regions. These results are visualized in Figure 2.
## 7. Discussion
In the present study, the prevalence of 25(OH)D level < 20 ng/mL among pregnant women in Taiwan was $30.1\%$ (weighted). The determinants of VDD included age, gestational age, red meat intake, vitamin D and/or calcium supplements, residential area, sun exposure, and the season of blood draw.
The occurrence of VDD [25(OH)D < 20 ng/mL] is common in pregnant women, although the rates vary in different Asian countries, ranging from $7\%$ to $40.7\%$ [25,26]. The present study found that VDD occurred more frequently in pregnant women living in northern Taiwan than in those living in southern Taiwan. A nationwide report on VDD among older adults (a risk group of VDD) had similar findings, reporting that VDD occurrence was higher in the north than in the south [27]. This phenomenon has several possible explanations. First, northern Taiwan has a higher latitude than other regions [28], and vitamin D status decrease with increasing latitudes [29]. Second, northern Taiwan has a humid subtropical climate, and sunlight may be of lower intensity than that in southern Taiwan and other regions characterized by a tropical monsoon climate. The association between age and VDD was found in the previous studies with the controversial findings. The former authors showed that age over thirty was the risk factor for VDD among pregnant women [26]. However, the current study indicated that younger age was a contributing factor for VDD, which was in line with other studies [30,31]. Our findings could be due to the habits of avoiding sunlight among almost youngers that they were likely to apply sun protection (e.g., using sunscreen, wearing long-sleeved clothes, preferring indoor activities). Thus, our findings indicate that it is worth planning VDD prevention, such as educating health literacy related to VDD and lifestyle changes in younger women, and such methods should be promoted integrating with efficient intervention strategies.
Regarding the impact of gestational age on maternal VDD, the findings are inconsistent across studies. Although several studies have reported that vitamin D status decreased during advanced gestation [32], our results are in line with those of studies reporting that the likelihood of VDD was reduced during the second and third trimesters. For example, Perreault et al. indicated that serum 25(OH)D concentrations were significantly greater in the last trimester compared to the first trimester [33]. Similarly, Savard et al. found that serum 25(OH)D levels significantly increased across trimesters [34]. In addition, Shen et al. noted a positive relationship between the increased vitamin D concentration and later gestational week [35].
It has been well established that sunlight is the main source of vitamin D. Hence, sun exposure and the summer season are the most important contributing factors to the vitamin D concentration. Nevertheless, if sun exposure cannot provide sufficient vitamin D because of factors such as sunlight intensity, time of exposure, and application of sun protection, the vitamin D status in the human body can be adjusted through nutrition and dietary intake. In the literature, the natural vitamin D content in foodstuffs is usually limited to vitamin D3 from animal products [36]. Our findings indicated that the consumption of red meat was associated with lower VDD rates. Moreover, the present study demonstrated that vitamin D and/or calcium supplements could reduce the likelihood of VDD.
In our sensitivity analysis, the effects of sunlight-related factors and dietary vitamin D intake on 25(OH)D levels varied by region. In northern Taiwan, dietary vitamin D intake was more important than sunlight-related factors for improving maternal vitamin D status; however, sunlight-related factors were the main sources of vitamin D for pregnant women living in the south and other parts of Taiwan, and vitamin D intake played a minor role. These variations in effectiveness corresponded to the variations in climate across Taiwan. These findings can assist health policymakers in designing regional strategies for the prevention of prenatal VDD.
To date, suboptimal vitamin D levels is mostly indicated for bone health but remain controversial across populations and countries. For some investigators, deficiency was defined as specific to bone; however, insufficiency was defined relating to other health outcomes. For others, deficiency covered diseased population and insufficiency covered at-risk population. One of the most commonly used definitions comes from the Endocrine Society Clinical Practice Guidelines [24]; vitamin D deficiency was defined as 25(OH)D values below 20 ng/mL (50 nmol/L), and vitamin D insufficiency was defined as 25(OH)D of 21–29 ng/mL (52.5–72.5 nmol/L). This guideline was accepted and used widely by the International Osteoporosis Foundation, American Association for Clinical Endocrinologists, Institute of Medicine, American Academy of Pediatrics, and government of Australia, New Zealand, Germany, Austria and Switzerland as well as in Taiwan. In any case, cut point is very important when looking at the results in 25(OH)D level.
Particularly in older adults, having a higher BMI or body fat percentage are significant subject-specific characteristics that negatively affect vitamin D metabolism [37]. Normal-weight women reached the higher 25(OH)d level after vitamin D supplementation faster than women with obesity [38]. However, in pregnant women, the association between BMI and VDD was not consistent across the studies. While several studies showed that high BMI was associated with VDD, others showed that BMI was not statistically significantly associated with VDD [39,40]. Obesity is strongly associated with insufficient dietary vitamin D intake and low sun exposure. Pre-pregnancy obesity predicts poor vitamin D status in mothers [41]. In our study, pre-pregnancy BMI (as a continuous variable) was significantly different in two groups of VDD and non-VDD, but in logistic regression, after adjusting for confounders, pre-pregnancy BMI was not significantly associated with VDD. The findings for BMI (as a categorical variable) were also insignificant in multiple logistic regression. Obesity is not associated with 25(OH)D levels in our study.
The present study is the first national report on vitamin D status among pregnant women in Taiwan. Our findings demonstrated specific differences in the effects of sunlight-related factors and vitamin D intake on vitamin D concentrations in distinct regions of Taiwan. However, several limitations should be considered. First, because this was a cross-sectional study, we can only note associations; we cannot determine the causal relationship. Second, several factors influencing vitamin D status were not assessed in our study, such as occupation and the brand and dose of supplements. Third, we used a self-report questionnaire, which may introduce assessment bias because of subjective responses. Fourth, although the present study highlights the critical role of dietary vitamin D intake, the data on nutrient quantitation per serving are unavailable.
## 8. Conclusions
VDD was prevalent in pregnant women in Taiwan. On the basis of our findings, we recommend the promotion of a robust health policy regarding safe sunlight exposure and effective dietary vitamin D intake, with adjustments according to the characteristics of various climate zones. In doing so, clinicians can enhance maternal vitamin D status, reduce the VDD-induced burden, and improve health and well-being.
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|
---
title: 'The Impact of Sarcopenia Onset Prior to Cancer Diagnosis on Cancer Survival:
A National Population-Based Cohort Study Using Propensity Score Matching'
authors:
- Chih-Hsiung Su
- Wan-Ming Chen
- Ming-Chih Chen
- Ben-Chang Shia
- Szu-Yuan Wu
journal: Nutrients
year: 2023
pmcid: PMC10005798
doi: 10.3390/nu15051247
license: CC BY 4.0
---
# The Impact of Sarcopenia Onset Prior to Cancer Diagnosis on Cancer Survival: A National Population-Based Cohort Study Using Propensity Score Matching
## Abstract
Purpose: The relationship between the onset of sarcopenia prior to cancer diagnosis and survival outcomes in various types of cancer is not well understood. To address this gap in knowledge, we conducted a propensity score-matched population-based cohort study to compare the overall survival of cancer patients with and without sarcopenia. Patients and Methods: In our study, we included patients with cancer and divided them into two groups based on the presence or absence of sarcopenia. To ensure comparability between the groups, we matched patients in both groups at a ratio of 1:1. Results: After the matching process, our final cohort included 20,416 patients with cancer (10,208 in each group) who were eligible for further analysis. There were no significant differences between the sarcopenia and nonsarcopenia groups in terms of confounding factors such as age (mean 61.05 years versus 62.17 years), gender ($52.56\%$ versus $52.16\%$ male, $47.44\%$ versus $47.84\%$ female), comorbidities, and cancer stages. In our multivariate Cox regression analysis, we found that the adjusted hazard ratio (aHR; $95\%$ confidence interval [CI]) of all-cause death for the sarcopenia group compared to the nonsarcopenia group was 1.49 (1.43–1.55; $p \leq 0.001$). Additionally, the aHRs ($95\%$ CIs) of all-cause death for those aged 66–75, 76–85, and >85 years (compared to those aged ≤65 years) were 1.29 (1.23–1.36), 2.00 (1.89–2.12), and 3.26 (2.97–3.59), respectively. The aHR ($95\%$ CI) of all-cause death for those with a Charlson comorbidity index (CCI) ≥ 1 compared to those with a CCI of 0 was 1.34 (1.28–1.40). The aHR ($95\%$ CI) of all-cause death for men compared to women was 1.56 (1.50–1.62). When comparing the sarcopenia and nonsarcopenia groups, the aHRs ($95\%$ CIs) for lung, liver, colorectal, breast, prostate, oral, pancreatic, stomach, ovarian, and other cancers were significantly higher. Conclusion: Our findings suggest that the onset of sarcopenia prior to cancer diagnosis may be linked to reduced survival outcomes in cancer patients.
## 1. Introduction
Sarcopenia is a condition marked by the reduction of muscle mass, strength, and physical performance [1,2]. It is generally defined as a decrease in appendicular muscle mass by two standard deviations below the mean for young, healthy adults [3]. Unlike cachexia, sarcopenia does not necessarily result from an underlying illness [4]. However, many patients with cachexia also have sarcopenia, while most people with sarcopenia do not have cachexia [4]. Sarcopenia is linked to higher rates of functional impairment, disability, falls, and death [5]. The causes of sarcopenia are complex and may include muscle disuse, changes in endocrine function, chronic diseases, inflammation, insulin resistance, and nutritional deficiencies [1]. Therefore, sarcopenia is distinct from cachexia and may occur before cancer develops.
Sarcopenia has a range of causes, including changes in endocrine function, proinflammatory cytokine activation, decreased alpha motor neurons in the spinal cord, reduced physical activity, and insufficient protein intake [6,7,8,9,10,11]. Research on the relationship between sarcopenia and cancer outcomes has produced conflicting results, with some studies showing an association between sarcopenia and poor cancer outcomes, while others have found no association [12,13,14,15,16]. These inconsistencies may be due to the inclusion of various cancer types, different definitions of sarcopenia (occurring before cancer diagnosis, related to cancer, or related to cancer treatment), and insufficient follow-up time in the studies [12,13,14,15,16]. Additionally, the measurement of oncological outcomes varies among studies [12,13,14,15,16].
The impact of sarcopenia on long-term survival appears to be significant across a broad range of cancer types. Sarcopenia diagnosis before a cancer diagnosis is crucial to differentiate cancer-related sarcopenia from cancer-treatment-induced sarcopenia. In this study, we used a head-to-head propensity score matching (PSM) approach including patients with cancer with and without sarcopenia to determine the oncological outcome of overall survival (OS) in these patients.
## 2.1. Study Cohort
For this study, we obtained data on patients with and without sarcopenia from the Taiwan Cancer Registry database (TCRD). These patients received a cancer diagnosis between 1 January 2008 and 31 December 2017, with the index date being the date of a cancer diagnosis. The follow-up period for these patients extended from the index date to 31 December 2019. The study protocols were reviewed and approved by the Institutional Review Board of Tzu-Chi Medical Foundation. In addition to the cancer registry database, we also used data from the Collaboration Center of Health Information Application, which provided additional information on cancer type, stage, and treatment for each patient [17]. We also tracked the vital status and cause of death of each patient.
## 2.2. Patients Selection
To be included in this study, patients had to be over the age of 20 and have a diagnosis of cancer without metastasis. We defined cancer patients as those with primary cancer. Patients with a history of cancer before the primary cancer diagnosis date (index data) were excluded from the study. The TCRD was used to verify the accuracy of all enrolled patients with primary cancer. Additionally, patients with synchronous or metachronous cancers were excluded from the cohort. To ensure that we included adult patients at risk of cancer, we defined our study population as those aged 20 years or older in Taiwan. Additionally, cancer patients with metastasis can have different survival outcomes depending on the extent of metastasis. Therefore, to avoid bias, we excluded patients with cancer and metastasis. Sarcopenia diagnosis was made before the cancer diagnosis date, and patients who did not have sarcopenia before the cancer diagnosis date were included as controls.
Sarcopenia is a muscle disease that results from adverse muscle changes that accumulate over a person’s lifetime. It is a common condition among older adults, but it can also occur earlier in life. The EWGSOP2 consensus paper defines sarcopenia as having low muscle strength as a key characteristic [18]. The diagnosis of sarcopenia is confirmed by detecting low muscle quantity and quality, while poor physical performance is indicative of severe sarcopenia. Therefore, sarcopenia was defined according to a previous study from the NHIRD [19] and was only recorded if it was diagnosed by rehabilitation specialists, orthopedics, or family physicians based on EWGSOP2 consensus [18].
The previous study employed the following protocol to define sarcopenia [19]: before 2016, there was no consensus on the definition of sarcopenia, and a variety of diagnostic criteria were being used [20]. In October 2016, the U.S. Centers for Disease Control and Prevention formally recognized sarcopenia as a disease, coding it as M62.84 in ICD-10-CM [21]. *In* general, the sarcopenia-related ICD-9-CM codes 728.2 and 728.9 can be considered equivalent to the ICD-10-CM code M62.84 [22]. The criteria have been used by other studies and are considered as similar to the diagnosis of sarcopenia [22]. In addition, the diagnosis of the sarcopenia-related ICD-9-CM codes 728.2 and 728.9 and ICD-10-CM code M62.84 were all verified by professional specialists (such as rehabilitation, orthopedic, or family physician). We defined the sarcopenia group in our study as “sarcopenia, muscular wasting, disuse atrophy, and disorder”.
## 2.3. Covariates and Propensity Score Matching
To analyze the time from the index date to all-cause death for patients with cancer with and without sarcopenia, we used a time-dependent Cox proportional hazards model that was adjusted for potential confounders. To account for potential confounders when comparing all-cause death between the sarcopenia and nonsarcopenia groups, we used propensity score matching. The variables used for matching included age, sex, Charlson comorbidity index (CCI) score, diabetes, hyperlipidemia, hypertension, end-stage renal disease (ESRD), liver cirrhosis, acute myocardial infarction (AMI), coronary artery disease (CAD), stroke, hepatitis B and C, congestive heart failure, dementia, chronic pulmonary disease, rheumatic disease, liver disease, diabetes with complications, hemiplegia and paraplegia, renal disease, acquired immune deficiency syndrome (AIDS), cancer type, cancer stage, income levels, and urbanization (see Table 1). We excluded repeat comorbidities from the CCI scores to prevent repetitive adjustment in the multivariate analysis. Cancer stages in our study were based on the clinical American Joint Committee on Cancer, Seventh Edition, which divides cancer types into early (stages I-II) and advanced (stages III-IV, with metastases removed) stages.
Comorbidities were identified based on ICD-9-CM or ICD-10-CM codes in the main diagnosis of inpatient records or if the patient had at least two outpatient visits within one year. Comorbidities that were present six months before the index date were recorded. Continuous variables are presented as the mean ± standard deviation or median (first quartile and third quartile), as appropriate. To match participants at a ratio of 1:1, we used the greedy method and matched participants with a propensity score within a caliper of 0.2 [23] based on the aforementioned covariates. Matching is a common technique for selecting controls with similar background characteristics to study participants in order to minimize differences between the two groups. We used a Cox model to perform the regression analysis of all-cause death in patients with cancer with and without sarcopenia and employed a robust sandwich estimator to account for clustering within the matched sets [24]. A multivariate Cox regression analysis was performed to calculate hazard ratios with $95\%$ confidence intervals (CIs) in order to identify potential independent predictors of all-cause death among the variables listed in Table 1.
## 2.4. Sensitivity Analysis
To understand the relationship between mortality and sarcopenia in patients with various types of cancer, a sensitivity analysis was conducted using inverse probability of treatment weighting (IPTW) for all-cause death in propensity score-matched sarcopenia and nonsarcopenia groups. The analysis adjusted for covariates listed in Table 2 and included all cancer types (as shown in Figure 1).
## 2.5. Statistical Analysis
The statistical analyses for this study were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA). The matching procedure was implemented using the PROC PSMATCH procedure in SAS [25]. A two-tailed Wald test was used, and a p value of less than 0.05 was considered statistically significant. Overall survival (OS) was estimated using the Kaplan–Meier method and the differences in OS between the sarcopenia and nonsarcopenia groups with cancer were determined using the stratified log-rank test to compare survival curves, stratified according to the matched sets [26].
## 3.1. Study Cohort
There was a total of 103,925 cancer patients included in the registry during the selected time frame. Before PSM, out of all the cancer patients included in the registry, $14.9\%$ were diagnosed with sarcopenia prior to their cancer diagnosis. This means that there were 15,527 sarcopenic cancer patients out of the total of 103,925 cancer patients, while the remaining 88,398 were nonsarcopenic cancer patients. Therefore, the percentage of sarcopenic patients out of all cancer patients is approximately $14.9\%$.
Propensity score matching resulted in a final study cohort of 20,416 patients, with 10,208 in both the sarcopenia and nonsarcopenia groups. The characteristics of these patients are listed in Table 1. The age distribution was balanced between the two groups (Table 1). Additionally, after head-to-head PSM, there were no significant differences in sex distribution, CCI score, diabetes, hyperlipidemia, hypertension, ESRD, liver cirrhosis, AMI, CAD, stroke, hepatitis B and C, congestive heart failure, dementia, chronic pulmonary disease, rheumatic disease, liver disease, diabetes with complications (severe diabetes), hemiplegia and paraplegia, renal disease, AIDS, cancer type, cancer stage, income levels, and urbanization between the two groups. The primary endpoint of all-cause death in the sarcopenia group (before cancer diagnosis) was significantly different from the nonsarcopenia group ($p \leq 0.001$; Table 1).
## 3.2. Multivariate Cox Regression Analysis
The results of the multivariate Cox regression analysis indicated that patients with cancer and sarcopenia before cancer diagnosis had a shorter OS compared to those without sarcopenia (see Table 2). The adjusted hazard ratio (aHR; $95\%$ CI) of all-cause mortality for the sarcopenia group compared to the nonsarcopenia group was 1.49 (1.43 to 1.55; $p \leq 0.001$). Several explanatory variables were found to be significantly associated with an increased risk of all-cause mortality. These included older age, being male, and having a CCI score of 1 or higher. Specifically, the aHRs ($95\%$ CIs) of all-cause mortality for those aged 66 to 75, 76 to 85, and over 85 years (compared to those aged 65 or younger) were 1.29 (1.23 to 1.36), 2.00 (1.89 to 2.12), and 3.26 (2.97 to 3.59), respectively (see Table 2). The aHR ($95\%$ CI) of all-cause mortality for those with a CCI score of 1 or higher compared to those with a CCI score of 0 was 1.34 (1.28 to 1.40). The aHR ($95\%$ CI) of all-cause mortality for men compared to women was 1.56 (1.50 to 1.62). No other explanatory variables were found to be significantly associated with an increased risk of all-cause mortality.
## 3.3. Sensitivity Analysis for Cancer Types
A stratified analysis based on IPTW was conducted to examine the distinct age groups and CCI scores, and the results are presented in a forest plot in Figure 1. Among patients with lung, liver, colorectal, breast, prostate, oral, pancreatic, stomach, ovarian, and other types of cancer, the adjusted hazard ratios (aHRs; $95\%$ confidence intervals [CIs]) for all-cause mortality in the sarcopenia group were 1.17 (1.06 to 1.29), 1.11 (1.01 to 1.22), 1.53 (1.38 to 1.71), 2.18 (1.81 to 2.63), 1.57 (1.28 to 1.92), 1.54 (1.30 to 1.83), 1.31 (1.02 to 1.68), 1.43 (1.21 to 1.69), 1.97 (1.32 to 2.93), and 1.56 (1.46 to 1.66), respectively. These aHRs were significantly associated with higher mortality in the sarcopenia group compared to the nonsarcopenia group, regardless of the age group, sex, or CCI score range (as shown in Figure 1). In addition, the aHR ($95\%$ CI) for all-cause mortality for patients with esophageal cancer was 1.24 (0.97 to 1.59, $$p \leq 0.0814$$) in the sarcopenia group compared to the nonsarcopenia group.
## 3.4. Sensitivity Analysis of CCI Score, Age Groups, and Sex
The results of the stratified analysis of the distinct age groups and CCI scores based on IPTW are presented as a forest plot in Figure 2. Among patients with cancer, the aHRs ($95\%$ CIs) for all-cause mortality for the sarcopenia group were 1.81 (1.71 to 1.92) for those with a CCI score of 0, 1.26 (1.20 to 1.33) for those with a CCI score of 1 or higher, 1.49 (1.41 to 1.56) for men, 1.50 (1.41 to 1.60) for women, 1.84 (1.73 to 1.96) for those aged 65 or younger, 1.37 (1.27 to 1.47) for those aged 66 to 75, and 1.24 (1.14 to 1.34) for those aged 76 to 85. These aHRs were significantly associated with higher mortality in the sarcopenia group compared to the nonsarcopenia group, regardless of the cancer type (as shown in Figure 2). Poor OS in relatively healthy individuals (those with a CCI score of 0), female sex, and younger age group (65 or younger) were more significant in the sarcopenia group than in the nonsarcopenia group.
## 3.5. Kaplan–Meier Survival Curves
Figure 3 presents the survival curve (in terms of OS) for the propensity score–matched sarcopenia (diagnosed with sarcopenia before cancer treatment) and nonsarcopenia groups, calculated using the Kaplan–Meier method. The 5-year OS for individuals who did not use opioids was $69.97\%$, while the 5-year OS for those who used long-term opioid analgesics was $51.82\%$ ($p \leq 0.001$). This difference in OS between the two groups was statistically significant.
## 4. Discussion
Cachexia is different from sarcopenia, which refers to the loss of skeletal muscle mass that is often two SDs below values that are adjusted for sex and age [27]. Most of the individuals with cachexia have sarcopenia, whereas most of the patients with sarcopenia do not have cachexia [27]. Muscle loss without fat loss is known as sarcopenic obesity, which is prevalent in older adults and is noted in patients with advanced cancer [28,29,30]. Sarcopenia can have various causes, including disuse atrophy, changes in endocrine function, chronic diseases, inflammation, insulin resistance, nutritional deficiencies, and certain cancer treatments such as sorafenib and androgen deprivation [6,7,8,9,10,11,31,32,33]. To investigate the effect of sarcopenia as a predictor of OS on patients with cancer, we included only patients who were diagnosed as having sarcopenia before cancer diagnosis to exclude cancer-related or cachexia-related sarcopenia and cancer-treatment-related sarcopenia. In this study, we aimed to investigate the impact of sarcopenia on the overall survival of cancer patients. To the best of our knowledge, our study has the largest sample size and the longest follow-up period compared to other studies that have examined the relationship between OS and sarcopenia in cancer patients.
Sarcopenia has been shown to be a significant predictor of survival in various types of cancer [12]. Sarcopenia has gained attention in the field of oncology due to its potential impact on cancer prognosis and the associated financial strain it can place on individuals and society [34]. Sarcopenia is a hallmark of cachexia [35]. Sarcopenia in cancer patients has been linked to reduced tolerance to anticancer treatment, increased susceptibility to cancer-treatment-related complications such as infection and immobility, and an increased risk of comorbidities [31]. These factors can contribute to higher mortality rates in cancer patients with sarcopenia compared to those without sarcopenia [31]. According to one study, reversing muscle wasting in a cancer cachexia model was found to improve survival outcomes. Additionally, a randomized controlled trial (RCT) showed that pharmacological agents can be effective in increasing muscle mass in cancer cachexia [36,37]. Therefore, it is crucial to acknowledge that sarcopenia can be modified in cancer patients. Our results indicated that sarcopenia onset before cancer is a poor prognostic factor for OS (Table 2 and Figure 1, Figure 2 and Figure 3); hence, early detection and treatment of sarcopenia are crucial and might be associated with improved OS in patients with cancer in the future. To determine whether reversing sarcopenia before cancer diagnosis could improve the OS of cancer patients, an RCT is necessary.
Several previous meta-analyses have indicated that there is a link between sarcopenia and increased mortality in cancer patients [38,39,40]. However, it is not clear whether the findings from these meta-analyses, which have mostly focused on specific types of cancer, can be generalized to a wider range of cancer types [38,39,40]. The survival effect of sarcopenia diagnosis before cancer diagnosis in different cancer types remains largely unclear. Furthermore, multiple endpoints have been noted in previous studies that contributed to heterogeneous outcomes [12,13,14,15,16] and a comparative long-term study on the survival of head-to-head PSM sarcopenia and nonsarcopenia groups is still lacking. In addition, insufficient sample sizes have been used in comparative studies for sarcopenia and nonsarcopenia in a wide spectrum of cancer types for OS outcomes. Our study has the largest sample size among studies examining the survival effect of sarcopenia in patients with a wide spectrum of cancer. Moreover, our mean follow-up time for the sarcopenia and nonsarcopenia groups including the patients with cancer was >5 years; this follow-up period was sufficient to evaluate survival outcomes (Table 1).
All confounding factors associated with mortality were balanced between the sarcopenia and nonsarcopenia groups receiving cancer treatment (Table 1). Cancer types and clinical cancer stages were homogenized between the sarcopenia and nonsarcopenia groups through PSM to evaluate the true survival effect of sarcopenia on patients with cancer because patients with different cancer types and cancer stages have different survival durations. As shown in Table 1, the crude all-cause death rate after PSM was significantly higher in the sarcopenia group than in the nonsarcopenia group. The head-to-head PSM design allows for an observational (nonrandomized) study approach that is similar to a randomized controlled trial (RCT) in some ways [41]. After PSM in our study, we believe that balanced covariates mimic an RCT without selection bias for the sarcopenia and nonsarcopenia groups [41]. According to the results of multivariate Cox proportional analysis (as shown in Table 2), the onset of sarcopenia before cancer diagnosis was found to be an independent predictor of all-cause mortality in cancer patients. Our literature review showed that our study had the largest sample size, longest follow-up period, and widest range of cancer types of any study using PSM to investigate whether the onset of sarcopenia before a cancer diagnosis is a significant predictor of OS in cancer patients. Figure 3 presents the results of this comparative study. Additionally, the results of the multivariate analysis showed that old age, a CCI of 1 or higher, and being male were poor prognostic factors for OS, as seen in Table 2. This finding is consistent with the results of previous studies on various types of cancer [42,43].
Our sensitivity analysis of a broad range of cancer types (including the top 10 most common cancers in Taiwan) found that sarcopenia significantly increased the risk of all-cause mortality in patients with lung, liver, colorectal, breast, prostate, oral, pancreatic, stomach, ovarian, and other types of cancer (as shown in Figure 1). Our study demonstrated that sarcopenia onset before a cancer diagnosis is an independent predictor of OS. Our findings are partially consistent with those of previous studies that have examined specific cancer types and used ill-defined definitions of sarcopenia, including sarcopenia that occurs before, after, or during cancer diagnosis [12,13,14,15,16]. One of the disadvantages of using an ill-defined definition of the time interval for sarcopenia (such as before, after, or during cancer diagnosis) is that the conclusions may be biased if the presence of cancer-related cachexia or cancer treatment-induced sarcopenia is not taken into account, rather than noncancer-related sarcopenia. Cancer-related cachexia or cancer-treatment-induced sarcopenia is different from sarcopenia prior to cancer diagnosis because the mechanisms are different for noncancer sarcopenia, cancer-related sarcopenia, and cancer-treatment-related sarcopenia [6,7,8,9,10,11,31,32,33]. Unlike cachexia, sarcopenia does not necessarily involve weight loss [28,29,30]. Noncancer-related sarcopenia can be caused by a range of factors, including disuse, changes in endocrine function, nutritional deficiencies, chronic diseases, inflammation, and insulin resistance [6,7,8,9,10,11]. Cancer-induced or cancer-treatment-induced sarcopenia may be attributed to cachexia or anticancer treatments in patients with cancer [6,7,8,9,10,11,28,29,30,31,32,33]. Toxicity caused by cancer-related-inflammation-induced cachexia and cancer treatments might be contributed to cancer-related sarcopenia instead of noncancer sarcopenia [6,7,8,9,10,11,28,29,30,31,32,33]. Our study focused on noncancer sarcopenia diagnosed prior to cancer diagnosis, and we excluded cancer-related sarcopenia to avoid the bias of different cancer types or different treatments.
After conducting a sensitivity analysis of the CCI, sex, and age (which were identified as significant independent factors for OS in cancer patients in Table 2), we found that a diagnosis of sarcopenia prior to cancer diagnosis remained a significantly poor prognostic factor for OS (as shown in Figure 2), regardless of CCI score, sex, or age group. However, sarcopenia did not have a significant effect on OS in cancer patients who were over the age of 85. Moreover, in sensitivity analysis, the aHR of OS was lower in men, patients with CCI ≥ 1, and older patients. This finding might be because the patients with cancer with a significantly high risk of mortality, such as those with CCI ≥ 1, male patients, and older patients (Table 2), had a shorter life expectancy than did those with CCI = 0, female patients, or younger patients. Therefore, the survival effect of sarcopenia might be masked by patients with cancer with a shorter life expectancy (CCI ≥ 1, male sex, and old age), contributing to decreased aHRs (Figure 2). Therefore, a diagnosis of sarcopenia prior to cancer diagnosis was not a significant prognostic factor for OS in cancer patients who were older (over the age of 85) and had a relatively short life expectancy (as shown in Figure 2).
One of the strengths of our study is that it is the first, largest, and longest-term follow-up comparative cohort study to examine the primary endpoint of OS in cancer patients with and without sarcopenia. To eliminate selection bias, PSM was used to ensure that the covariates between the two groups were homogenous for cancer patients (as seen in Table 1). This is the first study to investigate this relationship in such a comprehensive and in-depth manner. Studies estimating the survival effect of sarcopenia onset before cancer diagnosis on all-cause death in a wide spectrum of cancer types are rare. In our study, we found that poor prognostic factors for OS in cancer patients included the onset of sarcopenia before cancer diagnosis, high CCIs, being male, and being of advanced age (as seen in Table 2 and Figure 2 and Figure 3). These findings are consistent with those of previous cancer studies [42,43]. Our findings suggest that the onset of sarcopenia before cancer diagnosis may be associated with poorer OS in patients with lung, liver, colorectal, breast, prostate, oral, pancreatic, stomach, ovarian, and other types of cancer, compared to those without sarcopenia (as shown in Figure 1). To our knowledge, this is the first study to report that the impact of sarcopenia onset prior to cancer diagnosis on survival was stronger in cancer patients with a longer life expectancy, such as those with breast, prostate, or colorectal cancer, a CCI of 0, female patients, or younger patients (as seen in Figure 1 and Figure 2). In the patients with cancer with a longer life expectancy, a higher aHR of sarcopenia for mortality was noted in patients with cancer than in those with cancer but without sarcopenia. By contrast, the survival effect of sarcopenia was lower and masked, especially in the patients with cancer with a shorter life expectancy such as those with liver cancer, esophageal cancer, pancreatic cancer, CCI ≥ 1, male patients, or older patients (Figure 1 and Figure 2). Previous studies have not specifically focused on the impact of sarcopenia onset before cancer diagnosis in a wide range of cancer types. Our study is the first to examine the effects of this factor on all-cause mortality and in a diverse group of cancer types. Our findings should be taken into account in future clinical practice and prospective clinical trials to prevent or treat sarcopenia onset before cancer treatment, particularly in relatively healthy patients (those with a CCI of 0), women, younger patients, and those with cancer types that have a longer survival prognosis (as shown in Figure 1 and Figure 2).
This study has a few limitations that should be noted. Firstly, as all the patients enrolled in the study were Asian, it is unclear whether these results apply to non-Asian populations. However, there is no evidence to suggest that there are significant differences in oncological outcomes between Asian and non-Asian cancer survivors. Secondly, the diagnoses of all comorbid conditions were based on ICD-9-CM or ICD-10-CM codes. While the Taiwan Cancer Registry Administration regularly reviews medical charts and interviews patients to verify the accuracy of diagnoses, a large-scale randomized trial comparing carefully selected patients with sarcopenia onset prior to cancer diagnosis and those without sarcopenia would be necessary to gain more specific information on the population characteristics and disease occurrence. However, it is important to note that these measures do not completely eliminate the possibility of error in diagnosis, and hospitals with outlier charges or practices may be audited and face penalties if malpractice or discrepancies are identified. Finally, it is worth noting that the Taiwan Cancer Registry database does not include data on dietary habits or body mass index, which may be risk factors for OS. Despite this limitation, the study has several strengths, including the use of a nationwide population-based registry with detailed baseline information and the ability to conduct long-term follow-up through the linkage of the registry with the national Cause of Death database. The observed effects in this study were both statistically significant and of a large magnitude, indicating that they are unlikely to be affected by these limitations.
## 5. Conclusions
Our findings suggest that the onset of sarcopenia before cancer diagnosis may be associated with a reduction in OS and poorer OS in patients with lung, liver, colorectal, breast, prostate, oral, pancreatic, stomach, ovarian, and other types of cancer compared to those without sarcopenia. The impact of sarcopenia onset prior to cancer diagnosis on survival was stronger in patients with a longer life expectancy, such as those with breast, prostate, or colorectal cancer, a CCI of 0, female patients, or younger patients.
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|
---
title: A Study on Acute Myocardial Infarction and Its Prognostic Predictors
journal: Cureus
year: 2023
pmcid: PMC10005819
doi: 10.7759/cureus.34775
license: CC BY 3.0
---
# A Study on Acute Myocardial Infarction and Its Prognostic Predictors
## Abstract
Introduction Acute Myocardial Infarction (AMI) is a serious cardiac event characterized by the sudden death of heart muscle tissue due to the obstruction of blood flow to the heart. It is a leading cause of death and disability worldwide. The relationship between AMI and serum uric acid levels is an area of ongoing research. Serum uric acid is a byproduct of purine metabolism and is typically present in the blood at low levels. Elevated levels of uric acid have been linked to several cardiovascular risk factors, including hypertension, diabetes, and hyperlipidemia. This has led to the investigation of the relationship between uric acid levels and AMI.
Materials and Methods *In this* study, 100 individuals who were presented with acute myocardial infarction were included. All patients were categorized into four Killip’s classes based on history, clinical examination, and lab investigation. Subsequently, the four Killip’s classes were co-related with the serum uric acid of the patient.
Results Serum uric acid levels were high in males compared to females. serum uric acid levels were high in Killip’s class III (7.24) and IV (7.57) compared to class I (4.48) and II (5.26). There was no significant correlation between serum uric acid and the co-morbidities like diabetes and hypertension, with a p-value of 0.48.
Conclusion An increase in Killip *Class is* positively correlated with an increase in blood uric acid levels. Uric acid can therefore be utilized as a prognostic indicator in individuals who present with myocardial infarction.
## Introduction
Non-communicable diseases are becoming more significant since they have displaced infectious diseases as the leading cause of disability, morbidity, and premature mortality, demonstrating the epidemiological shift [1]. In India, cardiovascular disease (CVD) is a silent epidemic, and the prevalence of heart diseases has increased fourfold during the past 40 years. The number of CVD patients increased dramatically from 271 million in 1990 to 523 million in 2019, as did mortality, which increased from 12.1 million in 1990 to 18.6 million in 2019 [2]. Rapid urbanization and lifestyle modifications such as inactivity, an unwholesome diet, obesity, dyslipidemia, smoking, increased blood pressure, and diabetes has increased the prevalence of coronary heart disease during the past 20 years [3,4]. In conclusion, acute coronary syndrome (ACS) is a kind of CVD, and cardiovascular disease (CVD) is an umbrella term that covers a variety of illnesses that affect the heart and blood vessels.
The term “acute coronary syndrome" (ACS) refers to a variety of conditions. Acute Myocardial Infarction (AMI) can present as Non-ST Elevation Myocardial Infarction (NSTEMI), ST Elevation MI (STEMI), or unstable angina (UA) [5]. Notably, all of these illnesses may have a similar clinical appearance and symptoms. In ACS, relieving or limiting ischemia, preventing reinfarction, and improving outcome and well-being are the main therapy objectives. AMI risk stratification is carried out using several clinical evaluations and scores; Killip’s classification is one of them [6]. Killip classified AMI patients into four groups to detect the severity of left ventricular dysfunction and predict mortality.
Class I: No signs of LV failure Class II: Presence of S3 heart sound on auscultation and basal crepitation Class III: *Pulmonary edema* Class IV: Cardiogenic shock
The predicted mortality rates for the four classes are $6\%$, $17\%$, $38\%$, and $81\%$, respectively [6].
A significant risk factor and reliable predictor of cardiovascular illnesses is arterial stiffness [7]. Age and other medical conditions exacerbate arterial stiffness, which indicates vascular flexibility and function. According to some studies, uric acid can cause vascular endothelial dysfunction by inducing oxidative stress and inhibiting endothelial nitric oxide synthase, as well as by encouraging the growth of vascular smooth muscle cells and amplifying the vasoconstrictive effects of angiotensin II, endothelin, and thromboxane, which lead to subclinical changes in arterial structure [8]. Additionally, there is a strong relationship between uric acid and markers of arterial stiffness. Uric acid is a significant risk factor and predictor of cardiovascular events, such as acute myocardial infarction, atherosclerosis, and stroke [7,9]. With this knowledge, this study aims to investigate the association between Killip's class and serum uric acid levels in acute myocardial infarction.
## Materials and methods
A descriptive study was conducted in a tertiary care hospital in Guntur from April 2021 to September 2022 among 100 patients who were admitted to the emergency room with resting chest pain lasting more than 30 minutes after gaining ethics committee approval. Patients or their families were asked for their informed consent. Until a sample size of 100 was reached, a convenience sampling technique was utilized.
Inclusion and exclusion criteria Patients with STEMI or NSTEMI with resting chest pain lasting more than 30 minutes, new ST/T changes or new left bundle branch block, or presence of pathological Q waves on ECG or ECHO showing regional wall motion abnormality and raised cardiac enzymes (CK-MB, troponins) more than 99th percentile for the upper reference value, were all included in the study. Patients with other illnesses and drugs which are known to raise SUA levels were excluded from the study.
A semi-structured questionnaire was used to collect patients' socio-demographic details, presenting complaints, risk factors, and other pertinent clinical data. Blood pressure, random blood sugar, ECG or ECHO, chest X-ray, Killip's classification, and COBRA INTEGRA/COBAS C SYSTEM, which uses the uricase method to determine the quantity of uric acid in serum, were utilized as study tools. According to a study by Kuwabara M., the reference range for uric acid was 3 to 5 mg/dl in males and 2 to 4 mg/dl in females. Clinical examination, auscultatory findings for class II, chest X-ray findings for class III, and vital signs, clinical condition, and ECG of the patient for class IV were used to classify patients into Killip's classes. The pertinent data is subsequently entered into a Microsoft Excel master chart (Redmond, USA) and statistically assessed by IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.
## Results
The 100 patients in the current study had either STEMI or NSTEMI, and their ages ranged from 53.76 ± 8.056. Sixty-five percent of the study subjects were males, and out of them, 52 were smokers. Additionally, a large majority of persons, $66\%$ and $72\%$ of them, respectively, have a history of hypertension and diabetes mellitus. The distribution of sociodemographic profiles and risk factors were shown in Table 1.
**Table 1**
| Variables | Percentage (%) |
| --- | --- |
| Age in years | Age in years |
| 35-39 | 8.0 |
| 40-44 | 6.0 |
| 45-49 | 14.0 |
| 50-54 | 28.0 |
| 55-59 | 18.0 |
| 60-64 | 17.0 |
| >65 | 9.0 |
| Gender | Gender |
| Male | 65.0 |
| Female | 35.0 |
| History of smoking | History of smoking |
| Non- smokers | 47.0 |
| Smokers | 53.0 |
| Distribution of diabetes | Distribution of diabetes |
| Non- diabetics | 34.0 |
| Diabetics | 66.0 |
| Distribution of hypertension | Distribution of hypertension |
| Non- hypertensives | 30.0 |
| Hypertensives | 70.0 |
Out of the 100 individuals, $47\%$ had anterior wall MI, $12\%$ had anterior wall MI and lateral wall MI, $34\%$ had inferior wall MI, and $7\%$ had non-ST elevation MI (Table 2).
**Table 2**
| Region of the AMI | Percentage (%) |
| --- | --- |
| AWMI | 47.0 |
| AWMI/LWMI | 12.0 |
| IWMI | 34.0 |
| NSTEMI | 7.0 |
| Total | 100.0 |
KILLIP classes I and II made up around $80\%$ of the 100 patients in our study, whereas classes III and IV ($20\%$) (Figure 1).
**Figure 1:** *Distribution of study population based on KILLIP’S class.*
Males have higher mean uric acid levels compared to females (Figure 2).
**Figure 2:** *Distribution of mean uric acid levels among males and females.*
In the present study, SUA levels were substantially higher in class IV and class III patients. By using the Kruskal-Wallis test, the mean variance was statistically significant ($$p \leq 0.000$$), as shown in Table 3 and Figure 3.
By using the Kruskal-Wallis test, it was shown that there was no statistically significant correlation between mean SUA levels and the presence of diabetes or hypertension ($$p \leq 0.484$$) (Table 4).
**Table 4**
| Adjustable | Mean SUA | SD | P-value |
| --- | --- | --- | --- |
| Have- hypertension and diabetes | 5.545 | 1.547 | 0.484 |
| Either diabetes or hypertension seen | 5.029 | 1.3445 | 0.484 |
| Don’t have hypertension/diabetes | 4.781 | 0.4163 | 0.484 |
| Total | 5.223 | 1.418 | 0.484 |
## Discussion
In this study, blood uric acid levels in AMI were measured and correlated with Killip class among 100 study subjects. Our investigation showed that SUA levels are higher in Killip’s classes III and IV, with a mean of 7.57 and 7.24, respectively. Similarly, a study by Nadkar MY et al. [ 10] revealed that SUA levels were higher in classes III and IV compared to classes I and II. Another study by Bos MJ et al. [ 9] showed that SUA is the strongest risk factor for AMI. To contradict these studies, a study by Chen L et al. [ 11] stated that SUA levels were positively correlated with serum triglyceride levels but not with the severity of coronary artery disease. Whereas a study by Xue T et al. [ 12] revealed that only a stable high level of SUA was associated with an increased risk of MI, while a change in SUA in any direction was not associated with the risk of MI. A study by Kojima S et al. [ 13] stated that the combination of SUA and Killip’s class is a good predictor of mortality in patients with MI. A study conducted by Maryam M et al. [ 14] showed that patients with heart failure (the cases group), as compared to the control group, demonstrated a noticeably higher amount of uric acid. Additionally, individuals with STEMI had uric acid levels that were significantly greater than those with heart failure who did not have a STEMI. A study by Tsai TH [15] showed that even in patients undergoing primary percutaneous coronary intervention, Killip III continues to be a highly and independently reliable predictor of 30-day and one-year death in ST-segment elevation myocardial infarction patients.
According to our study and also stated by Taniguchi Y et al. [ 16], there was no significant relationship between hypertension and diabetes and SUA levels in our study. [ 16]. To contradict this, many studies proved a positive relationship between diabetes and SUA [17,18]. However, it was unclear why earlier research discovered a positive relationship between uric acid and diabetes.
Limitations To find out the results of the interaction between SUA and Killip's class, we did not follow up with the patients. We conducted our study with 100 participants; a larger sample size could produce more conclusive results.
## Conclusions
Based on the above findings, we conclude that the SUA levels are directly proportional to Killip’s class. Therefore, the risk of mortality due to AMI increases as uric acid levels rise. So, we can use serum uric acid levels as a predictor of cardiovascular disease.
## References
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|
---
title: Differentially Expressed Genes Analysis in the Human Small Airway Epithelium
of Healthy Smokers Shows Potential Risks of Disease Caused by Oxidative Stress and
Inflammation and the Potentiality of Astaxanthin as an Anti-Inflammatory Agent
authors:
- Irandi Putra Pratomo
- Aryo Tedjo
- Dimas R. Noor
- Rosmalena
journal: International Journal of Inflammation
year: 2023
pmcid: PMC10005861
doi: 10.1155/2023/4251299
license: CC BY 4.0
---
# Differentially Expressed Genes Analysis in the Human Small Airway Epithelium of Healthy Smokers Shows Potential Risks of Disease Caused by Oxidative Stress and Inflammation and the Potentiality of Astaxanthin as an Anti-Inflammatory Agent
## Abstract
Cigarette smoke (CS) was known for its effect of increasing oxidative stress that could trigger tissue injury and endothelial dysfunction mediated by free radicals and reactive oxygen species (ROS). ROS itself is a key signaling molecule that plays a role in the development of inflammatory disorders. Nuclear factor erythroid2 related factor2 (Nrf2) is the main regulator of antioxidant cellular response to cell and tissue-destroying components caused by CS. Nrf2 protein that is significantly activated in the smokers' small airway epithelium is followed by a series of gene expression changes in the same cells. This study aims to observe differentially expressed genes (DEGs) in the human small airway epithelium of smokers compared to genes whose expression changes due to astaxanthin (AST) treatment, an antioxidant compound that can modulate Nrf2. Gene expression data that was stored in the GEO browser (GSE 11952) was analyzed using GEO2R to search for DEG among smokers and nonsmokers subject. DEG was further compared to those genes whose expression changes due to astaxanthin treatment (AST) that were obtained from the Comparative Toxicogenomics Database (CTD; https://ctdbase.org/). DEG ($p \leq 0.05$) analysis result shows that there are 23 genes whose expression regulation is reversed compared to gene expression due to AST treatment. *The* gene function annotations of the 23 DEGs showed the involvement of some of these genes in chemical and oxidative stress, reactive oxygen species (ROS), and apoptotic signaling pathways. All of the genes were involved/associated with chronic bronchitis, adenocarcinoma of the lung, non-small-cell lung carcinoma, carcinoma, small cell lung carcinoma, type 2 diabetes mellitus, emphysema, ischemic stroke, lung diseases, and inflammation. Thus, AST treatment for smokers could potentially decrease the development of ROS and oxidative stress that leads to inflammation and health risks associated with smoking.
## 1. Introduction
Oxidative stress occurs due to an imbalance between the increased production of free radicals and decreased antioxidant capacity [1]. Under physiological conditions, oxidative stress will trigger an increase in the expression of endogenous antioxidant genes and cytoprotective proteins to prevent or limit tissue damage. This process is mediated by nuclear factor erythroid2 related factor2 (Nrf2) activity which then activates transcription way for antioxidant gene and enzyme detoxification [1, 2]. Thus, impaired activation of Nrf2 will cause a decrease in antioxidant capacity.
Cigarette smoke (CS) component that is dissolved in water is known to directly increase oxidative stress that could trigger tissue injury. Smoking tobacco has also been associated with vascular endothelium dysfunction through causative methods depending on the dose. This is mainly related to tobacco content of reactive oxygen species (ROS), nicotine, and inflammation driven by oxidative stress [3]. In particular, chronic CS exposure to respiratory tract tissue causes an increase in radical concentration, volatile compound (particularly oxygen species and reactive nitrogen), and CS condensate deposition, which will trigger a pleiotropic adaptive response, aimed at restoring tissue homeostasis [4]. Chronic exposure to CS generally encounters a cellular defense system characterized by activation of Nrf2. Nrf2 as the main regulator of antioxidant cellular response is proven to regulate the first line of defense against CS-induced cell and tissue-damaging components. This is indicated by the higher expression of Nrf2 in PBMC in moderate smokers compared to nonsmokers ($p \leq 0.01$). An increase in Nrf2 was not found in heavy smokers who possess a high level of nuclear transcription factor (NF-kB) and C-reactive protein (CRP) ($p \leq 0.01$) [5]. This indicates disruption of the Nrf2 role in heavy smokers with an inflammatory problem. *Nrf2* genetic effect also affects smokers' health status. This is indicated by the significant interaction between genotype rs6726395 with accompanied by the decrease of forced expiratory volume in one second (FEV1) ($$p \leq 0.011$$) [6]. The Haplotype rs2001350T/rs6726395A/rs1962142A/rs2364722A/rs6721961T is also associated with a lower annual decline in FEV1 ($$p \leq 0.004$$) [6].
Astaxanthin (AST) is a food xanthophyll that is often found in sea organisms, and because of its unique molecular feature, it possesses good antioxidant activity. More evidence has suggested AST's protective role to counter several diseases where oxidative stress and inflammation occur continuously. AST is known to modulate Nrf2 binding to antioxidant response elements (AREs) in the promoter region of most cytoprotective or detoxifying enzymes [7]. Recent studies have also shown that AST modulates the NF-B signaling network by increasing inflammation and oxidative stress in various experimental models [8, 9]. Several studies have shown that the anti-aging effect, as well as attenuation of oxidative stress and inflammation of AST, is carried out through Nrf2 activation and NF-kB inhibition [10–12].
Differentially Expressed Gene (DEG) is important to understand the biological difference between a healthy and ill condition. Identification of genes involved in disease is an important tool for revealing the molecular mechanisms of disease development. In pharmaceutical and clinical studies, DEG also plays an important role to choose biomarker candidates, therapeutic targets, and genetic signatures for diagnosis [13]. In this study, an analysis of changes in gene expression patterns due to smoking was carried out in the Gene Expression Omnibus (GEO) database [14, 15] which was compared with changes in profile expression genes due to AST treatment obtained from Comparative Toxicogenomics Database (CTD; https://ctdbase.org/) [16]. By comparing these two research data, it is intriguing to know what genes have the potential expression to be affected by AST, so it is hoped that it could further explain the potential of AST as a candidate for antioxidant supplements in terms of its mechanism of action in reducing the health effects that can appear on smokers.
## 2. Methods
This study gathers data from Gene Expression Omnibus (GEO) database, a study conducted by Hübner et al. [ 17]. The inclusion criteria of a healthy nonsmoker and smoker referred to those study. Healthy nonsmoker was people with normal physical examination, lung function, and chest X-ray, with smoking-related blood and urine within the nonsmoker range. The criteria for a healthy smoker were current smoking history, followed by normal physical examination, lung function, chest X-ray, and smoking-related blood and urine parameters consistent with current smokers. In the study, the age of the subjects was not distinguished [17]. Human small airway epithelium samples was obtained using fiber-optic bronchoscopy of 38 healthy nonsmokers and 45 healthy smokers, and Nrf2-associated gene expression was assessed using the Affymetrix HG-U133 Plus 2.0 microarray. Compared to healthy nonsmokers, it was found that the Nrf2 protein was significantly activated in the human small airway epithelium of healthy smokers and localized in the nucleus ($p \leq 0.05$). The research gene expression data stored in the GEO browser (GSE 11952) was then analyzed using GEO2R to look for DEG between smokers and nonsmokers subjects. Furthermore, DEGs of smoker's vs nonsmokers were compared with genes that changed expression due to AST treatment obtained from the CTD, with the target of finding genes that were opposite in expression between the 2 datasets.
Genes with opposite expressions were then made into protein networks and clustered using STRING (string-db.org) [18]. In these genes, gene function annotations were made using the GO biological process [19, 20]. Relationships between genes and smoking-related diseases obtained from CTD. At this stage, it was expected to know the role of AST on changes in biological processes that occur in smokers and the diseases that can accompany them based on gene expression profiles.
## 3. Results
The results of DEG analysis of research data from Hübner et al. [ 17] stored in the GEO browser (GSE 11952), showed that there were 4912 significantly differentially expressed genes (DEGs) in the human small airway epithelium of smokers compared to nonsmokers ($p \leq 0.05$). If the 4192 DEGs was compared with genes or proteins whose expression changed because of AST administration obtained from CTD, then the results are as shown in Table 1.
Table 1 shows 23 Nrf2-related genes/proteins that expression regulation was opposite between smokers (against nonsmokers) with the effect of AST treatment. The effect of AST in influencing the gene expression/protein could happen directly or indirectly. In the case of it happening indirectly, the AST slows down the reaction that influences a particular gene expression/protein. For instance, slowing down LEPR mutant reaction. LEP is known to be associated with leptin receptor (LEPR) and took part in activating several intracellular signaling channels [21]. The increase of LEP in the lungs and serum is associated with potentially worsening or hastening the development of lung diseases, including acute lung injury (ALI), acute respiratory distress syndrome, chronic obstructive pulmonary disease (COPD), airway remodeling associated with asthma, and lung cancer [21]. In addition, the presence of polymorphism LEPR is known to show a statistically significant difference between lung cancer patients and controls ($$p \leq 0.007$$) [22]. LEPR mutant is also known to cause kidney [23] and bone marrow fibrosis [24].
The relationship between these 23 genes and compounds found in environmental tobacco smoke (ETS) can be seen in Table 2. ETS is smoke that originated from burning tobacco products and smoke exhaled by smokers [25]. ETS consist of 40 biologically and toxicologically active compounds according to Hoffmann's list [26]. Three compounds from Hoffmann List are produced in milligrams per cigarette (tar, nicotine, and CO), while the remaining are in nanograms or micrograms level per cigarette [26, 27]. In Table 2, it could be seen that 6 out of 23 genes that undergo changes in profile expression in human small airway epithelium on smoker subjects are associated with the compounds from ETS according to the Hoffmann List. In addition to explaining how smoking can affect the expression profile of these genes, this can also clarify the potential benefits of giving AST to smokers.
From the 23 mentioned genes, the protein networks were made using STRING (string-db.org) as could be seen in Figure 1. There are 4 clusters with the red node as the central cluster (C1). The central cluster consists of 7 genes/proteins: SOD1, IDH1, TKT, PRDX1, GPX3 SKAP2, and BECN1 with SOD1 as central nodes. If Nrf2 (NFE2L2) was administrated into the networks, it could be seen that these genes were in the central cluster (C1). In Table 3, the annotations of the 7 (seven) genes/proteins which contain 10 (ten) groups of gene annotations (gene ontology and GO biological process) can be seen based on the smallest adjusted p value [28]. In Table 3, the genes/proteins can be seen to be involved in chemical and oxidative stress, reactive oxygen species (ROS), and apoptotic signaling pathway. Smoking is known to induce oxidative stress, as well as activate inflammatory response pathways, which trigger a cascade of events in which ROS production is an early but indispensable step [29]. CS is also known to induce in vivo epithelial cell apoptosis, however, fibrotic changes occur only after a viral exacerbation [29, 30].
Gene function annotation (GO biological process) of other clusters (C2, C3, and C4) can also be seen in Table 3. C2 cluster is related to the regulation of extrinsic apoptotic signaling pathways and the regulation of immune response. C3 cluster is related to transcription process regulation, programmed cell death, signaling receptor activity, and deacetylation reaction. While the C4 cluster is related to aerobic and cellular respiration, as well as the electron transport process in mitochondria. Based on the relationship between the central cluster and other clusters, it can be seen how chemical oxidative stress caused by ROS due to smoking activity could affect the transcription process regulation, signaling process related to apoptosis and receptor activity, as well as electron transfer process and other cellular processes.
If the 23 genes that change expression due to smoking were associated with diseases caused by smoking, it was known that all of genes were involved/associated with chronic bronchitis, adenocarcinoma of the lung, non-small-cell lung carcinoma, carcinoma, small cell lung carcinoma, type 2 diabetes mellitus, emphysema, ischemic stroke, lung diseases, and inflammation (Table 4). While the ones related to pulmonary heart disease are known to be as many as 19 genes. The interesting thing is that the 23 genes are also related to the inflammation disease category. It appears that this evidence suggests that the association between ROS and oxidative stress induced by smoking and smoking-related disease may be mediated by the inflammatory process. On the other hand, the administration of AST, thus has the potential to reduce the risk of the development of these diseases in smokers.
## 4. Discussion
Smoking activity is a major factor in various diseases, including immune-mediated inflammation disease. The concept of chronic or prolonged ROS production is central to the development of inflammatory diseases [31]. On tobacco, ROS production is mainly contributed by nicotine, the main component in tobacco. A low concentration of nicotine (0.1 μM) could induce ROS to about $35\%$, however, a significant increase in the amount of ROS could be observed at 1 and 10 μM nicotine concentrations of $54\%$ and $80\%$, respectively [32]. Aside from nicotine, ROS development is also stimulated by various agents such as pollutants in ETS such as heavy metals (lead, nickel, mercury, arsenic, cadmium, chromium, and cobalt), or other organic compounds such as hydroquinone, acrylonitrile, acrolein, formaldehyde, acetaldehyde, benzene, dan benzo(a)pyrene. Reactive oxygen species (ROS) is a key signaling molecule that plays an important role in the development of inflammatory disorders. The increase in ROS generation by neutrophils polymorphonuclear (PMN) at the sites of inflammation can for example lead to endothelial dysfunction and tissue injury [31]. However, nicotine-induced neutrophil activation by nicotine is also known to be ROS-independent [33].
Some of the associations between genes that changed expression in smokers with inflammation, endothelial dysfunction, and tissue injury can be explained as follows: in Table 2, it is known that an increase in histone deacetylase2 (HDAC2) expression, which is also caused by nicotine, hydroquinone, and benzo(a)pyrene compound, happens in smokers. The increase in expression also happens on secreted phosphoprotein1 (SPP1), transketolase (TKT), cytochrome b-245 beta chain (CYBB), and peroxiredoxin 1 (PRDX1) in smoker subjects compared to nonsmokers (Table 1). In atherosclerosis, overexpression of HDAC2 in endothelial cells under proatherogenic conditions and oxidative injury suppresses the expression of Arginase2 (ARG2), which further reduces the expression of endothelial nitric oxide synthase (eNOS) [34]. Endothelial dysfunction is known to be caused by a decrease in eNOS expression. In chronic diabetic foot ulcer (DFU) an increase of HDAC2 expression also happens where dysfunctional endothelial progenitor cells (EPCs) plays a major role in inhibiting vascular complication in DFU patient [35]. Inhibition of HDAC2 is known to prevent inflammatory disorders and ROS production in EPCs with high glucose levels [35]. SPP1 is highly expressed after stimulation of oxidized low-density lipoprotein (oxLDL) and plays a role in causing inflammation of human coronary artery endothelial cells (HCAECs) [36]. High TKT expression is also associated with advanced tumor stage and TKT inhibitors promote apoptosis of lung adenocarcinoma cells and cell cycle blockade [37]. CYBB, also known as NADPH-oxidase (NOX2) is known to be involved in angiotensin II-induced hypertension and endothelial dysfunction, as well as abundantly expressed in the endothelium [38]. PRDX1 is also significantly higher in stroke patients compared to control. PRDX1 level is also higher on blood samples taken 3 and 6 hours after the stroke attack compared to the control [39].
The 23 DEGs generated from the analysis of gene expression data in the GEO browser (GSE 11952) were the genes expressed in the human small airway epithelium of smokers vs nonsmokers, where the Nrf2 protein is also significantly activated and localized in the nucleus of the same cell [17]. This can also indicate how these genes are related to Nrf2. Furthermore, if we look at the protein network in Figure 1 where Nrf2 (NFE2L2) was in the central cluster (C1), there is a strong indication that the 23 DEGs produced are related to Nrf2 (NFE2L2). In mammals, Nrf2 has long been known to function as an evolutionarily conserved intracellular defense mechanism against oxidative stress. Nrf2 has been shown to contribute to the regulation of the heme oxygenase1 (HO-1) axis, which is a strong anti-inflammatory target, and has shown a relationship with the expression of inflammatory mediators in the NF-kB pathway and macrophage metabolism through the Nrf2/antioxidant response element (ARE) system [40]. Lungs are highly vulnerable to oxidative stress-inducing factors such as infection, allergen, and pollutant such as ETS. Oxidative stress that triggers Nrd2 activation has been shown in several human respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), or pulmonary parenchyma-related diseases such as acute respiratory distress syndrome (ARDS) and lung fibrosis [41]. In this study, it has been shown (in Table 4) the association of the 23 DEGs with these diseases and other smoking-related diseases such as pulmonary heart disease, ischemic stroke, and type 2 diabetes mellitus (T2D).
This study shows that AST could also act as a very good candidate to improve diseases related to inflammation [42]. AST is also known to increase Nrf2 and HO-1 expression in the lung, and suppress emphysema due to cigarette smoke in rats [43]. From the various previous explanations, it can be concluded that AST treatment in smokers has the potential to reduce the formation of ROS and the occurrence of oxidative stress that triggers inflammation, as well as the accompanying diseases. The potential for AST can then be confirmed through the next stage of research (e.g., clinical trials) including through observation of changes in gene expression biomarkers of the 23 DEGs.
## 5. Conclusion
From this study, we found that the 23 DEGs (smokers vs nonsmokers) in the human small airway epithelium were found to be inversely regulated by genes that changed expression due to AST treatment. Based on the GO biological process, some of these genes are known to be related to oxidative stress and ROS. AST has been confirmed to be efficacious in relieving chronic and acute inflammation in a variety of diseases, including neurodegenerative disorders, diabetes, gastrointestinal diseases, kidney inflammation, and skin and eye diseases.
## Data Availability
The data used to support the findings of the study are available on the database mentioned in the manuscript, as it used secondary data from the Gene Omnibus Ontology (GEO) database and Comparative Toxicogenomics Database (CTD; https://ctdbase.org/).
## Conflicts of Interest
The authors declare that they have no conflicts of interest regarding the publication of this manuscript.
## Authors' Contributions
IPP developed the ideas presented in this paper and revised the final draft of the manuscript. AT is involved in processing, analyzing, and presenting data in tables and figures. DRN revised and edited the final draft of the manuscript. R was involved in the literature search and drafting of the manuscript.
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|
---
title: 'Awareness of Dysphagia-Related Complications and Risks and the Importance
of Early Intervention in Patients with Parkinson''s Disease: A Qualitative Study'
authors:
- Kaifeng Yao
- Lihua Wang
- Lihua Zhang
journal: International Journal of Clinical Practice
year: 2023
pmcid: PMC10005865
doi: 10.1155/2023/9514851
license: CC BY 4.0
---
# Awareness of Dysphagia-Related Complications and Risks and the Importance of Early Intervention in Patients with Parkinson's Disease: A Qualitative Study
## Abstract
### Objective
To investigate the awareness of dysphagia-related complications and risks and the importance of early intervention in patients with Parkinson's disease (PD).
### Methods
Using the phenomenological approach of the qualitative study, 18 patients with PD in a Grade A tertiary hospital in Nantong were selected, and semistructured personal in-depth interviews were conducted. The interview content was analyzed using Colaizzi's seven-step method, and the topics and subtopics were further refined.
### Results
Awareness of dysphagia-related complications and risks and the importance of early intervention in patients with PD can be summarized into three topics: lack of knowledge about PD and dysphagia, changes in emotional cognition, and low need for early intervention for dysphagia.
### Conclusions
Patients with PD have a low awareness of dysphagia, do not follow any preventative measures, and have difficulty in recognizing the disease symptoms; hence, there is a vital need for early intervention. Medical staff need to create awareness among patients and their families, provide health education through multiple channels, popularize the knowledge of PD complications such as dysphagia, improve patient compliance with respect to medication, regular consultation, and medical treatment, guide the transformation of negative emotions in patients to positive emotions, and help patients with PD to actively prevent dysphagia and other complications and improve their quality of life.
## 1. Introduction
Parkinson's disease (PD) is the second most prevalent neurodegenerative disease among the elderly after Alzheimer's disease, [1] affecting $3\%$ of the global population [2]. It is caused by a decrease in the neurotransmitter dopamine level and associated nigrostriatal neurodegeneration [3]. It has both motor symptoms, such as static tremor, myotonia, and slow movement and nonmotor symptoms, such as autonomic dysfunction and mental symptoms [4, 5].
One of the most common nonmotor symptoms of PD, oropharyngeal dysphagia is mostly caused by gastrointestinal dysfunction and autonomic nerve disorder. As its clinical significance has become increasingly apparent in recent years, dysphagia has steadily attracted the attention of researchers. The prevalence of dysphagia in PD ranges between $18.5\%$ and $100\%$, a variation that can be explained by the use of different methodologies for assessing swallowing function or the level of PD [6].
Aspiration pneumonia, a complication of oropharyngeal dysphagia (OPD), is the leading cause of mortality in PD. The symptoms of OPD can manifest in the early stages of PD, and 40–$78\%$ of patients exhibit changes in swallowing function [7]. In the early stages of PD, medical personnel frequently overlook symptoms that respond to dysphagia. Some studies have found that $50\%$ of patients with PD may have unperceived dysphagia in the early stages, and occult aspiration accounts for $15\%$ of such cases. As per the results of some studies, dysphagia in patients with PD may lead to a small amount of drug residue in the pharynx [8, 9]. The impact of the medication is then solely affected by residue or tablet adaptation, such as when the tablets are crushed. In the late stage of PD with severe dysphagia, patients need to be intubated [6]. According to a study, the average survival time after OPD onset is only two years [7, 10]. Therefore, dysphagia is one of the most serious complications in patients with PD.
In addition to causing various oropharyngeal symptoms in patients with PD, such as excessive saliva, patients suffering from PD could have excessive production of saliva or reduction in the ability to swallow (reduced number of swallows per minute) saliva in the mouth and to cough, aspiration pneumonia, and asphyxia. Dysphagia can also lead to the occurrence or even aggravation of various negative emotions, such as anxiety, depression, shame, loneliness, inferiority complex, and self-image disorder, thereby affecting patients' communication and social interaction. There are several studies in China that focus on these factors and related negative emotions [11–14]. In 2020, the fourth edition of the China Parkinson's Disease Treatment Guidelines highlighted in the chapter on rehabilitation exercise therapy that medication is less effective in treating nonmotor symptoms such as dysphagia; instead, rehabilitation and exercise therapy can be of assistance.
In 2021, a consensus group, with Italian neurologists as the core, formed a multinational consensus on the screening, diagnosis, and prognostic value of dysphagia in PD [15]. Researchers have conducted multiple studies on PD with dysphagia, but there are few qualitative studies on the cognitive aspects of the complications in patients with PD with dysphagia. Considering that dysphagia has a definitive impact on the prognosis of patients with PD, in this study, we conducted in-depth interviews with patients with PD who did not have obvious dysphagia symptoms to comprehend their awareness of dysphagia and related intervention, to provide a theoretical basis for developing early personalized intervention programs for patients with PD.
## 2.1. Participants
Using the purposeful sampling method, patients with PD in a Grade A tertiary hospital in Nantong were selected as the interview respondents.
The inclusion criteria were as follows: [1] diagnosis of primary PD was in line with the 2015 movement disorder society's clinical diagnostic criteria for Parkinson's disease, [2] patients were aged ≤80 years old, [3] patients were aware of their own disease diagnosis and condition, and [4] patients were informed of and agreed to be interviewed.
The exclusion criteria were as follows: [1] patients without PD; [2] patients with cognitive impairment and communication impairment; [3] patients with other serious diseases that may cause dysphagia, such as stroke and esophageal cancer; and [4] patients with a history of psychological disorders or mental illness.
The sample size was supported by the saturated interview data; that is, no new themes emerged from the interview content of the interviewees. A total of 18 patients, numbered N1–N18, were interviewed. This study, numbered 2020KT171, was approved by the hospital's ethics committee.
## 2.2.1. Data Collection Method
[1] Study Design. Using the phenomenological approach [16] of the qualitative study, we conducted one-on-one semistructured interviews to collect data. Based on the purpose of the study and referring to literature, after consultation with two authoritative PD therapists on neurology and preinterviews with two patients with PD, the outline of this study was drawn up with the following interview questions:As per your knowledge, what are the early symptoms of PD?Do you know how PD affects swallowing?As per your knowledge, what are the symptoms of deglutition disorders in PD?Is there any difference in your current eating and drinking habits compared to previously? If yes, then please specifyAt times, do you drool?Are these manifestations bothering you?Do you know what will happen if a deglutition disorder worsens?What do you do at home if you have drooling, or you are unable to swallow?Do you speak to your relatives or medical staff about your symptoms?Do you think the support and concern of your loved ones are important?What kind of help do you hope to get for the deglutition disorder caused by PD?Are you willing to receive related exercise therapy to prevent PD deglutition disorders, such as swallowing training, voice training, and singing therapy?
[2] Data Collection. The researchers conducted in-depth interviews with 18 patients with PD according to the interview outline. Before data collection, the researchers introduced themselves to the interviewees and explained the significance, purpose, and main content of this study; the interviewees then signed the informed consent form. The interview times ranged from 28 to 56 minutes. During the interviews, which were recorded, the researchers listened attentively, focused on the patients' facial expressions, movements, and emotional reactions, did not interrupt them, and did not ask induced questions. All interviews were completed by the researchers themselves.
[3] Data Analysis. Data collection and analysis were conducted concurrently. At the end of each interview, the recorded audio was transcribed within 24 hours, and the interview data were analyzed. The phenomenological Colaizzi's method [17] was used for a seven-step analysis. The data were transcribed, coded, and analyzed by two people, and then refined, and finally defined as a reasonable and logical topic and subtopic.
## 2.2.2. Quality Control
Researchers involved in this study were engaged in clinical nursing in the Department of Neurology for 18 years. Nursing more than 80 patients with PD every year, they have accumulated rich theoretical understanding and practical experience in the treatment and nursing of patients with PD. Prior to conducting the interviews, the researchers participated in several online training courses on scientific research methods, such as qualitative study, organized by the Chinese Nursing Association. A nursing expert with rich experience in qualitative research and who has experience supervising graduate students in qualitative research made five revisions to the first draft of the interview outline developed by the research team. The nursing expert then instructed the researchers on how to properly select research participants, how to choose qualitative research methods such as rooting theory, ethnography, narrative research, and phenomenology, and on informing the reasons for choosing the selected method. Then, based on the preexperimental interview recordings, the researchers were instructed on the proper use of interview techniques to obtain more realistic and comprehensive information about the patients.
First, the researchers selected different representative interviewees according to their age, occupation, gender, and educational background. At the beginning of the interview, they informed the interviewees about the purpose, significance, content of this study, and the principles of voluntariness and confidentiality. After obtaining the informed consent, they recorded each interview onsite and ensured the privacy of the patients was protected. During the interview process, they used communication skills to gain the trust of the patients and asked open-ended questions according to the outline. The interview site was the No. 21 consulting room next to the neurology expert clinic, the environment was peaceful, with sufficient illumination, and the interview was not likely to be disturbed. This ensured that the interviewees could express their true feelings and thoughts without reservation.
## 3.1. Basic Information
The basic information of the 18 interviewees such as gender, age, occupation, education level, payment of medical expenses, course of disease, and type of medication is listed in Table 1. The different ages, occupations, and course of disease could ensure that the respondents were representative to saturate the sample.
## 3.2. Topic Results (Table 2)
The topic results are shown in Table 2 as follows:
## 3.2.1. Topic 1: Lack of Knowledge about PD and Dysphagia
[1] Insufficient Awareness of Common Symptoms of PD. Common symptoms of PD include motor and nonmotor symptoms. Motor symptoms include static tremor, myotonia, bradykinesia, and postural balance disorders. Nonmotor symptoms include smell disorder, sleep disorder, autonomic dysfunction (constipation, dysphagia, and excessive salivation), and cognitive and mental disorders [18]. When the interviewees were asked what the symptoms of PD were, most of the patients only spoke of their own symptoms and knew nothing about others. Also, the answers of $\frac{16}{18}$ of the respondents focused on common symptoms, such as static tremor. The results of this study showed that patients with PD had insufficient awareness of disease symptoms, and after being informed and educated about the disease, they expressed surprise that PD could affect swallowing and lead to dysphagia. N1: “I only know that this disease will lead to tremors in the hand or foot. I have tremors in my right hand, while my left hand functions well. I also have constipation. I have had good bowel movements recently, and the condition is much better than that a few days ago. I have not heard that it affects swallowing, and I havenot experienced it yet. I have a good appetite.” N2: “There are few people who suffer from this disease. Sometimes, my left hand trembles when I am nervous, and I react slowly. I am a weighing clerk in the supermarket. Sometimes, I tremble badly when I am busy.” N4: “After I got this disease, my reactions have slowed down. When sitting in a chair, I feel like a tendon in my leg is being stretched, but I feel okay when I stand up and walk. I do not know what the other symptoms are. No one has told me about them. Every time I see a doctor, I receive advice on my medication.” N9: “I have tremors in my hands and feet, due to which it is difficult to walk. That is the only problem. I do not talk as fluently as before. I usually chant sutras to others. I find that tremors in my hands and feet are not related to me being nervous. During my re-examination, I was instructed to change the dosage of Madopar to half a capsule, but the effect was not obvious. I do not know about other symptoms.” N11: “I just feel uncomfortable in my hands and feet, I cannot sleep well at night, and I often have nightmares. I do not know about other symptoms.” N17: “I like to read books. I have also checked about the disease on the internet. I know that this disease involves many symptoms, such as tremors in the hands and feet, muscle stiffness, and difficulty in walking. It is said that it can be treated with surgery, isn't that so?” [2] No Awareness of Excessive Salivation, the Main Symptoms of Oropharyngeal Dysphagia. Patients with PD may have dysphagia in the early stage, and the clinical manifestations vary. In addition to the main complaint of OPD, it can also manifest as excessive salivation, food residue in the oral cavity after eating, a slow-down in oropharyngeal swallowing, stagnation of bolus, and aspiration [19]. However, $80\%$ of patients with excessive salivation symptoms in this study were not aware that excessive salivation was also a manifestation of OPD. There were several situations:The doctor did not inform the patient. N4: “I sometimes produce a lot of saliva, especially when I sleep. Is too much saliva also a symptom of OPD? I never mentioned it to the doctor. ”Symptoms were ignored. N16: “Sometimes, when I sleep, I produce a lot of saliva. My wife dislikes it that my pillow is wet with saliva. The situation is good during the day. I have no trouble eating. I do not choke, but I just eat slowly.” N8: “I do not choke when I eat or drink. I have no problem swallowing. ”Concerned about personal image. N12: “During the day, sometimes when I talk, I feel like I am going to drool, which makes me embarrassed to talk to others. I did not know that it is also a symptom of my disease. I was afraid of being laughed at, so I did not speak about it previously.” [3] Danger of Oropharyngeal Dysphagia Is Unclear. Dysphagia in patients with PD can lead to various complications, such as insufficient drug intake, malnutrition, dehydration, or secondary pneumonia, which is the leading cause of death in patients with PD [20]. The results of this study showed that most of the respondents were unaware of the consequences of OPD and even undervalued or underestimated them after being informed. N2: “I did not know, I am fine now anyway” N5: “I can cough. Sometimes, I feel discomfort in my throat when I swallow something. I have to lift my neck to make it easier to swallow. I am also worried that it will worsen. Is there any serious consequence?” N9: “Compared with previously, there are indeed some changes in swallowing. When I take medicine, even though I swallow water several times, the tablets remain in my mouth. It seems that swallowing has slowed down.” [4] Lack of Coping Measures for Symptoms of Dysphagia. The results of this study showed that most patients do not know how to suitably face and take effective preventive measures in the early stage of dysphagia, such as salivation or slow swallowing; also, the symptoms of most patients are accompanied by “on-off phenomenon” due to medication effect time, [21] due to which the patient does not know when they will occur; this is related to the medication and health education. The main reasons for not knowing the intervention measures are as follows:There was no relevant guidance at the time of consulting a doctor. N14: “*Drooling is* much better now. I drool a lot when I sleep. Sometimes my pillow is wet, but I cannot help it. The doctor just tells me to take medicine.” N3: “In such a case, I only take medicine. The outpatient doctor did not tell me how to deal with this. ”Access to information. N11: “I drool, sometimes it is light and sometimes it is severe, and my voice has become lower. I do not know how to prevent this situation from getting worse, but I only take medicine. I use the mobile phone designed for elderly people and I cannot read. ”Lack of awareness of reexamination and self-medication. N16: “I have been buying medicine from pharmacies all the time. Last time, I changed my Madopar dose from a quarter to a half tablet. ”Expressing of comprehension. N17: “The doctor has not mentioned this. The outpatient doctor is busy and has no time to talk to us about it, but I think we should pay attention to our own disease and actively learn relevant knowledge.”
## 3.2.2. Topic 2: Emotional Cognitive Changes
[1] Anxiety and Depression. About $35\%$ of patients with PD have depression, and $31\%$ of patients with PD have anxiety, among whom, patients with both depression and anxiety account for the majority [22, 23]. In the context of COVID-19, due to the difficulty in consulting with doctors, patients with PD were more stressed, and their anxiety and depression levels were worse [24]. Most of the interviewed patients were worried about the inconvenience of visiting a doctor. N8: “I feel that my condition is getting worse. I am not interested in anything. I used to play cards to pass the time, but now I cannot hold the cards as my hands are shaking. I do not go out alone because I do not walk very quickly and I am afraid that the villagers will laugh at me.” N18: “My son and daughter are very kind to me, but they must go to work. I used to do all the housework at home, but now I feel as though I am useless.” [2] Sense of Powerlessness. Powerlessness is a psychosocial phenomenon. As a nursing diagnosis, powerlessness was first incorporated into North America in 1982. It refers to a perception that a person's behavior will not have a significant impact on the results, and there is a lack of control over the current situation and what will happen. N3: “This disease cannot be healed anyway. It cannot be cured, no matter how much medicine I take, and there are also side effects. I think it is too uncomfortable to take amantadine, so I stopped taking it. If complications really happen, there is nothing I can do about it.”
## 3.2.3. Topic 3: Need for Early Intervention of Dysphagia
The proportion of patients with PD with occult dysphagia, [25] which needs early evaluation and prevention is high [26]. The results of this study showed that patients with PD have different attitudes towards the need for early intervention of dysphagia.
[1] Interested in Intervention. Through the interviews, it was found that $65\%$ of the patients had a positive desire for intervention and hoped to get help from medical staff to prevent dysphagia through guidance. Two patients showed keen interest in early intervention of PD complications. N8: “I am often unhappy at home. It is great that there is a way for me to prevent complications. If you start the singing therapy course, please inform me about it. I will wait for your update at home.” N9: “I must participate in this training. It is good for me.” [2] Not Interested in Intervention. In this study, three respondents maintained an indifferent attitude towards early preventive measures. N16: “I am willing to participate in this training for early prevention of complications, but I am busy recently and may not be free to participate in the training. Let us talk about it later.” [3] Concerns. In this study, five patients had various concerns about the early intervention of dysphagia. Considering self-image. N10: “Okay, I am willing to participate, and I will cooperate in the exercise, but I hope to be taught alone because I do not think it is good that people recognize me during the class.” N1: “I used to work in government offices. My symptoms are not remarkable unless I am nervous. I do not want to participate in training, which may let other people know that I have Parkinson's disease. It is not good for me. ”Impact of family support system. N11: “My grandson accompanies me to get my medicine prescription during the holidays. Usually, I am at home alone and no one cares about me. I cannot participate in these activities or sing.” N17: “My son asked for a leave of one hour to accompany me to see a doctor. Time is very tight. I have to leave after getting the medication. I do not think it is necessary nor do I have the time to do voice exercise.” N4: “Yes, I still have low blood pressure. My daughter always blames me for thinking too much. It is obviously not a big deal.”
## 4.1. Strengthening Health Education and Correcting Misconceptions
The results of this study showed that outpatients with PD had low or no knowledge of PD symptoms other than motor symptoms and that $\frac{16}{18}$ of the respondents did not know or pay attention to one of the most serious complications, swallowing disorder, and only $\frac{6}{18}$ of the respondents were willing and wanted early intervention to delay the onset of swallowing disorders, which is in line with the other findings of this study that patients with PD have low knowledge of the disease and its complications. There are few qualitative studies on the knowledge and intervention needs for PD complications among patients [27]; however, studies have pointed out the need for early interventions to delay the onset of dysphagia complications in the early stages of PD.
One of the common complications of PD, dysphagia can lead to various other complications in patients, such as pneumonia, dehydration, and malnutrition. It can even aggravate emotional problems, such as anxiety and depression, and can directly or indirectly affect the quality of life of patients, accelerate the progress of the disease, and have a negative impact on the prognosis [6, 15, 28]. In addition to the burden of the disease itself, dysphagia may also cause negative changes in patients' self-confidence, self-image, and understanding of roles and social functions and damage their social interactions and communication behavior [29]. The National Institute for Health and Care Excellence [30] proposed the diagnosis and management methods for adult patients with PD and required that patients with communication disorders, dysphagia, and excessive saliva should be given speech and language therapy to improve their communication and speech functions and reduce the risk of aspiration. The results of this study show that most patients lack understanding of dysphagia in the early stage of PD. They do not know that excessive salivation or anterior spillage or saliva accumulation is a manifestation of dysphagia and lack coping measures for dysphagia. Patients with PD are mostly outpatients, who cannot rely on medical treatment to get health education and other services due to heavy outpatient work. Hospitals have not set up outpatient nurses for nursing special diseases, so patients cannot get standardized and comprehensive disease-related health education and nursing guidance, and the needs of various specialized assessments cannot be met. The results of this study also revealed that due to misunderstanding the condition, some patients with PD had a low rate of outpatient follow-up, and the longest follow-up time was 10 months. The opportunity to remedy such incorrect understanding and medication behavior in time was missed, resulting in an aggravation of the symptoms.
Xue et al. [ 31] studied and confirmed that the establishment of specialized care clinics can reduce the rehospitalization rate of patients and improve overall satisfaction. Calabresi et al. [ 32] confirmed through experiments that although drug and physical therapy can positively affect the clinical manifestations of PD, the guiding nursing management led by PD nurses may be the key to a better quality of life and higher patient compliance. Therefore, it is recommended to set up an intrinsic outpatient facility for PD-specific diseases, where medical guidance is provided by different professionals, specialist nurses, or graduate students with rich clinical experience and nursing knowledge working together with doctors and nurses, to achieve the integration of treatment, assessment, and education during medical treatment. Additionally, social media platforms such as WeChat in China can be used to set up nursing groups for patients with PD in China, to provide health consultation and nursing guidance. It is necessary to change patients' understanding of PD and dysphagia, help them gain correct knowledge and confidence to overcome the disease, and improve their compliance with medication and regular reexamination. It is also important that patients actively participate in multiple rehabilitation activities to delay the occurrence and progress of serious complications such as dysphagia, reduce the hospitalization rate, and improve their quality of life.
## 4.2. Paying Attention to Psychological Counseling
In this study, most patients showed obvious emotional disturbances, such as anxiety and depression, and hoped to receive more attention from their families and medical staff.
First, the disease factors of PD itself lead to poor mental health conditions, such as anxiety and depression. Second, the side effects of PD medication tend to aggravate anxiety and depression. In addition, the complexity of the treatment process during the pandemic caused anxiety and depression. In the present study, some relatives of elderly patients mistakenly believed that the patients' anxiety and depression were under their control, and they attributed these to the patients' delusions and lack of family care and support for patients.
The disease course of PD is long, and it takes time for the drugs to take effect, and some medicines seem to have no obvious effect. Patients need more understanding and support from their families. Patients who were already suffering from a sense of powerlessness in this study were in a negative state of mind, which may easily aggravate their depression.
During hospital admission, it is necessary to educate family members about basic disease knowledge, encourage patients to receive psychological treatment, and provide the patients more companionship and patience. It is necessary to analyze the psychological state of the patients by integrating facial expressions, mood, and language during the interview, fully gain the trust of the patients through empathy, encourage them to adjust their emotional state through multiple channels, give play to their subjective initiative, and help them gain a sense of self-respect in the process of emotional self-management.
We also found that the two younger interviewees had mild symptoms, but they expressed obvious concern about their self-image, and even more patients expressed anxiety. However, they were aware of their poor mental health and began to pay attention to self-regulation, which was not related to their educational background. After the interview, many respondents expressed their affirmation of the interview experience and hoped that they would have more opportunities to discuss and talk about their emotional reactions to the disease and be guided in their coping methods in the future. Some studies have applied singing therapy [32–37] and dance therapy [34] to improve swallowing, movement, and other functions in PD patients, in addition to providing the patients a pleasant experience, psychological benefit, and improving their confidence and quality of life.
## 4.3. A Program for Early Prevention of OD Complications
Standardized screening of dysphagia and early intervention are the focus of PD treatment. As early as in the Huangdi Neijing, an ancient Chinese classic, the thought of “preventive treatment of disease” was put forward. The word “prevention” highlighted in the implementation of the Medium- and Long-term Plan for the Prevention and Treatment of Chronic Diseases in China and the Healthy China Initiative 2019–2030 is consistent with the theory of “preventive treatment of diseases” in traditional Chinese medicine. Therefore, it is critical to advocate early evaluation, early intervention, and treatment of the disease before onset.
Kurpershoek et al. [ 38] conducted in-depth interviews with 20 patients with PD to understand their needs and wishes for an early nursing intervention plan. The results revealed that most people realized that their neurologists mainly focused on drug treatment and had little time to solve their needs for more comprehensive methods of living with Parkinson's disease, indicating that they lacked supportive nursing guidance. The patients hoped to discuss the early intervention nursing plan with medical staff in the early stage of the disease, so they could better understand the uncertainties they would face in the future.
The results of the present study show that most patients have a positive need for early intervention for PD complicated with dysphagia. For some patients, this was because of their lack of understanding, followed by the impact of the family support system. In the interview process, one respondent was elderly but had strong disease prevention awareness. The respondent had participated in group singing activities to exercise the swallowing muscles, which was related to the high cultural level and correct disease awareness of the respondent. Kurpershoek et al. [ 38] proved that patients can improve their swallowing and their severity of excessive salivation through routine swallowing training combined with voice training; however, the importance of early intervention was not emphasized in the study. Due to the early occult dysphagia in patients with PD, the intervention time is too late when there is obvious dysphagia. In 2017, Stegemoller et al. [ 34] applied singing therapy to patients with PD without obvious dysphagia for the first time. The results revealed that group singing behavior could prolong the time of laryngeal elevation and improve the emotional symptoms of patients with PD, proving that it was a good early intervention strategy.
In December 2021, the State Council issued the 14th Five-Year Plan for the Development of the National Aging Career Development and Elderly Care Service System, [39] which put forward specific requirements to improve health education and the health literacy of the elderly and strengthened the early screening, intervention, and classification guidance of key chronic diseases in the elderly. At present, researchers in China have not paid sufficient attention to the early intervention of PD, but they can use the principle of singing therapy to develop personalized early intervention programs for patients with PD with common occult dysphagia in the context of the pandemic, based on the characteristics of Chinese people. While improving disease awareness among the respondents, the interviews conducted during this study helped prevent or slow down the occurrence of complications, such as dysphagia, improved anxiety, depression, and other harmful emotions of patients and improved their quality of life.
## 5. Conclusion
At present, there are few national and international qualitative studies on PD, especially regarding the awareness of disease complications among patients. In the present study, we used descriptive phenomenological methods to conduct in-depth interviews with patients with PD to understand their cognitive status of dysphagia complications and the need for early intervention. The results of the present study show that most patients lack correct awareness of PD symptoms and early dysphagia, but they show a high need for early intervention. When patients with PD suffer from severe dysphagia, they may have severe pneumonia and face repeated hospitalization and difficulty in recovery, which may lead to economic and mental pressure on the family and society. Hospitals need to set up outpatient nurses for PD as soon as possible to provide health guidance and assessment for patients and provide resources and ways for further early intervention measures.
A limitation of this study is that all the respondents were only patients with PD from among the outpatients, and in case of some of the patients, their medication was handled by their family members, but they were not interviewed. These limitations will be taken into account in future studies.
## Data Availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
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|
---
title: LncRNA X Inactive Specific Transcript Exerts a Protective Effect on High Glucose-Induced
Podocytes by Promoting the Podocyte Autophagy via miR-30d-5p/BECN-1 Axis
authors:
- Ying Cai
- Sheng Chen
- Xiaoli Jiang
- Qiyuan Wu
- Yong Xu
- Fang Wang
journal: International Journal of Endocrinology
year: 2023
pmcid: PMC10005869
doi: 10.1155/2023/3187846
license: CC BY 4.0
---
# LncRNA X Inactive Specific Transcript Exerts a Protective Effect on High Glucose-Induced Podocytes by Promoting the Podocyte Autophagy via miR-30d-5p/BECN-1 Axis
## Abstract
Inhibiting podocyte autophagy promotes the development of diabetic nephropathy (DN). This study aims to explore the upstream regulatory mechanism of the autophagy-related gene BECN1 in high glucose (HG)-induced podocytes. C57BL/6 mice were treated with 50 mg/kg streptozotocin to construct a DN model. Biochemical indexes, pathological morphology of renal tissue, the morphology of renal podocytes, and the expressions of autophagy-related proteins in DN mice and normal mice were detected. The upstream miRNAs of BECN1 and the upstream long noncoding RNAs (lncRNAs) of miR-30d-5p were predicted by bioinformatics analysis and verified by dual-luciferase reporter assay. Mouse podocyte clone 5 (MPC5) cells were exposed to HG to construct a DN cell model. The levels of miR-30d-5p, X inactive specific transcript (XIST), and BECN1 in mouse kidney and MPC5 cells were detected by quantitative real-time polymerase chain reaction (qRT-PCR). The regulation of XIST/miR-30d-5p on the viability, apoptosis as well as proteins related to apoptosis, epithelial-mesenchymal transition (EMT), and autophagy in MPC5 cells were determined by rescue experiments. The levels of glucose, urinary protein, serum creatinine, and blood urea nitrogen were upregulated, but the kidney tissues and podocytes were damaged in DN mice. XIST targeted miR-30d-5p to promote viability while suppressing the apoptosis of HG-induced MPC5 cells. In kidney tissues or HG-induced MPC5 cells, the expressions of Beclin-1, light chain 3 (LC3) II/I, XIST, B-celllymphoma-2 (Bcl-2), and E-cadherin were downregulated, while the expressions of P62, miR-30d-5p, Bcl-2-associated X protein (Bax), cleaved-caspase-3, vimentin, and alpha-smooth muscle actin (α-SMA) were upregulated, which were reversed by XIST overexpression. The reversal effect of XIST overexpression was offset by miR-30d-5p mimic. Collectively, XIST promotes the autophagy of podocytes by regulating the miR-30d-5p/BECN1 axis to protect podocytes from HG-induced injury.
## 1. Introduction
Diabetic nephropathy (DN) is the main cause of end-stage renal disease (ESRD) worldwide, and hyperglycemia is the main factor driving DN into ESRD [1]. The typical symptom of DN is proteinuria, and podocyte injury is closely related to proteinuria [2]. Podocytes are highly differentiated terminal glomerular epithelial cells, which play an important role in regulating glomerular function. Podocyte injury is the basic feature of glomerular diseases, including podocyte fusion, podocyte hypertrophy, podocyte number reduction, podocyte apoptosis, podocyte epithelial-mesenchymal transition (EMT), and so on [3]. Therefore, protecting podocytes from high glucose (HG)-induced injury may be a new strategy to treat DN.
Increasing research has shown that the deficiency of autophagy is the key cause of podocyte injury [4, 5]. Autophagy is the process of transporting damaged organelles and aging proteins in cells to lysosomes for degradation [6]. Therefore, podocytes need to maintain intracellular homeostasis through autophagy, and the lack of autophagy will lead to podocyte injury, proteinuria, and glomerulosclerosis [5]. Beclin-1 encoded by BECN1 is an indispensable protein in autophagy and is the key factor of autophagy initiation. The activation of autophagy mediated by Beclin-1 is an important mechanism to reduce podocyte injury induced by HG [7]. Therefore, it is our focus to promote the autophagy of podocytes by activating the expression of Beclin-1 in podocytes.
Epigenetic mechanism exerts an important effect on the regulation of malignant tumors, immune diseases, and metabolic diseases [8]. Noncoding RNA, an epigenetic mechanism, including long noncoding RNA (lncRNA) and microRNA (miRNA), has been proven to be involved in the development of DN and has been suggested as a diagnostic marker or therapeutic target for DN [9, 10]. miRNA can inhibit the translation of mRNA or promote its degradation by binding to downstream mRNA [11]. Through an online database, in this research, the possible target miRNAs of the BECN1 gene were predicted, among which miR-30d-5p has been proven to suppress the autophagy of renal cell carcinoma cells [12]. Nevertheless, whether miR-30d-5p is also involved in the regulation of podocyte autophagy is yet to be elucidated, which is thus discussed in this research. Given that lncRNAs often participate in the disease progression by sponging miRNA to inhibit the function of miRNA, the upstream lncRNAs of miR-30d-5p were predicted in this research, of which lncRNA XIST was reported to participate in the protection of podocytes against HG-induced cell injury by regulating the miR-30/AVEN axis [13]. Therefore, we speculated that XIST may engage in the modulation of podocyte autophagy via the miR-30d-5p/BECN1 axis, thereby exerting a protective effect on HG-induced podocytes.
## 2.1. Animals and Drug Administration
C57BL/6 mice ($$n = 10$$, 18–22 g) were supplied by Shanghai Slake Animal Laboratory Co. Ltd. The room temperature was maintained at 22 ± 2°C and humidity was set at 50–$60\%$ with a normal period (12 hours (h) light/12 h dark). The research was approved by the Ethics Committee of Zhejiang Baiyue Biotech Co., Ltd. for Experimental Animals Welfare (approval number: ZJBYLA-IACUC-20210818).
The mice were randomly divided into two groups, namely, the DN group ($$n = 5$$) and the control group ($$n = 5$$). Subsequently, the DN model was constructed as previously described [14]. Briefly, mice in the DN group were treated with 50 mg/kg streptozotocin (STZ, S0130, Merck, Germany) in citrate buffer (C2488, Merck, Germany) for 5 consecutive days, and those merely treated with citrate buffer were applied as the control group.
## 2.2. Biochemical Parameters and 24 h Urine Protein Determination
Blood samples were collected from the tail of mice, and glucose (GLU) was tested every week by a blood glucose meter (Accu-Chek, Roche, Switzerland) to ensure the successful construction of the diabetes model. Four weeks after the injection of STZ, the 24-h urine of mice was collected, and the urine protein was determined by the urine protein test kit (C035-2-1, Nanjing Jiancheng, China). The establishment of the DN model was considered to be successful, as obvious albuminuria was detected in mice. Blood from the tail vein of mice was collected and centrifuged at 7000 r/min for 10 minutes at 4°C to obtain plasma samples. The levels of serum creatinine (Scr) and blood urea nitrogen (BUN) in plasma were detected by an automatic biochemical analyzer (AU680, Beckman, USA).
## 2.3. Histopathological Examination
The mice were sacrificed by cervical dislocation and their renal tissues were collected and fixed in $4\%$ paraformaldehyde. The fixed kidney tissues were embedded in paraffin and made into 4 μm sections. After being dewaxed with xylene and rehydrated with gradient ethanol, the sections were subjected to staining with the hematoxylin and eosin (H&E) kit (G1003, Servicebio, China) and Periodic Acid-Schiff (PAS) staining kit (G1008, Servicebio, China), respectively. Thereafter, the sections were dehydrated and transparentized. Finally, the sections were sealed with glycerol gelatin aqueous (S2150, Solarbio, China), and the results were observed under a microscope (AE2000, Motic, China).
## 2.4. Ultrastructural Changes under Transmission Electron Microscope
The renal tissues were washed in phosphate-buffered saline (PBS) and then fixed in a fixing solution (G1102, Servicebio, China). After dehydration by acetone, the tissues were embedded in resin and sectioned with an ultrathin microtome (Leica EM UC7, Germany). The sections were stained with uranyl acetate and lead citrate, and the morphological changes of renal podocytes were observed by the transmission electron microscope (HITACHI HT7700, Japan).
## 2.5. Immunohistochemistry
The sections were routinely dewaxed and hydrated and then were subjected to antigen repair using antigen repair solution (G1202, Servicebio, China). After being treated with $3\%$ H2O2, the sections were blocked with bovine serum albumin (BSA; abs9157, Absin, China) and then probed with the primary antibody against light chain 3B (LC3B; ab63817, Abcam, USA) and horseradish peroxidase (HRP)-labeled secondary antibody goat anti-rabbit IgG (S0001, Affinity, USA). Thereafter, LC3 expression was visualized by 3,3′-diaminobenzidine (DAB) solution (G1212, Servicebio, China), and the nuclear staining was conducted by a hematoxylin staining solution. Finally, the results were observed under the microscope.
## 2.6. Western Blot
Kidney tissues or mouse podocyte clone 5 (MPC5) cells were subjected to the extraction of total proteins using the protein extraction kit (W034, Nanjing Jiancheng, China). Next, the protein concentration was quantified with the BCA protein assay kit (P1511, Applygen, China). Subsequently, the proteins were separated by SDS-PAGE gel (W003, Nanjing Jiancheng, China), blocked with BSA, and successively incubated using primary antibodies and secondary antibodies. The details of the antibodies are shown in Table 1. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as an internal reference. Eventually, the protein bands were visualized using an electrochemical luminescence reagent (W028, Nanjing Jiancheng, China) with a Tanon 5200 Imaging System (Shanghai, China).
## 2.7. Bioinformatics Assay
The miRDB (https://mirdb.org/index.html) and TargetScan (https://www.targetscan.org/vert_72/) were used to search the miRNAs targeting BECN1. The binding sites between miR-30d-5p and BECN1/XIST were predicted by TargetScan and Starbase (https://starbase.sysu.edu.cn/).
## 2.8. Cell Culture
Mouse podocytes MPC5 (BNCC342021, BeNa Culture Collection, China) were cultivated in DMEM-H complete medium (BNCC338068, BeNa Culture Collection, China) at 37°C with $5\%$ CO2. For glucose exposure, MPC5 cells were treated with normal glucose (NG, 5.5 mmol/L) or HG (30 mmol/L) for 48 h as previously described [15].
## 2.9. Transfection
MiR-30d-5p mimic (M, miR10000515-1-5) and mimic control (MC, miR1N0000002-1-5) were obtained from Ribobio (China). *The* gene sequence of XIST was obtained from the NCBI database and then amplified by polymerase chain reaction (PCR). The amplified gene sequence was inserted into pCDNA3.1–3 × FLAG-N (ZL-3.1FLAGN, Ke Lei Biological Technology, China) to construct the XIST overexpression (OE) plasmid (hereafter called OE-XIST). MPC5 cells were inoculated into a six-well plate and cultured to be approximately $80\%$ confluent prior to transfection. Thereafter, OE-XIST/empty vector or miR-30d-5p mimic/mimic control was transfected into cells by Lipofectamine 2000 Transfection Reagent (11668-019, Invitrogen, USA) that had been diluted in Opti-MEM™ medium (31985070, Thermo Fisher, USA). After being incubated for 24 h, the transfected cells were analyzed by quantitative real-time PCR (qRT-PCR).
## 2.10. Target Gene Verification
The target gene verification was conducted by dual-luciferase reporter assay. MiR-30d-5p mimic/mimic control and the pmirGLO vector (E1330, Promega, USA) containing the wild-type (WT) or mutant (MUT) 3′UTR region of BECN1 or XIST were cotransfected into 293T cells (BNCC353535, BeNa Culture Collection, China) using Lipofectamine 2000 Transfection Reagent. Post 48 h transfection, the activities of firefly luciferase and Renilla luciferase were measured using the Dual-Luciferase® Reporter Assay System (E1910, Promega, USA).
## 2.11. qRT-PCR
Total RNA was extracted from MPC5 cells using Trizol reagent (15596026, Invitrogen, USA) and miRNeasy mini kit (217004, Qiagen, German). The cDNA synthesis was performed using the first strand cDNA synthesis kit (K1612 or B532453, Sangon, China). The qPCR was conducted on a real-time PCR system (CFX Connect, Bio-rad, USA) using SYBR Green Abstract PCR Mix (B110031) and MicroRNAs qPCR Kit (B532461) obtained from Sangon (China). The relative values were calculated by the 2−ΔΔCT method [16] and normalized to GAPDH or U6. The sequences of the primers are listed in Table 2.
## 2.12. Cell Counting Kit-8 (CCK-8) Assay
MPC5 cells in the logarithmic phase were harvested and prepared into 3 × 104/mL cell suspension, followed by being added into a 96-well plate (2 × 103 cells/well). After the cells were incubated with 10 μL CCK-8 solution (C0005, TopScience, China) for 4 h, the absorbance at 450 nm was determined with a microplate reader (CMaxPlus, MD, China).
## 2.13. Cell Apoptosis Assay
Annexin V-FITC/PI Apoptosis Detection Kit (556547, BD, USA) was utilized to evaluate the cell apoptosis. MPC5 cells were digested and prepared into 1 × 106/mL cell suspension in binding buffer, and the treated cell suspension was incubated with 5 μL of Annexin V-FITC and 10 μL of PI for 20 minutes in the dark. In the end, the cell apoptosis was detected by an Accuri C6 flow cytometer (BD Biosciences, USA).
## 2.14. Statistical Analysis
The measurement data were presented as mean ± standard deviation. The results between the two groups were analyzed by an independent sample t test. One-way analysis of variance (ANOVA) was adopted for the comparison among multiple groups. The experiments were repeated three times. All statistical analyses were implemented with GraphPad 8.0 software, and P value less than 0.05 was considered to be statistically significant.
## 3.1. Autophagy Inhibition was Observed in the Kidney Tissue of DN Mice
We detected the levels of GLU, urinary protein, Scr, and BUN in two groups of mice, and found that the levels of these indexes in DN mice were higher than those in control mice (Figures 1(a)–1(d), $P \leq 0.01$). Histological staining results showed that compared with those of the control mice, the glomerular structure of DN mice was disordered and the basement membrane was thickened (Figures 1(e) and 1(f)). Through transmission electron microscope, we observed the disorganized podoid processes, exfoliated podocytes, and thickened basement membrane in DN mice (Figure 1(g)). In addition, the expression of LC3 in the kidney tissue of DN mice was decreased (Figure 1(h)). Meanwhile, the results of Western blot showed that the levels of Beclin1 and LC3 II/LCE I were lower yet the P62 level was higher in the DN group than those in the control group (Figures 1(i)–1(l), $P \leq 0.001$).
## 3.2. MiR-30d-5p Targeted BECN1 and XIST
We predicted the possible target miRNAs of BECN1 through the online website and then obtained five candidate miRNAs by the intersection of the results (Figure 2(a)). Based on the literature retrieval results, miR-30d-5p was finally selected as the target miRNA of BECN1 and XIST for subsequent studies. According to the predicted sequences in Figures 2(b) and 2(d), we construct the recombinant plasmid and transfect it with miR-30d-5p mimic into 293T cells. The results indicated that the luciferase activity of 293T cells transfected with miR-30d-5p mimic and BECN1-WT plasmid or XIST-WT was reduced (Figures 2(c) and 2(e), $P \leq 0.001$).
## 3.3. XIST Targeted miR-30d-5p to Regulate the Viability and Apoptosis of MPC5 Cells
In addition, the expression of XIST was lower, and the expression of miR-30d-5p was higher in HG-induced MPC5 cells (Figures 3(a) and 3(b), $P \leq 0.001$). We constructed XIST overexpression plasmid and verified its transfection efficiency (Figure 3(c), $P \leq 0.001$), and found that OE-XIST can reduce the level of miR-30d-5p while increasing the level of BECN1 (Figures 3(d) and 3(e), $P \leq 0.001$). At the same time, we also tested the transfection efficiency of miR-30d-5p mimic in MPC5 cells and found that miR-30d-5p mimic increased the miR-30d-5p expression while reducing the expression of BECN1 (Figures 3(f) and 3(g), $P \leq 0.001$). Subsequently, we observed changes in the biological behaviors of the MPC5 cells. Under HG induction, the viability of MPC5 cells was decreased, while the apoptosis was increased (Figures 3(h)–3(j), $P \leq 0.001$). Additionally, OE-XIST promoted the viability and suppressed the apoptosis of HG-induced MPC5 cells, whereas miR-30d-5p mimic did the opposite (Figures 3(h)–3(j), $P \leq 0.001$). The aforementioned effects of OE-XIST or miR-30d-5p mimic were reversed by cotransfection of OE-XIST and miR-30d-5p mimic (Figures 3(h)–3(j), $P \leq 0.01$).
## 3.4. XIST Targeted miR-30d-5p to Regulate the Expressions of Apoptosis-, EMT- and Autophagy-Related Proteins
We evaluated the regulation of XIST and miR-30d-5p on apoptosis-, EMT-, and autophagy-related proteins. HG-induced upregulation of Bcl-2-associated X protein (Bax), cleaved caspase-3, vimentin, alpha-smooth muscle actin (α-SMA), and P62 in MPC5 cells, as well as downregulation of B-celllymphoma-2 (Bcl-2), E-cadherin, Beclin-1, and LC3 II/I in MPC5 cells (Figures 4(a)–4(h) and 5(a)–5(d), $P \leq 0.001$). However, these effects induced by HG were negated by OE-XIST and enhanced by miR-30d-5p mimic (Figures 4(a)–4(h) and 5(a)–5(d), $P \leq 0.05$). Nevertheless, the aforementioned effects of OE-XIST or miR-30d-5p mimic were countervailed by cotransfection of OE-XIST and miR-30d-5p mimic (Figures 4(a)–4(h) and 5(a)–5(d), $P \leq 0.05$). In addition, HG treatment reduced the number of autophagosomes in MPC5 cells, which were neutralized by OE-XIST (Figure 5(e)). Moreover, miR-30d-5p mimic reduced the number of autophagosomes in HG-induced MPC5 cells and offset the effects of OE-XIST (Figure 5(e)).
## 4. Discussion
Inducing autophagy to protect podocytes from HG-induced injury has become a promising strategy in the treatment of DN [17]. Under normal blood glucose levels, autophagy is an important protective mechanism in renal epithelial cells, including podocytes, proximal tubular, mesangial, and endothelial cells; under hyperglycemic conditions, repression of the autophagic mechanism can contribute to the development and progression of diabetic kidney disease [18, 19]. In this study, we clarified that the mechanism of the XIST/miR-30d-5p/BECN1 axis in protecting podocytes from HG-induced injury is related to the promotion of podocyte autophagy.
The role of XIST in DN is various. Wang reported that XIST facilitates the development of DN by affecting the biological behaviors of human mesangial cells and regulating inflammation [20]. Yang et al. pointed out that XIST facilitates renal interstitial fibrosis in DN by upregulating the expressions of fibrosis-associated markers in HK-2 cells exposed to high glucose [21]. Meanwhile, Long et al. found that XIST expression is downregulated in HG-induced podocytes, and XIST overexpression promotes viability while inhibiting the apoptosis of podocytes [13]. Consistent with those of Long et al., our results uncovered that XIST overexpression has a protective effect on podocytes induced by HG. However, this result seems to be contrary to the conclusion reported by Wang [20] and Yang et al. [ 21] that XIST promotes the development of DN. This contradictory phenomenon may be due to the different cells studied or the different functions of XIST as lncRNA [22].
The relationship between XIST and autophagy has been reported in a variety of cells, such as hepatic stellate cells [23] and ovarian cancer cells [24]. Overexpression of XIST increases the level of LC3II/LC3I yet decreases the level of p62 to induce autophagy in nucleus pulposus cells [25]. XIST knockdown weakens the proliferation and autophagy in retinoblastoma cells [26]. In this study, we also found that XIST overexpression increased the LC3II/LC3I and Beclin1 levels but decreased p62 protein level to activate the autophagy of HG-induced podocytes. In addition, the targeting relationship between XIST and miR-30d-5p has been verified in rat Schwann cells [27], and it has also been proved that XIST overexpression can reduce diabetic peripheral neuropathy by repressing miR-30d-5p expression to induce autophagy [27]. In addition, the inhibitory effect of miR-30d-5p on autophagy has been demonstrated in renal cell carcinoma and hypoxic-ischemic rats [12, 28]. Analogous to previous studies, we found that upregulation of miR-30d-5p reversed the regulation of XIST overexpression on HG-induced podocyte autophagy, indicating that the protective effect of XIST on podocyte injury was achieved by targeting miR-30d-5p.
Classical autophagy is divided into five stages, including the initiation process, phagocyte nucleation, phagocyte expansion, autophagosome-lysosome fusion, and lysosomal substrate degradation [29]. Beclin1 is involved in the formation of autophagic precursors, and LC3-I can bind to P62 after being cleaved into LC3-II, mediating the aggregation of ubiquitinated proteins, followed by P62 degradation with the ubiquitinated proteins [29]. Therefore, increasing the protein expression of Beclin 1 is beneficial to the promotion of autophagy. In this study, we found that BECN1 was the target gene of miR-30d-5p. Fengyan Zhao et al. found that miR-30d-5p antagonist could upregulate the Beclin1 level, promote autophagy, and then restore the neurological function of hypoxic-ischemic rats [28]. A study on DN showed that HG-induced downregulation of Beclin-1 and LC3 II/I as well as upregulation of P62 in MPC5 [30]. In our research, the same phenomenon was observed, but the effect of HG was nullified by XIST overexpression and potentiated by miR-30d-5p upregulation, indicating that XIST boosted the expression of BECN1 by inhibiting miR-30d-5p, thus promoting autophagy.
Podocyte apoptosis is one of the main causes of the decrease in the number of podocytes, and there is a crosstalk between autophagy and apoptosis of podocytes [31]. It is reported that autophagy can protect the podocytes from apoptosis [32]. Silencing SPAG5-AS1 promotes autophagy yet inhibits the apoptosis of podocytes via SPAG5/AKT/mTOR pathway to alleviate podocyte injury [33]. Jin et al. revealed that exosomes derived from adipose-derived stem cells alleviate diabetic nephropathy by promoting autophagy flux and attenuating apoptosis in podocytes [34]. Liu et al. proved that XIST targets miR-30d-5p to facilitate autophagy and thus inhibit the apoptosis of Schwann cells [27]. In our study, XIST overexpression impeded podocyte apoptosis, while miR-30d-5p reversed the effect of XIST overexpression, suggesting that XIST promoted the autophagy of podocytes by downregulating miR-30d-5p to hamper the podocyte apoptosis.
HG can induce podocyte EMT in the process of DN [35]. In this process, the great loss of E-cadherin (the landmark molecule of epithelial cells), together with abundant expression levels of vimentin and α-SMA (the landmark molecules of interstitial cells), can be observed in podocytes, which leads to the increased fusion of podocytes and the decreased adhesion to the glomerular basement membrane, finally resulting in decreased podocytes [36]. However, EMT is a reversible process, so DN can be alleviated by inhibiting the EMT of podocytes [37]. In addition, Shi et al. found that the EMT of podocytes exposed to HG can be reduced by restoring autophagy activity [38]. Consistent with previous research results, we discovered that XIST promoted the autophagy of podocytes by modulating the miR-30d-5p/BECN-1 axis, and then inhibited EMT of podocytes.
In summary, our data indicate that XIST promotes the autophagy of podocytes by regulating the miR-30d-5p/BECN1 axis, thereby protecting podocytes from HG-induced injury. This study provides a new target for the treatment of DN.
## Data Availability
The analyzed data sets generated during the study are available from the corresponding author upon reasonable request.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
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|
---
title: 'Can Following Paleolithic and Mediterranean Diets Reduce the Risk of Stress,
Anxiety, and Depression: A Cross-Sectional Study on Iranian Women'
authors:
- Behzad Zamani
- Mobina Zeinalabedini
- Ensieh Nasli Esfahani
- Leila Azadbakht
journal: Journal of Nutrition and Metabolism
year: 2023
pmcid: PMC10005875
doi: 10.1155/2023/2226104
license: CC BY 4.0
---
# Can Following Paleolithic and Mediterranean Diets Reduce the Risk of Stress, Anxiety, and Depression: A Cross-Sectional Study on Iranian Women
## Abstract
### Background
Psychiatric disorders have been a challenge for public health and will bring economic problems to individuals and healthcare systems in the future. One of the important factors that could affect these disorders is diet.
### Objective
In the current study with a cross-sectional design, we investigated the association of Paleolithic and Mediterranean diets with psychological disorders in a sample of adult women.
### Methods
Participants were 435 adult women between 20 and 50 years old that refer to healthcare centers in the south of Tehran, Iran. The diet scores were created by the response to a valid and reliable semiquantitative food frequency questionnaire (FFQ), and the psychological profile was determined by response to the Depression, Anxiety, and Stress Scale (DASS-21). The multivariable-adjusted logistic regression was applied to compute the odds ratio (OR) and $95\%$ confidence interval (CI).
### Results
After adjusted for potential confounders, it is evident that participants in the highest Paleolithic diet tertile had lower odds of depression (OR = 0.21; $95\%$ CI: 0.12, 0.37: $P \leq 0.001$), anxiety (OR = 0.27; $95\%$ CI: 0.16, 0.45: $P \leq 0.001$), and stress (OR = 0.19; $95\%$ CI: 0.11, 0.32; $P \leq 0.001$) in comparison to the lowest tertile. Furthermore, those in the third tertile of the Mediterranean diet score were at lower risk of depression (OR = 0.20; $95\%$ CI: 0.11, 0.36; $P \leq 0.001$), anxiety (OR = 0.22; $95\%$ CI: 0.13, 0.38; $P \leq 0.001$), and stress (OR = 0.23; $95\%$ CI: 0.13, 0.39; $P \leq 0.001$) compared with those in the first tertile.
### Conclusion
The result of the current study suggests that greater adherence to Paleolithic and Mediterranean dietary patterns may be related with a decreased risk of psychological disorders such as depression, anxiety, and stress.
## 1. Introduction
Psychological disorders were considered one of the leading causes of disability with severe consequences [1]. Some common psychological disorders are depression, anxiety, and stress [2]. Over 300 million individuals, or $4.4\%$ of the world's population, are depressed [3]. Globally, $7.3\%$ of people experience anxiety, and $17.6\%$ experience psychological discomfort [4, 5]. Women have twice as many mental problems as males [6]. Psychological disorders make people more susceptible to economic, social, and health problems [5, 7]. Psychological disorders are linked to genes and lifestyle factors, including inactivity and smoking [8].
One of the lifestyle factors that is associated with psychological disorders is nutrition [9]. The association between diet and psychological disorders has been highlighted in several studies [10]. High consumption of fruits, vegetables, nuts, legumes, and whole grains was related to a lower risk of psychological disorders [11]. Contrarily, diets heavy in red meat, processed meat, high-fat dairy items, refined grain, and high-sugar beverages are linked to increased psychological disorders [12]. Mediterranean diet (MD) as a healthy dietary pattern has a positive impact on CVD, diabetes, and neurological disorders [13].
There is no consensus on the relationship between the MD and mental health. In cross-sectional research conducted in Spain [14], adherence to the Mediterranean diet was related to a decreased incidence of depression and anxiety. In contrast, such a substantial connection was not detected [15] in a study of 1183 Australian adults. However, a significant component of this Australian sample's diet consisted of items not typically eaten in a Mediterranean-style diet. Research on female adolescents in Iran revealed a correlation between MDP adherence and decreased depressive symptoms [16]. However, a comprehensive review and meta-analysis found no significant association between the MD and depression risk in cohort studies [17]. Although a cross-sectional study in Iran identified evidence demonstrating a negative link between the MD and the risk of psychiatric disorder in both sex groups [18], no study specifically focuses on the female population. Moreover, the Paleolithic diet, which is high in fruits, vegetables, nuts, and roots and low in fried foods, grains, dairy products, salt, refined fats, and sugar, has positively affected the glycemic index and CVD risk markers [19, 20]. However, no study has so far examined the potential association of the PD with psychological disorders.
To the best of our knowledge, no study is available linking both dietary pattern PD and MD on psychiatric disorders in Iranian women. It should also be noted that the prevalence of psychological disorders in this population differs from other communities. In addition, previous research in Iran revealed that the MD adhered to nine components of mental disorders; however, the current study assessed ten components.
Given these points and the controversial findings of the MD on mental disorders, we conducted the present study to examine the association of the PD and MD with psychological disorders in a sample of Iranian women.
## 2.1. Study Design and Participants
The present study's cross-sectional design was conducted on a women population. We selected participants from clients referred to 10 healthcare centers affiliated with the Tehran University of Medical Sciences (TUMS). These centers were chosen at random from a total of 29 local healthcare centers. We determined the number of our subjects at each chosen health center concerning the overall number of people using the facility. Using the following formula, α = 0.05, standard deviation (σ) = 5.2, and estimation error (d) = 0.5 [21], sample size was calculated as follows: n ≥ [Z1−α/2 × σ/d]2. According to this formula, 416 people were needed; however, given access to data and to consider any probable exclusion, 435 participants were studied. Several healthcare centers affiliated with the TUMS were chosen randomly with a multistage cluster sampling method. Before the research, participants provided informed permission in writing. Those who did not provide informed consent and did not participate in completing the questionnaires, and those who consumed less than 500 or more than 3,500 kilocalories of energy per day, were eliminated from the research. Participants had to be healthy women between 20 and 50 years old and of Iranian descent to be included. Women who were pregnant, lactating, premenopausal, with chronic diseases such as diabetes, cardiovascular diseases, liver or kidney dysfunction, cancer, or diagnosed with a psychological were not included. Individuals taking medications affecting the mental status were also excluded. Informed consent was received from all participants. The research council of the School of Nutritional Sciences and Dietetics of the TUMS confirmed the study protocol (number: 9511468004).
## 2.2. Assessment of Psychological Profile
The psychological profile was evaluated by the short form of the Depression, Anxiety, and Stress Scale (DASS-21) [22]. Since the DASS-21 is based on a dimensional concept rather than a categorical concept of mental disorders, we cannot compare variables using the groups “depressed,” “anxious,” and “stressed,” which would be appropriate for a clinical diagnostic tool.
The Beck Depression Inventory scale and the Depression subscale had a + 0.70 correlation, the Zung Anxiety Inventory and the Anxiety subscale had a + 0.67 correlation, and the Perceived Stress Inventory and the Stress subscale had a + 0.49 correlation. In addition, its reliability and validity in the Persian version have been approved previously [23].
To fill up the questionnaire, one must indicate the current state of a symptom during the previous week. Each of the three subscales of the DASS consists of seven questions. The final score was derived from the sum of the scores on three subscales. The responses are categorized as zero, low, medium, and high, with scores ranging from 0 to 3. Since the DASS-21 is the abbreviated version of the original scale (42 questions), the final score for each of these subscales should be multiplied by two [24]. For depression, anxiety, and stress, the individuals are classified into five categories based on their overall score: normal, mild, moderate, severe, and extremely severe. To categorize participants into two groups, the median score was used as the cutoff point, and they were divided into groups higher and lower than the median.
## 2.3. Assessment of Dietary Intake
A valid and reliable 168-items semiquantitative food frequency questionnaire (FFQ) was used to assess usual dietary intake [25]. An expert nutritionist collected nutritional data via a face-to-face interview. Household measures were used to convert portion sizes to gram intake. Then, macro- and micro-nutrient intakes were computed using Nutritionist IV software (First Databank Division, the Hearst Corporation, San Bruno, CA, USA, modified for Iranian foods).
## 2.4. Paleolithic Diet and Mediterranean Diet Scores Measurement
The work by Whalen et al. [ 26] was used to compute the PD and MD compliance scores. The food items gathered from each participant's FFQ were categorized into 14 food categories (such as vegetables, fruits, fruit and vegetable diversity score, lean meat, fish, nuts, and calcium as more PD characteristics and red and processed meat, dairy foods, sugar-sweetened beverages, baked goods, grains and starches, sodium, and alcohol as less characteristic PD). The range of PD scores was from 13 to 65, with higher values indicating more adherence to the PD.
MD scores have 11 components. As shown in Table 1, this approach was adjusted regarding dairy items, cereals, starches, and alcohol consumption for the MD score. The components of the score were transformed into quintiles of consumption, and a score of 1–5 was applied to each component. The score of each component was then summed to create the final diet pattern score. The final scores could range from 10–50 for the ten components of the MD score.
Because all research participants were Muslims and did not use alcohol, alcohol consumption was not considered a factor in these results.
## 2.5. Assessment of Other Variables
Baseline information was obtained and documented, including age, marital status, socioeconomic status (home and welfare status), education status, and use of supplements or medications using a sociodemographic questionnaire.
## 2.6. Socioeconomic Status Demographic
The socioeconomic status demographic questionnaire was used for this purpose, which included questions on marital status, education, occupation, family size, means of support, and mode of transportation. The codes were appended to each questionnaire item to generate the socioeconomic status score. The score was split into three groups, and individuals were classified according to their socioeconomic status: low, middle-class, or high [27].
## 2.7. Anthropometric Indices
The subject's body weight was assessed by a digital scale (SECA, Hamburg, Germany) which is closest to 0.1 kg while wearing light clothing and without footwear. A wall-mounted stadiometer measured standing height to the nearest 0.5 cm. The following formula was used to calculate the body mass index (BMI): BMI = weight/height2 (kg/m2).
## 2.8. Physical Activity
The 24-hour recall method was used to estimate the level of physical activity and reported in metabolic equivalents × hours per day (Met.h/d). Activity levels were categorized into four classes (light, moderate, vigorous, and intense). The level of physical activity of subjects was presented as Met.h/d [28].
## 2.9. Statistical Analysis
*The* general characteristics of the population were compared in tertiles of the PD and MD using χ2 tests and one-way ANOVA of variance for categorical and continuous variables, respectively. The dietary intakes of participants were computed using the ANCOVA analysis and by adjusting for the energy intake among tertiles of the PD and MD.
Psychological profile variables were analyzed as both continuous and categorical variables. The mean scores of depressions, anxiety, and stress were compared in tertiles of the PD and MD using the ANCOVA test. Furthermore, we used the binary logistic regression to provide odds ratios (OR) and $95\%$ confidence intervals ($95\%$ CI), in crude and adjusted models, for the association of the PD and MD with psychological profile variables, in which the median was considered as a cut-point to categorize variables into two groups. According to previous publications, factors such as energy intake, age, physical activity, socioeconomic status, marital, and dietary supplement use were considered confounding variables. SPSS version 24 (SPSS Inc, Chicago, IL, USA) was applied to perform statistical analysis. $P \leq 0.05$ was considered statistically significant. The PD and MD variables are both independent variables.
## 3. Results
Participants' baseline characteristics by diet score tertiles are demonstrated in Table 2. Individuals' mean age and the BMI were 31.37 years and 23.78 kg/m2, respectively. There was no significant difference between the tertiles of the PD and MD and most of the variables. Only a significant association was observed between the PD and participants' socioeconomic status.
The energy-adjusted intake of selected nutrients and food groups across tertiles of the PD and MD are shown in Tables 3 and 4. Compared with those in the first tertile, individuals in the third tertile of the PD had a higher intake of energy, protein, carbohydrate, fiber, vitamin B6, folic acid, calcium, magnesium, zinc, fruits, vegetables, nuts, and fish and lower intakes of fat, sodium, grain and starch, red and processed meat, and sugar-sweetened beverages. Furthermore, higher adherence to the MD score was associated with higher intakes of energy, fiber, vitamin B6, folic acid, magnesium, fruits, vegetables, nuts, and fish and lower intakes of sodium, grain, starch, and red and processed meat.
Multivariable-adjusted means and standard errors (SE) of depression, anxiety, and stress scores for each tertile of both dietary pattern scores are provided in Table 5. In the crude model and after adjusting for potential confounders such as age, BMI, energy intake, physical activity, socioeconomic status, marital status, educational status, and supplement use, women in the highest tertile of the PD had lower depression, anxiety, psychological stress score than those in the lowest tertile. This association also was observed between psychological profile variables and MD.
In the crude model, participants in the top tertile of the PD had a lower risk of depression (OR = 0.26; $95\%$ CI: 0.16, 0.43: P ≤ 0.001), anxiety (OR = 0.32; $95\%$ CI: 0.20, 0.51: P ≤ 0.001), and stress (OR = 0.23; $95\%$ CI: 0.14, 0.38: P ≤ 0.001). After controlling for potential confounders, this significant association was even strengthened (depression: OR = 0.21; $95\%$ CI: 0.12, 0.37: P ≤ 0.001; anxiety: OR = 0.27; $95\%$ CI: 0.16, 0.45: P ≤ 0.001; and stress: OR = 0.19; $95\%$ CI: 0.11, 0.32; P ≤ 0.001) (Table 6). In crude and fully adjusted models, women with the highest tertile of the MD score had a lower odds of depression (Crude model: OR = 0.25; $95\%$ CI: 0.15, 0.42; P ≤ 0.001 and model 2: OR = 0.20; $95\%$ CI: 0.11, 0.36; P ≤ 0.001), anxiety (Crude model: OR = 0.28; $95\%$ CI: 0.17, 0.45; P ≤ 0.001 and model 2: OR = 0.22; $95\%$ CI: 0.13, 0.38; P ≤ 0.001), and stress (Crude model: OR = 0.29; $95\%$ CI: 0.18, 0.48; P ≤ 0.001 and model 2: OR = 0.23; $95\%$ CI: 0.13, 0.39; P ≤ 0.001) compared with those in the lowest tertile (Table 6).
## 4. Discussion
Our finding supposed that women following the PD and MD dietary patterns had a lower risk of psychological disorders such as depression, anxiety, and stress. To the best of our knowledge, the current study is the first investigation that evaluates the association of the Paleolithic diet with psychological profiles among a sample of an Iranian women.
Several putative mechanisms may explain the protective effects of Paleolithic and Mediterranean diet patterns on psychological profiles. One of the factors that have a relationship to psychological disorders is inflammation and oxidative stress [29, 30]. Some food groups high in Paleolithic and Mediterranean diets, such as vegetables, fruits, and nuts, are rich sources of antioxidants [31]. Thus, they could improve inflammation and oxidative balance.
Moreover, these food groups are high in fiber and micronutrients such as magnesium, folic acid, and vitamin C. The intake of fiber, particularly from vegetables and fruits, has an inverse association with symptoms of depression [32]. Dietary fibers such as pectin, gums, and fructans cannot be digested by human enzymes and play a role as a prebiotic. They may be fermented by microbial flora and produce short-chain fatty acids (SCFAs) [33]. SCFAs are able to improve intestinal epithelial barrier integrity, consequently decreasing permeability to lipopolysaccharides that are an endotoxin, release from pathogen bacteria, and increase inflammation [34]. Moreover, prebiotic fibers can help a healthier microbiome in the gastrointestinal tract [35]. The intestinal microbiome has an important effect on the gut-brain axis that plays a crucial role in mental health and brain function [36].
Magnesium is an essential element that not only plays an important role in numerous reactions in the neuron system as a coenzyme but also has an antidepressant effect through N-methyl-D-aspartate (NMDA) receptor block [37]. Folic acid is also associated with brain function via the effect on the metabolism of biogenic amines such as serotonin [38]. Vitamin C could improve mental function through several mechanisms, such synthesizing neurotransmitters, neuronal maturation, and anti-inflammatory activities [39].
The Paleolithic and Mediterranean diet patterns emphasize lower consumption of saturated fatty acids and higher consumption of monounsaturated fatty acids and polyunsaturated fatty acids, especially n-3 PUFAs. The central nervous system has the most percentage of lipids in the body after adipose tissue [40]. The dietary fatty acid composition can affect brain function [41]. The intake of saturated fatty acids (SFAs) could lead to neuroinflammation and impair brain activity [42, 43]. On the other hand, a higher intake of monounsaturated fatty acids (MUFAs) may promote insulin signaling in the brain and preserve the integrity of the brain's dopamine system, consequently diminishing the risk of depression [43, 44]. Moreover, n-3 PUFAs could affect the function of the brain by regulating brain-derived neurotropic factor BDNF, synthesis of the neurotransmitter, and synaptic plasticity [45].
Our findings revealed that adhering to the Paleolithic and Mediterranean diets is related to a lower risk of depression, anxiety, and stress. There are few studies that assess the relationship of the Paleolithic diet to psychological variables, and only a study by Norwood and collaborators evaluated the effect of the Paleolithic diet on psychological characteristics in 42 people ($94\%$ females) [46]. The Paleolithic diet was considered a restrictive dietary pattern in the mentioned study. They concluded that individuals following a Paleolithic diet had better psychological well-being compared to people who did not follow a particular diet. As mentioned, oxidative stress and inflammation may be essential in psychological disorders. A cross-sectional study of 646 men and women aged 30 to 74 years old showed that following Paleolithic and Mediterranean diet patterns is inversely associated with lower oxidative stress and inflammation [47]. However, several prior studies examined the association of the Mediterranean diet with psychological variables, but there are limited investigations that have been conducted in the Middle East area, particularly in women. A cross-sectional study by Sadeghi et al. showed an inverse association between adherence to the Mediterranean diet with depression, anxiety, and stress [48]. Another study evaluated the association of the Mediterranean diet with psychological disorders in female adolescents [16]. Like our study, the score of psychological disorders, including depression, anxiety, and stress, were determined by DASS-21 questionnaire. Their results showed that a higher score of the Mediterranean diet was associated with a reduced risk of depression but no significant association with anxiety and stress. Furthermore, a recent updated meta-analysis of observational studies by Shafiei et al. found that there is no significant association between the following Mediterranean diet and risk of depression based on the analysis of cohort studies; however, a significant inverse association was observed between adherence to the Mediterranean diet and depression risk in cross-sectional studies [27].
Our study has several strengths, including adjusting for several potential confounders, interviewing participants by expert nutritionists, and using a valid and reliable FFQ for collecting nutritional data. However, it is necessary to consider some limitations. It is impossible to characterize a causality relationship given the study's cross-sectional design. The study population was adult women 20–50 years old, and it may not be correct to generalize our findings to other populations with different conditions. In addition, for the assessment of the psychological profile, the DASS-21 questionnaire was used. The DASS-21 questionnaire is suitable for screening depression, anxiety, and stress and could not be valid for clinical diagnosis.
Our finding supposes that following Paleolithic or Mediterranean dietary patterns could be inversely associated with psychological disorders such as depression, anxiety, and stress in a sample of adult women. Further studies with large-scale and prospective cohorts or intervention designs are needed to deepen our understanding of the effect of Paleolithic or Mediterranean dietary patterns on mental health.
## Data Availability
Data are available from the corresponding author upon reasonable request.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
BZ and LA designed the research, BZ analyzed the data, MZ and BZ wrote the manuscript, and LA had the primary responsibility for the final content. ENS provided feedback on the manuscript and revising it critically for important intellectual content. All authors read and approved the final manuscript./
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|
---
title: Radiosensitivity in Non-Small-Cell Lung Cancer by MMP10 through the DNA Damage
Repair Pathway
authors:
- Yawei Bi
- Kun Cao
- Yuan Wang
- Wei Yang
- Na Ma
- Xiao Lei
- Yuanyuan Chen
journal: Journal of Oncology
year: 2023
pmcid: PMC10005878
doi: 10.1155/2023/5636852
license: CC BY 4.0
---
# Radiosensitivity in Non-Small-Cell Lung Cancer by MMP10 through the DNA Damage Repair Pathway
## Abstract
NSCLC (non-small-cell lung cancer) is an aggressive form of lung cancer and accompanies high morbidity and mortality. This study investigated the function and associated mechanism of MMP10 during radiotherapy of NSCLC. MMP10 expression in patients and their overall survival rate were assessed through GEPIA. Protein expression was tested by western blotting. Radioresistance was detected in vitro by apoptosis and clonogenic assay. The extent of DNA damage and repair was revealed by the comet test and γH2AX foci test. High MMP10 levels in specimens of lung adenocarcinoma were related to poor patient outcomes. Clonogenic and apoptosis assays revealed that MMP10 knockdown in A549 cells initiated radiosensitization. Furthermore, MMP10 siRNA increased damage to the DNA in NSCLC cells, while MMP10 was observed to participate in DNA damage repair post-ionizing radiation. Thus, after irradiation, MMP10 plays an essential role in NSCLC through the repair pathway of DNA damage; regulating MMP10 for NSCLC radiosensitivity might have potential treatment implications in radiotherapy of NSCLC.
## 1. Introduction
NSCLC (non-small-cell lung cancer) is an aggressive lung cancer type that accompanies increased death rates and morbidity, including squamous cell carcinoma, large cell carcinoma, and adenocarcinoma [1]. The three methods commonly used for lung cancer treatment involve radiation, surgery, and chemotherapy [2]. However, over recent years, the development of “precision radiotherapy” is defined as stereotactic body radiation therapy, has indicated its precise, low, and noninvasive side effects, which furnishes more possibilities of treatment for lung cancer by radiotherapy [3]. While several methods for comprehensive radiotherapy-based treatment are involved in NSCLC, it tolerates ionizing radiation with progressive radiotherapy, indicating that most such patients had essentially serious effects [4–6].
MMP10 (matrix metalloproteinase-10) is an essential member of the MMP (matrix metalloproteinase) family [7]. It is a mesenchymal lysing enzyme that can break down the core collagen IV, V, IX, X-proteins, fibronectin, laminin, elastin, gelatin, and proteoglycan [8]. Because MMP10 has roles in several pathological and physiological processes, it is essential for tissue damage repair, embryonic development, and other processes [9, 10]. MMPs function in extracellular matrix (ECM) degradation and breakdown of the basement membrane tissues to facilitate tumor invasion, growth, and metastasis; besides, mediation of the ECM basement membrane is a significant stage for the transfer of tumors [11, 12]. While several studies on MMP-2 and MMP-9 have been reported, there are only a few reports on MMP10 and tumor associations [13, 14]. Recently, MMP10 was shown to play a significant role in pro-MMPs activation [15]; it is expressed at high levels in epithelial tumors like bladder transitional cell cancer, gastric cancer, esophageal cancer, NSCLC, and skin cancer [16–19]. These findings indicate a close relationship between MMP10 and the development and occurrence of tumors.
In this research, we examined the function of MMP10 in NSCLC and observed that MMP10 conferred resistance to radiotherapy in NSCLC via the repair pathway for the damaged DNA. The regulatory function of MMP10 on NSCLC radiosensitivity might have therapeutic possibilities in the radiotherapy of NSCLC.
## 2.1. Public Bioinformatics Analysis
*Differentially* genes were obtained using the limma R package from TCGA-LUAD, TCGA-LUSC, and normal lung tissues (GTEx). Here, MMP10 (NM_002425.3) expression between tumorous tissues of LUAD, LUSC, and normal surrounding tissues was analyzed using the “Expression DIY” module of GEPIA [20]. Survival analysis was performed according to the MMP10 expression status and Kaplan–Meier curves were plotted; comparison of MMP10 mutations was done according to the survival status (LIVING/DECEASED) in 514 TCGA-LUAD patients using cBioportal (https://cbioportal.org). The Spearman method was used for the expression correlation between DDR-related genes and MMP10.
## 2.2. Cell Culture and Treatment
A549, the human LUAD (lung adenocarcinoma) cell line, was procured from ATCC (USA). They were cultured in DMEM containing fetal bovine serum ($10\%$) at 37°C in an incubator with a CO2 ($5\%$) chamber with appropriate humidity. A549 cells were radiated at dose of 8 Gy (clonogenic assay with 0 Gy, 2 Gy, 4 Gy, and 8 Gy). For apoptosis assay, the cells were detected by flow cytometry 24 h after radiation. Cellular state and density were observed during culture and fluid was changed on alternate days.
## 2.3. Irradiation
For cell radiation treatment, we used 60Co γ-rays (Radiation Center, Faculty of Naval Medicine of the Second Military Medical University, Shanghai, China). A specific dose was given to the cells at a rate of 1 Gy/min. All irradiations were performed at room temperature.
## 2.4. siRNA and Cellular Transfections
MMP10 siRNA was obtained from Thermo Fisher (Cat.#AM16708). MMP10 siRNA was transfected along with lipofectamine 3,000 from Invitrogen as per the provided instructions. Cells transfected with the empty vector were used as negative control (NC), along with untransformed cells (parental). A549 cells were cultured for at least 24 h after transfection and then exposed to radiation. Cells that were successfully transfected were used for assays at specified time points.
## 2.5. Clonogenic Assay
A549 cell survival was examined by clonogenic assay. The cells were trypsinized, counted, and seeded in 60-mm culture dishes in two sets of three for each dose of radiation; the number of cells seeded was according to the dose of radiation (0 Gy-200 cells, 2 Gy-400 cells, 4 Gy-800 cells, and 8 Gy-1600 cells), followed by irradiation with 0, 2, 4, and 8 Gy after 24 h. After ten days, cells were fixed using paraformaldehyde and methylene blue ($1\%$) stain. Thirty minutes later, dishes were washed using phosphate-buffered saline (PBS) and dried naturally. Then, the clone formation was counted.
## 2.6. Apoptosis Assay
To stain the irradiated A549 cells, Annexin V-fluorescein isothiocyanate (AV) and propidium iodide (PI) in the kit for apoptosis detection from Invitrogen (California, USA) were utilized. The cells were plated in six-well plates at a density of 105 cells per well and allowed to attach for 24 h. 24 h after 8 Gy radiation, the cells were harvested by trypsin digestion, washed with precooled phosphate-buffered saline (PBS) twice, and resuspended. Then, the cells were stained with AV and PI at room temperature for 15 min in a dark room. Flow cytometry (Beckman CytoFLEX) was conducted for analyses as per the instructions of the manufacturer.
## 2.7. Neutral Comet Assay
The extent of damage to DNA of A549 cells was examined by the neutral comet assay using a kit from Trevigen Inc. (Gaithersburg, MD) that was used at 4 h and 8 h post-irradiation as per the protocol provided by the manufacturer. First, slides were immersed in a $1\%$ NMA and dry thoroughly. Next, the single cell suspension prepared (2 × 104 cells/ml) was immersed in LMA under a 40°C water bath. Third, cell suspension was mixed and rapidly pipetted onto the surface of the precoated slide. The slides were then incubated at 4°C for 25 min at 25 V in TBE. Then, the gel was stained with PI (10 μg/ml) for 20 min and then rinsed gently with ddH2O. Finally, all slices were examined by an Olympus BX60 fluorescence microscope. Total 100 images in each slide were analyzed using CASP 1.2.3b2 software (CASPlab, Poland).
## 2.8. Western Blotting
Post-irradiation, at 0 h, 0.5 h, and 8 h, preparation of total cell lysates was performed using the ProtectJETTM Mammalian Cell Lysis Reagent from Fermentas (Lithuania) as per the protocol provided by the manufacturer. The membranes with the transferred protein were incubated with gentle agitation with following specific primary antibodies (1: 1,000) at 4°C overnight: p-ATM (1: 1000), p-DNA-PKcs (1: 1000), Rad51 (1: 1000), MMP10 (1: 1000), and actin (1: 1000) (all primary antibodies were from Abcam, USA). The secondary antibody (1: 5000) were also from Abcam. Electrochemiluminescence (Santa Cruz Biotechnology Inc) was used to detect all the membranes.
## 2.9. Immunofluorescence Staining
For this, γH2AX foci, a marker for DNA double-strand breaks, was detected via immunofluorescence assay. Post-2 Gy radiation, transfection of A549 cells with siRNA against MMP10 was conducted. At specified times, cells were fixed with chilled methanol/acetone (1: 1); then, BSA ($3\%$; in PBS) was used for blocking at room temperature for 60 min. Then, cells allowed to bind to a γH2AX primary antibody (1: 300; Abcam, US) were reacted with the secondary antibody (1: 1000). Then, confocal and conventional microscopy was used to monitor immunofluorescence; each group recorded the number of γH2AX foci in 30 cells and took the average.
## 2.10. Statistical Analysis
Data were acquired after conducting a minimum of three experiments conducted independently and were presented as the mean ± standard deviation. The statistical significance limit was considered to be $P \leq 0.05.$
Per treatment, for all the experimental groups, mean and standard error (SEM) was calculated. For all pairwise comparison procedures, Student's t-test was used, including calculating P values.
## 3.1. High Levels of MMP10 in Lung Adenocarcinoma Correlates with Poor Patient Outcomes
We analysed the expression of MMP10 in both LUAD and LUSC specimens by R-language according to TCGA and GTEx databases. The expression of MMP10 in the two types of NSCLC was higher than that in normal lung tissue, but only the expression of MMP10 in LUSC was statistically significant (Figure 1(a)). The MMP10 level in 486 primary LUAD specimens (Figure 1(b)) was remarkably higher than 338 healthy tissues. We then explored the relationship between MMP10 levels and LUAD/LUSC patient lifespan through R-language. Overall survival and disease-free survival were significantly lower for patients with MMP10high LUAD (Figures 1(c) and 1(d)) relative to those with MMP10low tumors ($P \leq 0.05$). Then, we analyzed the clinical data combined with MMP10 gene mutation data in TCGA-LUAD via cBioPortal (https://cbioportal.org) online tools. The results show that a total of 11 mutation sites (including 9 Missense, 1 Truncating, and 1 Splice) were found between 0 and 476 amino acids of MMP10 and 9 mutations in the domain. These mutations were all concentrated in the previous LIVING group, and none of the 186 cases in the DECEASED group had mutations, which illuminated the prognosis of LUAD patients with MMP10 mutation that shows better survival level (Figure 1(e)).
## 3.2. Impact of MMP10 siRNA on A549 Cell Survival and Apoptosis Post-Irradiation
To reveal the effects of MMP10 in radio treatment, we first used MMP10 siRNA for inhibiting the expression of MMP10 in A549 cells (Figure 2(a)). Then, using these cellular models for clonogenic assay, MMP10 knockdown rendered these cells significantly sensitive to IR (Figure 2(b)). Furthermore, we explored the effect of siMMP10 on the apoptosis of A549 cell after irradiation using flow cytometry. As we could see in Figures 2(c) and 2(d), although there were little differences in early apoptotic rate (fourth quadrant) and late apoptotic rate (first quadrant) between group parental and group NC (negative control) due to little difference in detection time, the total number of apoptosis (first and fourth quadrant) detected in the siMMP10 group was significantly less than that in groups parental and NC, which meant MMP10 knockdown significantly promotes A549 cell apoptosis post-IR.
## 3.3. Increase in NSCLC Cell DNA Damage Due to MMP10 siRNA Post-IR
Then, comet assay was conducted to reveal the activity of MMP10 in NSCLC post-IR and examine the extent of DNA damage using MMP10 siRNA. Indeed, MMP10 expression knockdown enhanced the damage to DNA post-IR (Figures 3(a)–3(c)), suggesting a possible, important function of MMP10 in the repair pathway for DNA damage post-IR.
## 3.4. Involvement of MMP10 in the Pathway for DNA Damage Repair
For confirming our inference, we examined the pathway for DNA damage repair by using western blot and immunofluorescence post-IR and treatment with MMP10 siRNA. The γH2AX foci assay revealed a much higher foci number at 8 h after IR in the siMMP10 + IR group than in the IR group (Figures 4(a)–4(d)), suggesting significant impairment of DNA repair as a result of MMP10 knockdown in response to IR. DNA damage repair in body after radiation is mainly carried out through NHEJ (nonhomologous end-joining) and HR (homologous recombination) pathways. Among them, HR is a completely correct repair pathway, because it requires homologous sister chromatids as templates, so the repair process only occurs during the S and G2 phases of DNA replication. In contrast, the NHEJ pathway plays roles throughout the cell cycle because it can directly rejoin broken DNA without the need for homologous sequences, and thus is often the primary repair modality for DSBs [21]. Related studies have shown that the DNA damage repair mechanism in tumor cells is extremely active, and a series of DNA damage repair-related proteins (ATM, DNA-PKcs, and Rad51) are involved in the regulation of tumor radiation resistance [22]. In order to further explore the relationship between MMP10 and DNA damage repair pathway, we tested the correlation between core genes of DDR pathway (HR and NHEJ) and expression of MMP10 in LUAD, which we found partial core genes of the DDR pathway were positively correlated with MMP10 expression, especially the genes in the HR pathway (Figure 5(a)). Further examination showed the inhibition of phosphorylation of proteins involved in DNA damage repair post-MMP10 siRNA treatment (Figure 5(b)), indicating the involvement of MMP10 in the pathway for DNA damage repair post-IR.
## 4. Discussion
This study revealed the involvement of MMP10 in NSCLC radiosensitivity through the pathway for DNA damage repair. First, R-language was used to reveal a significantly high expression of MMP10 in LUAD samples as per TCGA and GTEx samples, relative to that in normal tissue. Next, analysis of the correlation between the expression of MMP10 with LUAD patient lifespan revealed significantly lower rates of overall survival for patients with MMP10high LUAD than those having tumors with MMP10low ($P \leq 0.05$). We also found that MMP10 gene mutation data in TCGA-LUAD showed a better survival level which meant MMP10 might play a role in promoting tumor progression indirectly. IR can induce double-stranded DNA breaks and the subsequent apoptosis of corresponding cells [23]. Based on the bioinformatics analysis results, next, we used siMMP10 on A549 cells which rendered radioresistance for a better cell survival rate and lower apoptosis rate compared with negative control. Besides, we found MMP10 was closely associated with DNA damage through neutral comet assay. This brought us great interest so that we did series of experiments to detect the relationship between MMP10 and DNA damage repair after IR. Then, we illuminated that the knockdown of MMP10 increased the damage to DNA post-IR through the inhibition of the pathway for DNA damage repair, which we deem the primary reason for resistance to radiotherapy in NSCLC.
Radio treatment is an important approach to the treatment of tumors in lung cancer, although the outcome is not so satisfactory [24]. Studies on radiosensitization involve the following aspects: tolerance of tumor cells to hypoxia, repair of damaged DNA, apoptosis, angiogenesis, and disorders of the cell cycle [25–27]. While research on radiosensitization has progressed, it is still in the preliminary stage. With the resistance to radiotherapy of lung cancer cells, several uncertainties still exist in the treatment [28, 29].
MMP10, as an essential component of the MMP family, is active in various pathological and physiological processes and is essential for the repair of the damaged tissue, development of the embryo, and other processes [30]. Recently, MMP10 was found to be essential for pro-MMP activation [31]; a high expression of MMP10 was observed in tumors epithelial cells, such as transitional cell cancer of the bladder, gastric cancer, skin cancer esophageal cancer, and NSCLC [17, 32–34]. In this study, we observed that MMP10 knockdown significantly inhibited A549 cell survival and facilitated apoptosis post-IR. Furthermore, MMP10 knockdown could enhance the extent of DNA damage post-IR. Studies have shown that abnormally active DNA damage repair ability is the core mechanism of tumor cell to resist IR, which is also the main reason why tumor cells have a better survival rate and lower apoptosis rate [35]. DNA strand breaks are severe damages caused by IR, which can be divided into DNA single-strand breaks, DNA double-strand breaks, DNA base damage, and DNA crosslinks. Among them, DNA double-strand breaks (DSBs) are the most important form of damage caused by IR, and it is also recognized as the most serious form of damage [36]. In response to DSBs, cells establish complex signaling networks for the activation of DNA damage checkpoints. Once the cell detects damage, a host of DNA repair factors localize to the site of chromatin damage and initiate the DNA repair machinery by recruiting other repair proteins. In eukaryotic cells, DSBs are mainly repaired by the NHEJ and HR pathways [37]. Therefore, we made an assessment of the repair pathway for DNA damage by western blotting and immunofluorescence suggested a crucial function of MMP10 in NSCLC post-radiation through the pathway for DNA damage repair and the regulatory role of MMP10 on NSCLC radiosensitivity may confer therapeutic indications to radiotherapy. In addition, as both the NHEJ and HR pathways proteins (DNA-PKcs, ATM, and Rad51) phosphorylation were reduced in our result; MMP10 might affect the upstream proteins of the NHEJ and HR pathways, which meant that MMP10 might be involved in the core regulation of DNA damage repair; this is our next research direction.
Although radiotherapy is currently the mainstay of NSCLC treatment, tumor radioresistance has greatly limited the efficacy of radiotherapy. As DNA strand breaks are the main reason for cell death caused by IR, screening and discovering the key molecules involved in DNA radiation damage repair and elucidating their mechanism are the core basic issues in the field of radiotherapy. But so far, there are very few genes that could be clinically targeted for radiosensitization. Our research results suggest that MMP10 may play an important role in tumor radioresistance. In the next step, we will continue to study how MMP10 regulates DNA damage repair pathway and carry out clinical transformation.
To conclude, this is the first report to show that knockdown MMP10 can significantly radiosensitize NSCLC. We also find that MMP10 regulates tumor radiosensitivity through the DNA damage repair pathway. These novel findings would possibly aid in discovering new mechanism to enhance radiosensitivity to NSCLC.
## Data Availability
The datasets are available under reasonable request.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
Xiao Lei and Yuanyuan Chen designed the study; Kun Cao did the most experiments. Wei Yang and Na Ma did some experiments and analyzed the data. Yuan Wang did the most bioinformatics analysis part. Yawei Bi participated in the writing of the paper and revision of the manuscript. All authors read and approved the final manuscript. Yawei Bi, Kun Cao, and Yuan Wang contributed equally to this work.
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|
---
title: Food readjustment plus exercise training improves cardiovascular autonomic
control and baroreflex sensitivity in high‐fat diet‐fed ovariectomized mice
authors:
- Bruno Nascimento‐Carvalho
- Adriano Dos‐Santos
- Nicolas Da Costa‐Santos
- Sabrina L. Carvalho
- Oscar A. de Moraes
- Camila P. Santos
- Katia De Angelis
- Erico C. Caperuto
- Maria‐Claudia Irigoyen
- Katia B. Scapini
- Iris C. Sanches
journal: Physiological Reports
year: 2023
pmcid: PMC10005889
doi: 10.14814/phy2.15609
license: CC BY 4.0
---
# Food readjustment plus exercise training improves cardiovascular autonomic control and baroreflex sensitivity in high‐fat diet‐fed ovariectomized mice
## Abstract
Despite consensus on the benefits of food readjustment and/or moderate‐intensity continuous exercise in the treatment of cardiometabolic risk factors, there is little evidence of the association between these two cardiovascular risk management strategies after menopause. Thus, the objective of this study was to evaluate the effects of food readjustment and/or exercise training on metabolic, hemodynamic, autonomic, and inflammatory parameters in a model of loss of ovarian function with diet‐induced obesity. Forty C57BL/6J ovariectomized mice were divided into the following groups: high‐fat diet‐fed ‐ $60\%$ lipids throughout the protocol (HF), food readjustment ‐ $60\%$ lipids for 5 weeks, readjusted to $10\%$ for the next 5 weeks (FR), high‐fat diet‐fed undergoing moderate‐intensity exercise training (HFT), and food readjustment associated with moderate‐intensity exercise training (FRT). Blood glucose evaluations and oral glucose tolerance tests were performed. Blood pressure was assessed by direct intra‐arterial measurement. Baroreflex sensitivity was tested using heart rate phenylephrine and sodium nitroprusside induced blood pressure changes. Cardiovascular autonomic modulation was evaluated in time and frequency domains. Inflammatory profile was evaluated by IL‐6, IL‐10 cytokines, and TNF‐alpha measurements. Only the exercise training associated with food readjustment strategy induced improved functional capacity, body composition, metabolic parameters, inflammatory profile, and resting bradycardia, while positively changing cardiovascular autonomic modulation and increasing baroreflex sensitivity. Our findings demonstrate that the association of these strategies seems to be effective in the management of cardiometabolic risk in a model of loss of ovarian function with diet‐induced obesity.
The objective of this study was to evaluate the effects of food readjustment and/or exercise training on metabolic, hemodynamic, autonomic, and inflammatory parameters in a model of loss of ovarian function with diet‐induced obesity. The findings of the present study lend support to the hypothesis that exercise training and food readjustment promote specific benefits in some of the evaluated parameters. However, only the association of food readjustment with exercise training was effective in promoting metabolic, hemodynamic, autonomic, and inflammatory benefits in a model of loss of ovarian function with diet‐induced obesity.
## INTRODUCTION
Menopause is an aging‐related natural event, characterized by the final menstrual period with the loss of ovarian function and progressive decline in endogenous estrogen levels (Ambikairajah et al., 2019; Pae et al., 2018). It is well‐established that the hormonal changes in menopause are associated with an increase in central adiposity and changes in body fat distribution (Ambikairajah et al., 2019; Peppa et al., 2012). Experimentally, ovariectomy is an established model for mimicking the changes in human menopause, because it has several similar human risk factors, such as increased blood pressure, impaired myocardial function and autonomic modulation, as well as reduced baroreflex sensitivity (Braga et al., 2015; Sanches et al., 2015; Shimojo et al., 2018).
The association of menopause with changes in adipose tissue also suggests metabolic damage, in the production of pro‐inflammatory cytokines, increasing free fatty acids and visfatin production. These adaptations are probably associated with an increase in susceptibility to insulin resistance, dyslipidemia, and hypertension (Peppa et al., 2012). Indeed, the loss of ovarian function added to a diet‐induced obesity increased metabolic risks, body weight, and adiposity, along with inflammatory profile damage (Ludgero‐Correia et al., 2012; Pae et al., 2018).
It has been reported that the reduction of ovarian hormones is related to sympathovagal imbalance associated with reduced parasympathetic modulation (Adachi et al., 2011). In addition, when obesity is diagnosed, changes, such as baroreflex impairment, increase in leptin, insulin resistance, and obstructive sleep apnea, are all indicative of increased sympathetic nervous system activity, probably associated with a down‐regulation of beta‐adrenergic receptors (Guarino et al., 2017). These changes may lead to increased blood pressure and susceptibility to hypertension (Guarino et al., 2017). Indeed, obesity in postmenopausal women poses major cardiovascular risks, with elevated systolic and diastolic blood pressure associated with high arterial stiffness (Son et al., 2017).
The guidelines of the American Heart Association (AHA), the American College of Cardiology (ACC), among others, recommend changes in dietary patterns, increased level of physical activity, and reduced body weight (Whelton et al., 2018). Indeed, food readjustment is effective in promoting increased vagal modulation in severe obesity (Paul et al., 2003). Increasing vagal modulation and decreasing sympathovagal balance in a population with obesity‐associated type 2 diabetes may induce resting bradycardia (Ziegler et al., 2015). Moreover, food readjustment can contribute to reducing systolic blood pressure in normal and ovariectomized rats (Roberts et al., 2001).
In parallel with dietary effects, the literature reports positive effects of physical training on baroreflex sensitivity, decrease in oxidative stress, resting bradycardia and parasympathetic modulation in experimental models of loss of ovarian function (da Palma et al., 2016; Irigoyen et al., 2005). This is associated with hemodynamic, autonomic, and inflammatory benefits in a model of metabolic syndrome with loss of ovarian function (Conti et al., 2015).
Although both types of intervention promote improvements, each change different parameters. Thus, we should assess the effectiveness of these strategies (food readjustment and/or exercise training) in a condition of obesity plus ovarian deprivation and determine which mechanisms are changed when this nonpharmacologic approach is adopted. Thus, the objective of this study was to evaluate the effects of food readjustment and/or exercise training on metabolic, hemodynamic, autonomic, and inflammatory parameters in a model of loss of ovarian function with diet‐induced obesity. Therefore, our hypothesis is that the deleterious cardiovascular, metabolic, and inflammatory effects due ovarian hormones deprivation and high‐fat diet may be attenuated by physical training and food readjustment.
## Experimental model and study groups
Experiments were performed using 40 female C57BL/6J mice, 9 weeks of age, from the School of Medicine of the University of São Paulo, housed in a temperature‐controlled room (22°C) with a 12‐h dark/light cycle. Until the 5th week of the protocol all animals had the same treatment. They were fed a high‐fat diet and after the recovery of ovariectomy surgery they were reallocated into each of the 4 experimental groups. They are: HIGH‐FAT (HF)—fed a high‐fat diet ($60\%$ lipids) until the end of the protocol;FOOD READJUSTMENT (FR)—fed a low‐fat diet ($10\%$ lipids) until the end of the protocol;HIGH‐FAT DIET WITH EXERCISE TRAINING (HFT)—fed a high‐fat diet until the end of the protocol, plus exercise training from the 6th to 10th weeks of protocol;FOOD READJUSTMENT WITH EXERCISE TRAINING (FRT)—fed a low‐fat diet until the end of the protocol, plus exercise training from the 6th to 10th protocol week; The mice received Ain‐93 (chow with $10\%$ lipids) or Ain‐93 adapted (chow with $60\%$ lipids) (Reeves et al., 1993). Food readjustment was characterized by consumption of a high‐fat diet (Ain‐93 adapted) during 5 weeks, followed by consumption of feed (Ain‐93) until the end of the protocol (5 weeks). The high‐fat diet groups consumption of chow with $60\%$ lipids continued for the entire protocol (10 weeks), The exchange of diet mentioned above aimed to verify the influence of dietary adjustment alone or associated with the practice of physical training. All animals were anesthetized (50 mg/kg ketamine and 10 mg/kg xylazine, intraperitoneal, i.p), and a small abdominal incision was performed, the oviduct was sectioned, and the ovaries removed (Heeren et al., 2009; Marchon et al., 2015). To clarify the moments of the experiment, a protocol diagram is presented in Figure 1. The Ethics Committee of Sao Judas Tadeu has approved the research project (protocol number $\frac{025}{2016}$).
**FIGURE 1:** *Protocol diagram.*
The animals had to walk and run on a motorized treadmill (10 min/day; 0.3 km/h) for 4 consecutive days, before the maximal running test. The maximal running test was performed by all groups as described in detail in a previous study (De Angelis et al., 2004; Heeren et al., 2009). Exercise training was performed on a treadmill (Imbramed TK‐01, Brazil) at moderate intensity (∼$60\%$–$80\%$ maximal running speed) for 1 h per day, for 4 weeks (Heeren et al., 2009; Marchon et al., 2015).
At the end of the experiment, gastrocnemius and white adipose tissue were collected after euthanasia (Flues et al., 2010). All procedures were approved by the Institutional Ethics Committee of São Judas University ($\frac{025}{2016}$), according to the guidelines of the National Council of Control of Animal Experimentation.
## Measurement of blood glucose level and Oral glucose tolerance test (OGTT)
The animals were fasted overnight for 12 h (8 pm–8 am) with free access to water. Glucose was measured using an Accu‐Check Advantage Blood Glucose Monitor (Roche Diagnostic Corporation). Animals were fasted for 12 h, given a gavage glucose load (1.4 g/kg), and blood samples were taken at baseline and 15, 30, 60, 90, and 120 min from a cut made at the tip of the tail (Song et al., 2017).
## Hemodynamic measurements
Two days after the last training session, mice were anesthetized (mixture of $0.5\%$–$2\%$ isoflurane and $98\%$ O2 at a flow rate of 1.5 L/min), and polyethylene‐tipped Tygon cannulas filled with heparinized saline were inserted into the carotid artery and jugular vein for direct measurements of arterial pressure (AP) and drug administration, respectively.
Two days after surgery, hemodynamic measurements were performed, the animals were awake and allowed to move freely in their cages. The cannula was coupled to a biological signal transducer for recording blood pressure signals (Blood Pressure XDCR, Kent Scientific) for 30 min using a digital converter (Windaq DI720, 4‐kHz sampling frequency, Dataq Instruments) (Heeren et al., 2009; Marchon et al., 2015). The recorded data were analyzed on a beat‐to‐beat basis to quantify changes in mean arterial pressure (MAP) and HR.
## Baroreflex sensitivity evaluation
Baroreflex sensitivity was evaluated by tachycardic or bradycardic responses induced by two injections of sodium nitroprusside (8 g/kg body wt IV) or phenylephrine (8 g/kg body wt IV), respectively. Data were expressed as beats per minute (bpm) per mm Hg. Maximal dose per injection was <20 μg (De Angelis et al., 2004).
Peak increases or decreases in MAP after phenylephrine or sodium nitroprusside injection and the corresponding peak reflex changes in HR were recorded for each drug dose. The drugs were administered randomly in all animals, its response peaks (maximum blood pressure change) were usually observed between 3 to 4 s with phenylephrine, and response peaks were usually observed between 8 and 10 s with sodium nitroprusside. After the return of blood pressure the baseline the injections were applied. Baroreflex sensitivity was calculated by the ratio between changes in HR to the changes in MAP, allowing a separate analysis of reflex bradycardia and reflex tachycardia.
## Cardiovascular autonomic modulation
Pulse interval (PI) variability and systolic arterial pressure (SAP) variability were assessed in time and frequency domains by spectral analysis using Cardioseries Software (V2.7). For frequency domain analysis of cardiovascular autonomic modulation, PI and SAP were divided into segments and overlapped by $50\%$, cubic spline‐decimated to be equally spaced in time after linear trend removal; power spectral density was obtained through the fast Fourier transformation. The components of spectral analysis were quantified in the low‐frequency ranges (LF, 0.4–1.5 Hz) (Pelat et al., 2003; Thireau et al., 2008) and high‐frequency ranges (HF, 1.5–5.0 Hz) (Pelat et al., 2003; Thireau et al., 2008).
## Inflammatory mediators
Interleukin 6 (IL‐6), interleukin 10 (IL‐10), and tumor necrosis factor alpha (TNF‐α) levels were determined in adipose tissue using a commercially available ELISA kit (R&D Systems Inc.), according to the manufacturer's instructions. ELISA was performed in a 96‐well polystyrene microplate with a specific monoclonal antibody coating. Absorbance was measured at 540 nm in a microplate reader (Shimojo et al., 2018). Moreover, the ratio of pro‐inflammatory cytokines to anti‐inflammatory was performed to analyze the inflammatory profile (Feriani et al., 2018).
## Statistical analysis
The data were evaluated in Graph Pad Prism (V 8.0.1). The results are presented as mean ± SEM. Data homogeneity was tested through the Kolmogorov–Smirnov test. Two experimental groups were compared using one‐way ANOVA with Tukey post hoc test. The significance level adopted was $p \leq 0.05.$
## RESULTS
Body composition (final body weight, gastrocnemius, and white adipose tissue), metabolic assessments (blood glucose level and OGTT), the maximal running test (initial and final), and average consumption (grams and calories) are shown in Table 1. The average consumption was increased in relation to the grams in food readjustment groups (HF and HFT vs. FR and FRT). Only the association of food readjustment with exercise training (FRT) reduced body weight and white adipose tissue compared with the control group (HF). Blood glucose level was reduced, and the glucose mobilization capacity was improved represented by the smaller area under the curve in the FRT in relation to high‐fat groups (HF and HFT). Moreover, the trained groups had increased maximum running capacity (HF vs. HFT and FRT).
**TABLE 1**
| Parameters | HF | FR | HFT | FRT | p |
| --- | --- | --- | --- | --- | --- |
| Average consumption of grams (every 2 days) | 3.47 ± 018 | 5.08 ± 0.31 a | 3.23 ± 0.13 b | 5.44 ± 023 a , c | <0.001 |
| Average consumption of calories (every 2 days) | 18.57 ± 0.97 | 19.31 ± 1.19 | 17.32 ± 0.68 | 20.68 ± 0.88 | 0.115 |
| Maximal running capacity initial (s) | 718.30 ± 29.39 | 692.60 ± 55.39 | 640.90 ± 31.93 | 686.50 ± 20.77 | 0.463 |
| Maximal running capacity final (s) | 677.9 ± 50.06 | 676.9 ± 72.7 | 929.3 ± 39ab | 903.9 ± 54.41 a , b | 0.001 |
| Final body weight, (g), 10 weeks | 29.1 ± 1.321 | 27.03 ± 0.7251 | 27.98 ± 0.8722 | 22.73 ± 0.5687abc | <0.001 |
| Total white of adipose tissue, (g) | 0.07768 ± 0.0094 | 0.05914 ± 0.0058 | 0.07886 ± 0.0104 | 0.04942 ± 0.0039 a | 0.032 |
| Blood glucose (mg/dL) | 160.1 ± 10.79 | 131.6 ± 10.1 | 162.2 ± 7.13 | 113.1 ± 11.81 a , c | 0.004 |
| OGTT (AUC) | 23,137 ± 1223 | 20,064 ± 703.1 | 23,722 ± 1144 | 17,344 ± 1304 a , c | 0.001 |
No differences were observed in blood pressure, and resting bradycardia was obtained only by exercise training plus food readjustment (HF vs. FRT) (Table 1). However, baroreflex sensitivity was increased in both trained groups (HFT and FRT) compared with the control group (HF), for bradycardic response (BR: HF: 2.00 ± 0.29; FR: 2.84 ± 0.23; HFT: 3.48 ± 0.54; FRT: 3.46 ± 0.35; bpm/mm Hg, $$p \leq 0.0155$$) and tachycardic response (TR: HF: 3.15 ± 0.55; FR: 3.95 ± 0.49; HFT: 6.33 ± 0.96; FRT: 6.02 ± 0.65; bpm/mm Hg, $$p \leq 0.0039$$) (Figure 2).
**FIGURE 2:** *Baroreflex sensitivity parameters in high‐fat (HF) (n = 10), food readjustment (FR) (n = 10), high‐fat plus exercise training (HFT) (n = 10), and food‐readjustment plus exercise training (FRT) (n = 10). (a) Bradycardic response; (b) Tachycardic response. Different letter indicates statistically different groups (one‐way ANOVA + Tukey test, p < 0.05). Values reported as mean ± SEM. a = p < 0.05 vs. HF.*
Interestingly, we observed an increase in all parameters of time domain measurements of heart rate variability by exercise training plus food readjustment (HF vs. FRT), the cardiac autonomic modulation (VAR‐PI) and the cardiac parasympathetic modulation (RMSSD) (Table 2). Regarding the time‐domain of cardiac autonomic modulation, no changes were observed in LF‐PI and HF‐PI; however, the LF/HF ratio was reduced only in the exercise training group (HF vs. HFT) (Table 2).
**TABLE 2**
| Parameters | HF | FR | HFT | FRT | p |
| --- | --- | --- | --- | --- | --- |
| Heart rate | 681 ± 13 | 663 ± 18 | 627 ± 39 | 607 ± 14 a | 0.049 |
| (bpm) | 681 ± 13 | 663 ± 18 | 627 ± 39 | 607 ± 14 a | 0.049 |
| Systolic arterial pressure | 134.5 ± 2.60 | 130.4 ± 2.86 | 128 ± 3.20 | 123.6 ± 4.91 | 0.201 |
| (mm Hg) | 134.5 ± 2.60 | 130.4 ± 2.86 | 128 ± 3.20 | 123.6 ± 4.91 | 0.201 |
| Diastolic arterial pressure | 95.09 ± 2.37 | 94.41 ± 1.89 | 87.54 ± 5.55 | 88.3 ± 3.68 | 0.345 |
| (mm Hg) | 95.09 ± 2.37 | 94.41 ± 1.89 | 87.54 ± 5.55 | 88.3 ± 3.68 | 0.345 |
| Mean arterial pressure (mm Hg) | 115.9 ± 2.15 | 112 ± 2.15 | 106.8 ± 4.91 | 109.2 ± 2.15 | 0.281 |
| VAR‐PI (ms2) | 15.67 ± 3.32 | 49.22 ± 17.11 | 29.52 ± 10.40 | 66.98 ± 11.84 a | 0.046 |
| RMSSD (ms) | 2.14 ± 0.27 | 3.01 ± 0.64 | 2.92 ± 0.59 | 4.77 ± 0.80 a | 0.049 |
| LF‐PI (ms2) | 6.03 ± 2.24 | 1.64 ± 0.64 | 1.99 ± 0.95 | 3.59 ± 1.73 | 0.227 |
| HF‐PI (ms2) | 1.71 ± 0.37 | 2.24 ± 1.55 | 3.15 ± 1.39 | 3.01 ± 1.69 | 0.291 |
| LF/HF) | 2.83 ±0.61 | 1.36 ±0.31 | 0.90 ±0.32 a | 1.22 ±0.31 | 0.016 |
The variance of systolic blood pressure was reduced by exercise training plus food readjustment compared with that in the control group (Var‐SAP: HF: 35.59 ± 7.91; FR: 25.25 ± 1.64; HFT: 16.98 ± 3.71; FRT: 9.61 ± 1.74; mm Hg2, $$p \leq 0.0018$$). However, the low‐frequency band of systolic blood pressure did not change (Figure 3).
**FIGURE 3:** *Variability of systolic arterial pressure parameters in high‐fat (HF) (n = 7), food‐readjustment (FR) (n = 7), high‐fat plus exercise training (HFT) (n = 8), and food‐readjustment plus exercise training (FRT) (n = 8). (a) Variance of systolic blood pressure (VAR‐SAP); (b) Low‐frequency band of systolic blood pressure (LF‐SAP). Different letter indicates statistically different groups (one‐way ANOVA + Tukey test, p < 0.05). Values reported as mean ± SEM. a = p < 0.05 vs. HF. b = p < 0.05 vs. FR.*
No differences were observed in either pro‐inflammatory (IL‐6 and TNF‐alfa) or anti‐inflammatory (IL‐10) parameters (Figure 4). However, when pro‐inflammatory cytokines were associated with anti‐inflammatory ones (IL‐6/IL10 and TNF‐alfa/IL‐10), a reduction was obtained with both interventions (exercise training and/or food readjustment) (IL‐6/IL10: HF: 1.02 ± 0.13; FR: 0.71 ± 0.05; HFT: 0.60 ± 0.04; FRT: 0.64 ± 0.06; pg/mL/mg, $$P \leq 0.034$$; TNF‐alfa/IL‐10: HF: 0.13 ± 0.01; FR: 0.08 ± 0.01; HFT: 0.07 ± 0.01; FRT: 0.08 ± 0.01; pg/mL/mg, $$p \leq 0.001$$) (Figure 4).
**FIGURE 4:** *Inflammatory profile in high‐fat (HF) (n = 6), food‐readjustment (FR) (n = 5), high‐fat plus exercise training (HFT) (n = 6), food‐readjustment plus exercise training (FRT) (n = 5). (a) TNF‐α; (b) Interleukin‐6 (IL‐6); (c) Interleukin‐10 (IL‐10); (d) ratio of TNF‐ α and Interleukin‐10; (e) ratio of Interleukin‐6 and Interleukin‐10. Different letter indicates statistically different groups (one‐way ANOVA + Tukey test, p < 0.05). Values reported as mean ± SEM. a = p < 0.05 vs. HF.*
## DISCUSSION
The current study attempted to determine the effects of food readjustment and/or exercise training on metabolic, hemodynamic, autonomic, and inflammatory parameters in a model of loss of ovarian function with diet‐induced obesity. Food readjustment promoted a reduction in pro‐ and anti‐inflammatory cytokine ratio, whereas exercise training also promoted inflammatory profile benefits, increased the maximal running capacity, improved sympathovagal balance, and increased baroreflex sensitivity.
Our findings demonstrated that exercise training plus food readjustment may be a powerful strategy to improve maximal running capacity; body composition was also improved with the decrease in adipose tissue and body weight, leading to changes in metabolic assessments, such as a reduction in blood glucose and OGTT. Resting bradycardia and positive changes in autonomic modulation were observed in the increased heart rate variability, augmented cardiac parasympathetic modulation and reduced vascular sympathetic modulation, increased baroreflex sensitivity, as well as reduced pro‐ and anti‐inflammatory cytokine ratios.
Despite the average increase in the intake of grams in the food readjustment groups, the total intake of calories was equivalent among all groups, which demonstrates that the nature of the macronutrients is probably related to metabolic damage and suggests that leptin action remains unchanged among groups. Studies have shown that with food readjustment the decrease in adipose tissue is accompanied by improvement in glucose metabolization associated with decreased basal glucose (Ziegler et al., 2015). These adaptations in metabolism were obtained only when food readjustment was associated with exercise training. Changes on HRV parameters (SDNN and RMSSD) are accompanied by inversed changes to blood glucose levels (Ernst, 2017). Thus, the better cardiac autonomic control in FRT may have potentialized the expected effect of the food readjustment in glycemic control.
Although the evidence points to a role of nonpharmacologic strategies for the management of high blood pressure, with a lowering of blood pressure by reducing of body weight (Whelton et al., 2018), our findings demonstrate that the reduction in body weight and adipose tissue in this model was not effective in promoting changes in arterial pressure. On the contrary, the association of food readjustment plus exercise training was effective in decreasing vascular sympathetic modulation. A recent meta‐analysis using nonpharmacological approaches for reducing body weight (food readjustment and/or exercise training) seems to lend support to the finding of reduction in sympathetic nervous activity (Costa et al., 2018). As autonomic changes precede other changes (Bernardes et al., 2018), two strategies may be recommended to obtain blood pressure reduction: a longer intervention time or a greater total volume of exercises in a shorter time.
According to the literature, estrogen exhibits vasodilatory properties, and the intracellular transmembrane G protein‐coupled estrogen receptor is one of the vascular estrogen binding sites, next to ERα and ERβ, which is closely related to pressure reduction, in addition to acting in body weight maintenance (Haas et al., 2009). Thus, ovarian deprivation causes increased body weight and blood pressure, and this seems to be linked to the absence of estrogen for its receptors, causing them to lose them functions (Shi et al., 2013).
Resting bradycardia is a regular finding in chronic exercise, generated by an improvement in frank‐starling mechanism and in cardiac autonomic modulation (Almeida & Araújo, 2003; Shimojo et al., 2018). The exercise training‐induced reduction in sympathovagal balance represents a better relationship between the autonomic sympathetic and parasympathetic loops. However, other benefits in cardiac autonomic modulation (increased Var‐PI) and in parasympathetic autonomic modulation (increased RMSSD) were induced by only exercise training plus food readjustment. In fact, in different populations these strategies are effective in promoting this benefit (da Palma et al., 2016; Ziegler et al., 2015).
There is few evidence of cardiac function and histology of the heart in the model used in this study (ovarian deprived female mice, fed high‐fat diet and exercise training). However, it is known that the high‐fat plus ovarian deprivation modifies cardiomyocyte diameter in Wistars rats, promoting cardiac hypertrophy (Goncalves et al., 2017). Additionally, in APOB‐100 transgenic female mice (model of metabolic syndrome) plus consumption of high‐fat diet, exercise training is protective against the development of pathological cardiac hypertrophy, with the maintenance of important indicators of cardiac function (Tóth et al., 2022). Thus, possibly the cardiac structure of mice with ovarian deprivation plus consumption of high‐fat diet presents a series of deleterious adaptations, and exercise training probably can attenuate this picture. However, specific studies are needed to understand these phenomena.
Exercise training improves functional capacity and, consequently, improves baroreflex sensitivity in different types of comorbidities associated with loss of ovarian function (Irigoyen et al., 2005; Shimojo et al., 2018). Indeed, arterial baroreflex is effective in mediating cardiac disorders associated with arterial hypertension, which is crucial for cardiovascular, morpho functional, and autonomic adaptive benefits induced by chronic exercise (Moraes‐Silva et al., 2010). Thus, we can consider that the beneficial effects observed in trained groups in cardiovascular autonomic parameters are associated with the improvement in baroreflex activity in these groups.
In the food readjustment group, the reduction in the inflammatory profile is justified by the shorter period of consumption of a high‐fat diet, and consequently, these animals had a reduction in adipose tissue (↓$23.86\%$) and in inflammation. In addition, a reduction was also expected in the group with the association of strategies.
There were some limitations to this study. First, the absence of a control group that could provide additional answers about the effects to the food readjustment and exercise in without ovariectomy animals. Second, the short period of surgical recovery after the canulation may influence autonomic and hemodynamic outcomes.
## Conclusion
The findings of the present study lend support to the hypothesis that exercise training and food readjustment promote specific benefits in some of the evaluated parameters. However, only the association of food readjustment with exercise training was more effective in promoting metabolic, hemodynamic, autonomic, and inflammatory benefits in a model of loss of ovarian function with diet‐induced obesity.
## AUTHORS' CONTRIBUTIONS
Bruno Nascimento‐Carvalho conceived and designed the research, conducted experiments, analyzed data, interpreted results of experiments, drafted the manuscript, edited and revised the manuscript; Adriano dos‐Santos conducted experiments and revised the manuscript; Nicolas Da Costa‐Santos conducted experiments and revised the manuscript; Sabrina L. Carvalho conducted experiments and revised the manuscript; Oscar A. de Moraes analyzed data and revised the manuscript; Camila P. Santos conducted experiments, analyzed data, and revised the manuscript; Katia De Angelis interpreted results of experiments and revised the manuscript; Erico C. Caperuto edited and revised the manuscript; Maria‐Claudia Irigoyen drafted the manuscript, edited and revised the manuscript; Katia B. Scapini interpreted results of experiments and revised the manuscript; Iris C. Sanches conceived and designed the research, interpreted results of experiments, edited and revised the manuscript.
## FUNDING INFORMATION
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001″; Conselho Nacional de Desenvolvimento Científico e Tecnológico (PQ ‐ CNPq ‐ process $\frac{307138}{2015}$–1 and process $\frac{435123}{2018}$–1); and ANIMA INSTITUTE—AI. CAPES provided support for personal demands of the main author; CNPq provided support for laboratory demands of research. ANIMA provided support for Human Movement Lab, São Judas University.
## CONFLICT OF INTEREST
No conflicts of interest, financial or otherwise, are declared by the authors.
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title: Cord serum metabolic signatures of future progression to immune-mediated diseases
authors:
- Tuulia Hyötyläinen
- Bagavathy Shanmugam Karthikeyan
- Tannaz Ghaffarzadegan
- Eric W. Triplett
- Matej Orešič
- Johnny Ludvigsson
journal: iScience
year: 2023
pmcid: PMC10005901
doi: 10.1016/j.isci.2023.106268
license: CC BY 4.0
---
# Cord serum metabolic signatures of future progression to immune-mediated diseases
## Summary
Previous prospective studies suggest that progression to autoimmune diseases is preceded by metabolic dysregulation, but it is not clear which metabolic changes are disease-specific and which are common across multiple immune-mediated diseases. Here we investigated metabolic profiles in cord serum in a general population cohort (All Babies In Southeast Sweden; ABIS), comprising infants who progressed to one or more immune-mediated diseases later in life: type 1 diabetes ($$n = 12$$), celiac disease ($$n = 28$$), juvenile idiopathic arthritis ($$n = 9$$), inflammatory bowel disease ($$n = 7$$), and hypothyroidism ($$n = 6$$); and matched controls ($$n = 270$$). We observed elevated levels of multiple triacylglycerols (TGs) an alteration in several gut microbiota related metabolites in the autoimmune groups. The most distinct differences were observed in those infants who later developed HT. The specific similarities observed in metabolic profiles across autoimmune diseases suggest that they share specific common metabolic phenotypes at birth that contrast with those of healthy controls.
## Graphical abstract
## Highlights
•*Cord serum* metabolomics performed in a general population prospective birth cohort•Primary outcomes of interest were later progression to specific autoimmune diseases•Many similarities in metabolic profiles were detected across the autoimmune diseases•Later hypothyroidism had the most distinct metabolic profile, with increased lipids
## Abstract
Health sciences; Human metabolism; Immunology; Lipidomics; Metabolomics
## Introduction
Autoimmunity is a complex process contributing to widespread functional decline that affects multiple organs and tissues. Overall, over 80 autoimmune diseases have been identified including, among the most common ones, type 1 diabetes (T1D), multiple sclerosis, celiac disease (CD), inflammatory bowel disease (IBD), and rheumatoid arthritis (RA).1 Several of the autoimmune diseases are manifested in childhood. The prevalence and incidence of several of these autoimmune diseases have increased over the last decades.2,3,4,5 *The pathogenesis* of most of the autoimmune diseases is, however, generally not fully characterized. It has been suggested that both genetic predisposition and environmental factors, and their mutual interactions, play a significant role in the disease pathogenesis.6,7 Many autoimmune diseases share common risk factors or pathogenic mechanisms. For example, T1D and CD share common predisposing alleles in the class II HLA-region.8,9 Approximately $6\%$ of patients with T1D also develop clinical CD10 whereas subjects with CD are at risk for developing T1D before age 20,10 T1D, multiple sclerosis (MScl), and RA are also classified as T cell-mediated autoimmune diseases.11 Importantly, it has been shown that fundamental processes underlying T cell functionality are linked to changes in the cellular metabolic programs.12 External perturbation of key metabolic processes may impair T cell activation, differentiation, and cytokine production. We have also shown that differentiating human CD4+T-cells have subset-specific differences in glycosphingolipid pathways.13 Abnormal metabolism is a common feature of several autoimmune diseases, which occurs before the onset of clinical disease, including in T1D,14,15 CD,16,17,18 and IBD.9 Changes in specific phospholipids and amino acids have been reported at birth in genetically disposed children who progressed to islet autoimmunity and T1D later in life.14 In adolescents and adults, similarly as in children, metabolic dysregulation related to altered phospholipid profiles and alteration in steroidogenesis, bile acid biosynthesis and sugar metabolism have been reported.19 In future CD, altered levels of phospholipids and triacylglycerols have been detected already before the infants had been exposed to gluten.9 In pediatric IBD, alteration in metabolome, including phospholipids, has been reported,20 with similar changes being reported also in adults including downregulation of alky lether phospholipids such as plasmalogens.21 In other autoimmune diseases, dysregulated amino acid, central carbon, and phospholipid metabolism have been associated with rheumatoid arthritis.22,23 In autoimmune thyroid disease, altered amino acid pathways, primary bile acid biosynthesis, and steroid hormone biosynthesis have been identified.24,25 In adult CD, recent meta-analysis reported conflicting results, however, most studies were focused on a limited set of metabolites, such as short-chain fatty acids and ketogenic metabolites26 and the adult CD is highly heterogeneous. Overall, especially in children, current data thus suggest that there may be some commonalities between metabolic signatures preceding different autoimmune diseases. However, at present there are very few studies comparing common and specific metabolic patters preceding multiple autoimmune diseases.
Herein, we investigate cord serum metabolomes in a general population cohort (All Babies In Southeast Sweden; ABIS),27 comprising children who later progressed to one or more immune-mediated diseases (T1D, CD, juvenile RA [JIA], IBD, hypothyroidism [HT]), and matched controls. We studied the metabolic changes across all autoimmune mediated disease groups, looking at the overall metabolic changes in those subjects later developing a specific disease. We also investigated whether maternal lifestyle factors had an impact on the observed changes, and further investigated the association of the specific HLA-conferred risk factors with metabolic profiles.
## Metabolic profiles in cord blood
A total of 545 lipids and 3,417 polar/semipolar metabolites were detected in cord serum, of which 201 lipids and 120 metabolites were identified at the level 1 and 2 and quantified, and additional 20 metabolites were identified at the level 3 (Metabolomics Standard Initiative28 as marked in Tables S1 and S2). To investigate global changes of metabolomes across the study groups (Table 1), including also the unidentified compounds, we first performed model-based clustering for the two datasets separately, with the clustering resulting in 8 lipid clusters (LC) and 12 polar metabolite clusters (PC) (Table 2).Table 1Demographic characteristics of the study cohort. Values shown as means (standard deviation), unless noted otherwiseCDIBDJIAHTT1DControlsN (F/M)28 ($\frac{9}{19}$)7 ($\frac{5}{2}$)9 ($\frac{3}{6}$)6 ($\frac{0}{6}$)12 ($\frac{8}{4}$)270 ($\frac{124}{152}$)Gestational age (weeks)40 (1.7)40 (1.7)40 (1.1)39 (1.8)39 (1.0)40 (1.5)Birth weight (g)3705 [586]a3540 [753]3640 [487]3163 [488]a3745 [512]3580 [503]Maternal age (year)30.0 (4.6)31.0 (3.0)27.0 (6.1)30.5 (6.0)30.0 (6.5)29.0 (4.7)Maternal BMI (kg/m2)23.0 (4.7)22.3 (2.4)23.7 (1.5)23.4 (3.5)22.9 (4.8)22.9 (3.8)Delivery (vaginal/cesarean/b)$\frac{22}{3}$/$\frac{34}{0}$/$\frac{37}{2}$/$\frac{05}{0}$/$\frac{17}{3}$/$\frac{2231}{23}$/48Age of diagnosis (years)11.5 (5.7)16 (1.6)15 (5.3)16 (1.6)13.5 (3.3)NAaSignificant difference in comparison with the control group.bnot available for all subjectsTable 2Description of lipid (LC) and polar metabolite (PC) clustersClusterMain classes of compoundsSpecific examplesLC1LPC, SM, CerSM(42:3), LPC(22:5), Cer(d18:½4:0)LC2PC, PC_OPC(40:8), PC(40:6), PC(O-40:4)LC3CE, Lac/HexCer, PC, PI, SMCE(18:0), CE(18:1), Hexcer(d18:½4:0)LC4PC_PUFA, LPC_PUFALPC(18:2), LPC(20:4), PC(38:4)LC5TG_SFATG(14:$\frac{0}{16}$:$\frac{0}{18}$:1),TG(16:$\frac{0}{16}$:$\frac{0}{16}$:0), TG(50:0)LC6TG_MUFA, TG_PUFATG(58:9), TG(18:$\frac{1}{18}$:½2:6), TG(58:6)LC7UnknownsPutative identifications: TGsLC8UnknownsPutative identifications: various phospholipidsPC1Bile acids, microbial metabolitesCA, CDCA, GCA, 3-indoleacetic acidPC2Amino acidsValine, Phenylalanine, lysine, serinePC3Free fatty acids, lipidsArachidonic acid, 16-Hydroxypalmitate, LPC(17:0)PC4Unknowns, highly polar compoundsPC5Free fatty acids, lipidsC16:1, C18:2, linoleic acidPC6UnknownsPC7UnknownsPC8UnknownsPC9UnknownsPC10UnknownsPC11Unknowns, highly polar compoundsPutative identifications: exogeneous compoundsPC12Unknowns We first investigated whether the gestational age, sex, birth weight or maternal factors (including BMI, maternal age, maternal diagnosis, dietary patterns) had an impact on the metabolome. Out of these parameters, gestational age and birth weight showed the most significant association with metabolite clusters (Figure 1) and several individual metabolites (Table S1). Also, maternal age showed associations with the lipid and metabolite clusters. Maternal BMI and diet had modest impact on cord blood metabolome, the former via positive associations with TGs containing saturated fatty acyls. The latter had weak impact on the cord blood metabolome (R below ±0.25), except for three known metabolites of coffee that showed significant association between maternal coffee consumption and cord blood levels of these metabolites ($R = 0.38$–0.81, $p \leq 0.0001$). Among maternal diagnoses, other food allergies than lactose intolerance or nut allergy showed significant associations with clusters LC7, LC8 and PC3, smoking with four polar metabolite clusters (PC4, PC7, PC8 and PC11), use of antibiotics with LC5, LC6, PC7 and PC12 and educational level with PC7 and PC12. The latter may be attributed to the negative association between the educational level and smoking, and associations between educational level and diet (negative association between educational level and vegetables in the diet, positive association with eating French fries).Figure 1Associations of various demographic and lifestyle factors, and food intake with metabolomeSpearman correlations shown between lipid and polar metabolite clusters and the metadata. ∗$p \leq 0.05.$ Abbreviations: BW, birth weight; Del, delivery mode (cesarean versus vaginal); GA, gestational age; Sex (female versus male); T1D, type 1 diabetes; T2D, type 2 diabetes.
For further data analyses, we investigated the impact of adjustment with maternal age, maternal BMI, gestational age, and birth weight. Among these factors, maternal age, gestational age and birth weight had an impact on the results, and for further data analysis, the data were adjusted with these three factors.
## Autoimmune diseases share similar metabolic profiles already at birth
We observed significant differences between the control group and the different diagnostic groups, both at the level of lipid and metabolite clusters as well as at the level of individual metabolites (Figure 2, Tables S2 and S3), after adjustment for gestational age, birth weight and maternal age. We investigated the differences both at the level of individual disease diagnosis as well as by pooling all autoimmune cases together, excluding the HT group as it appeared to be an outlier among the disease groups. Figure 2Comparison of different autoimmune disease groups and controls at the metabolite cluster level(A) Lipid clusters and (B) metabolite clusters. Logfold change (FC) with ∗p.adjusted<0.05. Cluster descriptions are provided in Table 2.
Among the individual diagnostic groups, the subjects who later developed HT differed most significantly from the control group. Five of the eight lipid clusters showed significantly upregulated levels in HT compared to controls. Overall, all disease groups showed a trend of upregulation of lipid clusters LC5, LC6 and LC8, although the difference between the groups compared with controls was only significant for HT. These three lipid clusters are composed of mainly triacyclglycerols (TGs). Overall, T1D and IBD clustered together with similar trend over multiple lipid clusters. Similarly, CD and JIA clustered together. On metabolic cluster level, T1D showed significant differences in comparison with control group in PC2, PC4, PC7 and PC11. The CD group showed significant differences in PC4 and PC5, whereas the IBD group showed significant differences in PC6. PC2 includes mainly amino acids, PC5 includes mainly on free fatty acids, and other polar lipids, PC4 and PC11 consist of mainly unidentified metabolites, which based on their chromatographic behavior are highly polar small metabolites, whereas PC7 includes semipolar compounds putatively identified as free fatty acids and polar lipid derivatives.
Among the individual metabolites, 17 lipids and seven polar metabolites were different between the control and case groups at the level of nominal p values; however, none reached statistical significance after FDR correction. These lipids were mainly TGs comprising saturated fatty acyls, whereas the polar metabolites included mainly secondary bile acids, one short-chain fatty acid, and two amino acids. In specific diseases, we observed changes particularly in HT in lipids, with upregulated levels of large number of lipids (TGs, SMs, and several other phospholipids) and downregulation of dehydroepiandrosterone sulfate. In CD, we observed a trend of decreased levels of phospholipids (PC, SM), secondary bile acid UDCA and serine and increased TGs, isovaleric acid and C20:5. In IBD, trend of decreased levels of ether PCs and some other phospholipids were observed as well as increased levels of isovaleric and isocapric acid. In JIA, the main difference was in TGs, with increased levels compared to controls, and also differences in several gut microbiota-related metabolites. In T1D, we observed decreased levels of phospholipids, including PCs and SMs, and downregulation of CDCA and fructose.
## The autoimmune cases showed difference in metabolic co-regulation
Next, we investigated the interplay of the lipid and metabolite clusters and clinical features in autoimmune cases and control groups separately (Figure 3A) as well as those lipids and polar metabolites that showed significant differences (Figure 3B). In autoimmune group, the gestational age showed negative association with PC9 whereas this association was much weaker in control group. The birth weight showed negative association with LC6 in the autoimmune group whereas this association was absent in the control group. We also observed clear differences between the case and control groups in metabolite and lipid cluster mutual associations. Figure 3Relative levels of selected metabolites across the study groups∗p.adjusted<0.05. Linear models adjusted for for maternal age, birth weight and gestational age.
## Pathway analysis reveals alteration in lipid metabolism
Pathway analyses were performed by comparing controls against autoimmune mediated diseases grouped together (CD, T1D, JIA, IBD) using both Mummichog and GeneSet Enrichment Analysis (GSEA) algorithms for the pathway analyses to increase their robustness. We further filtered the results based on the number of metabolites detected in each pathway and the number of significant hits. The results indicated that the autoimmunity was associated with multiple pathways including arachidonic acid metabolism, steroid and tryptophan metabolism (Figure 4).Figure 4Pathway analysis comparing cases (without HT group) versus controlsThe upper panels show pvalues using the MFN (left) and KEGG (right) pathway maps, using Mummichog (yaxis) and GSEA (xaxis) pathway analysis methods. Size of the circle corresponds to the pathway impact value. The corresponding tables with number of metabolites in the pathways (total number/hits/significant hits) and pvalues shown under these panels. Abbreviations: Meta pvalue calculated by combined GSEA and Mummichog score; Sig, significant.
Next, we selected those lipids that contain either arachidonic acid (AA) or docosahexaenoic acid (DHA), as these lipids have shown to be a crucial role in the development of the infant immune system.29 We then examined the difference between the controls and autoimmune groups, by applying a partial correlation analysis (Figures 5A and 5B). The intra-lipid correlations were clearly weaker in the autoimmune group when compared with the control group (Figure 5A), although there was no significant difference in the partial correlation between lipid classes on the two groups (Figure 5B).Figure 5Partial correlation network analysis, done separately for controls and cases (excluding HT)Here, each node represents a metabolite, metabolite cluster, or a clinical parameter (gray color). Each edge represents the strength of partial correlation between two compounds/parameters after conditioning on all other compounds in the datasets. Edge weights represent the partial correlation coefficients, with Edge colors: blue color for negative correlations and red for positive correlations, the thickness of the line shows the strength of the correlation. Edge ranges adjusted between ±0.22 to 1.(A) Arachidonic acid and DHA containing lipids in yellow color, with partial correlations $p \leq 0.1.$(B) Network on the level of lipid (yellow color) and polar metabolite clusters (blue color).
## HLA risk is associated with changes in amino acid and PUFA
Next, we investigated the association between HLA risk genotype and metabolite profiles, both at the cluster and individual metabolite level by using a linear regression model. For T1D, the risk genotypes were classified as decreased, neutral, increased, and high risk while in CD, the groups were very low, low, and moderate. The T1D risk type was associated with LC2, PC2 and PC4, the latter two showing reduced levels in comparison with the decreased genotype versus neutral, increased, and high-risk genotypes (Figure 6). At the level of individual lipids and polar metabolites, large number of phospholipids, both PCs and SMs, particularly those PCs with PUFA showed similar trends, as well as AA and DHA, i.e., with reduced levels with increasing risk HLA risk genotype (Table S4). For CD, the metabolic profiles did not show associations with the risk genotype. Figure 6Impact of HLA-conferred risk for T1D on metabolic profilesLIMMA model, adjusted with maternal age, birth weight and gestational age, logarithmic fold changes between cases with neutral versus decreased risk (green), increased versus decreased risk (yellow) and high versus decreased risk (red) for lipid cluster 2 ($$p \leq 0.018$$) and polar metabolite clusters 2 ($$p \leq 0.041$$) and 4 ($$p \leq 0.015$$).
## Discussion
We performed untargeted metabolomics analyses to obtain a comprehensive picture of metabolic profiles in cord blood samples in infants who later developed autoimmune diseases. The similarities in metabolic profiles, particularly across T1D, JIA, IBD, CD, suggests that the diseases share common metabolic alteration already at birth, i.e., years before the onset of the disease. As a common feature, we observed elevated levels of multiple classes of TGs, including both saturated and polyunsaturated fatty acid containing TGs. In addition, multiple gut microbiota related metabolites, such as secondary bile acids UDCA and ketolithocholic acid, were altered in the autoimmune groups. The most significant pathways impacted were related to arachidonic acid derived fatty acid metabolism (prostaglandin and leukotriene metabolism) and steroid hormone metabolism. Of individual diseases, most distinct differences were observed in those infants who later developed HT, showing significantly increased levels of large number of lipids.
We also observed that phospholipids, particularly PUFA containing lipids, as well as free fatty acids AA and DHA were associated with HLA-conferred disease risk, with decreased levels of this type of lipids with increasing genotype risk profile. The AA pathway has been shown to play a key role in inflammatory processes.30,31 Indeed, chronic inflammation is known to be an underlying cause of multiple diseases, such as metabolic syndrome, type 2 diabetes, non-alcoholic fatty liver disease, hypertension, cardiovascular disease, and autoimmune diseases.32 The role of arachidonic acid in inflammation is related to the production of oxylipins, which are oxygenated lipid mediators that promote or resolve inflammation.30 The AA-related oxylipins are usually considered to be inflammatory, proliferative and vasoconstrictive.30 *Elevated plasma* arachidonic acid to docosahexaenoic acid ratios have also been associated with increased risk of IA in the Finnish Type 1 Diabetes Prediction and Prevention Study (DIPP) birth cohort.33,34 The AA-related oxylipins have also been shown to be associated with increased risk of type 1 diabetes risk in Diabetes Autoimmunity Study in the Young (DAISY) cohort.31 Also in adult subjects with IBD, PUFA dysregulation has been suggested to be associated in the bowel inflammation process through eicosanoids, derived from AA corresponding to increased colonic inflammatory cytokines and increased serum fatty acids.35 Similarly, in rheumatoid arthritis, AA metabolism has been suggested to play an important role in the disease manifestation.22 Currently, there are no previous studies that compared the metabolic patterns in cord-blood of children who later developed different autoimmune diseases in a general population-based set-up, or studies that would have linked the HLA risk type with metabolic profiles in infants. There are multiple studies, including our earlier studies on predictive metabolic patterns of T1D14,36 and CD,9 however, these have been done in a genetically high-risk cohorts. We did observe some similarities with the current study and our earlier results, particularly related to changes in CD. However, it should be noted that the current cohort has distinct differences related to previous studies, particularly as in the current cohort the median age of diagnosis was 15 years, whereas in the high risk T1D and CD cohorts we have investigated earlier the median age of diagnosis was much lower (<10 years). Our results were also in agreement of published results on metabolomic changes reported in patients with rheumatoid arthritis which have reported that children with active JIA had higher plasma triglyceride concentrations compared to healthy control subjects.37,38 Adult subjects with rheumatoid arthritis, on the other hand, have shown to have lower levels of multiple LPCs, which were further correlated with interleukin-6 and disease activity indices.23 Overall, our study suggests that there are shared metabolic characteristics across multiple autoimmune diseases, plausibly because of shared physiopathologic mechanisms, genetic and environmental factors because of autoimmune tautology. However, more mechanistic studies are required to elucidate the pathways responsible for the disease development, and the factors contributing to the process.
This study in a general-population prospective birth cohort indicates that future autoimmune diseases share several common features in metabolic profiles at birth. The causes of these common features and their relevance for disease pathogenesis are yet to be elucidated. Given these metabolic profiles are detected already at birth, likely causes are attributed to maternal diet and other environmental exposures.
## Limitations of the study
We acknowledge limitations of the study. The number of subjects within each disease group was low. This is an inherent limitation of general population study setting when studying the diseases with low incidence. As a strength of such setting, the study is not limited to populations with HLA-conferred risk of specific diseases, thus allowing for comparative studies across the different diseases. Although the analytical coverage of the metabolites was comprehensive, we could not identify all metabolites detected. However, the pathway analysis tool does include the whole data and it also includes pathway to identify the unknown compounds, thus giving a representative view of the metabolic changes at the pathway level.
## Key resources table
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LipidsCat#846725triheptadecanoylglycerol (TG(17:$\frac{0}{17}$:$\frac{0}{17}$:0))LarodanCat#33-1700trihexadecanoalglycerol (TG(16:$\frac{0}{16}$:$\frac{0}{16}$:0))LarodanCat#33-16101-stearoyl-2-linoleoyl-sn-glycerol (DG(18:$\frac{0}{18}$:2))Avanti Polar LipidsCat#8556753-trioctadecanoylglycerol (TG(18:$\frac{0}{18}$:$\frac{0}{18}$:0))LarodanCat#33-18103β-Hydroxy-5-cholestene-3-linoleate (ChoE(18:2))LarodanCat#64-18021-hexadecyl-2-(9Z-octadecenoyl)-sn-glycero-3-phosphocholine (PC(16:0e/18:1(9Z)))Avanti Polar LipidsCat#8008171-(1Z-octadecanyl)-2-(9Z-octadecenoyl)- sn-glycero-3-phosphocholine (PC(18:0p/18:1(9Z)))Avanti Polar LipidsCat#8781121-oleoyl-2-hydroxy-sn-glycero-3- phosphocholine (LPC(18:1))LarodanCat#38-18011-palmitoyl-2-oleoyl-sn-glycero-3- phosphoethanolamine (PE(16:$\frac{0}{18}$:1))Avanti Polar LipidsCat#8524673β-hydroxy-5-cholestene-3-stearate (ChoE(18:0))LarodanCat#64-18001-palmitoyl-d31-2-oleoyl-sn-glycero-3- phosphocholine (PC(16:0/d$\frac{31}{18}$:1))Avanti Polar LipidsCat#8507572-diheptadecanoyl-sn-glycero-3- phosphoethanolamine (PE(17:$\frac{0}{17}$:0))Avanti Polar LipidsCat#830756N-heptadecanoyl-D-erythro- sphingosylphosphorylcholine (SM(d18:$\frac{1}{17}$:0))Avanti Polar LipidsCat#8605851-stearoyl-2-hydroxy-sn-glycero-3- phosphocholine (LPC(18:0))Avanti Polar LipidsCat#855775beta-Muricholic acidSteraloidsCat# C1895-000Chenodeoxycholic acidSigma-AldrichCat# C1050000Cholic acidSigma-AldrichCat# C2158000Deoxycholic acidSigma-AldrichCat# 700197PGlycochenodeoxycholic acidSigma-AldrichCat# 700266PGlycocholic acidSigma-AldrichCat# 700265PGlycodehydrocholic acidSteraloidsCat# C2020-000Glycodeoxycholic acidGlycocholic acidSigma-AldrichGlycohyocholic acidSteraloidsCat#C1860-000Glycohyodeoxycholic acidSteraloidsCat# C0867-000Glycolitocholic acidSigma-AldrichCat# 700268PGlycoursodeoxycholic acidSigma-AldrichCat# 06863Hyocholic acidSteraloidsCat# C1850-000Hyodeoxycholic acidSteraloidsCat# C0860-000Litocholic acidSigma-AldrichCat#700218Palpha-Muricholic 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acidBionordicaCat#20849[D4]- Glycolitocholic acidBionordicaCat#31308[D4]- Chenodeoxycholic acidBionordicaCat#20848[D4]- Deoxycholic acidBionordicaCat#20851[D4]- Litocholic acidCat#20831Valine-d8Sigma-AldrichCat#486027Glutamic acid-d5Sigma-AldrichCat# 631973Succinic acid-d4Sigma-AldrichCat# 293075Heptadecanoic acidSigma-AldrichCat# H3500Lactic acid-d3Sigma-AldrichCat# 616567Citric acid-d4Sigma-AldrichCat# 485438Arginine-d7Sigma-AldrichCat# 776408Tryptophan-d5Sigma-AldrichCat# 615862Glutamine-d5Sigma-AldrichCat# 616303
## Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact: Tuulia Hyötyläinen ([email protected]).
## Materials availability
This study did not generate new unique reagents.
## Method details
Cord serum samples from a All Babies In Southeast Swedecohort (ABIS) were extracted with two methods for separate extraction of lipids and polar/semipolar metabolites and the extracts were then analyzed using two methods using an ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (QTOFMS) and the data were processed using MZmine 2.5339as described below.
## Experimental model and subject details
ABIS is a general population prospective birth cohort designed to identify environmental and genetic factors associated with autoimmune diseases.27 A total of 1,435 ABIS infants had their HLA genotype sequenced. We selected children who later developed specific immune-mediated diseases, i.e., those subjects who later were diagnosed with either T1D, CD, IBD (Crohn's disease, Colitis ulcerosa), JIA or HT, and controls who remained healthy during the follow-up, matched for date of birth and sex (Table 1). The clinical parameters were similar across the different groups, with only birth weight showing significantly different values in those children who progressed to CD or HT later in life. The Swedish National Patient Register provided the diagnoses (https://www.socialstyrelsen.se/en/statistics-and-data/registers/national-patient-register/). CD diagnosis was determined only if the subjects had the diagnosis confirmed after their initial diagnosis. The gestational age, birth weight, or the type of delivery did not show statistically significant differences across the groups.
This study was performed in accordance with the Declaration of Helsinki. The ABIS study was approved by the Research Ethics Committees of the Faculty of Health Science at Linköping University, Sweden, $\frac{1997}{96}$,287 and $\frac{2003}{03}$–092 and the Medical Faculty of Lund University, Sweden.
## Lipidomics and metabolomics
A total of 360 cord blood samples were randomized and analyzed as described below.. Quantification was performed using calibration curves and the identification was done with a custom database, with identification levels 1 and 2, based on Metabolomics Standards Initiative. Quality control was performed by analysing pooled quality control samples. In addition, a reference standard (NIST 1950 reference plasma), extracted blank samples and standards were analyzed as part of the quality control procedure.
## Lipidomic analysis
A total of 360 cord blood samples were randomized and analyzed as described below. 10 μL of serum was mixed with 10 μL $0.9\%$ NaCl and extracted with 120 μL of CHCl3: MeOH (2:1, v/v) solvent mixture containing internal standard mixture ($c = 2.5$ μg/mL; 1,2-diheptadecanoyl-sn-glycero-3-phosphoethanolamine (PE(17:$\frac{0}{17}$:0)), N-heptadecanoyl-D-erythro-sphingosylphosphorylcholine (SM(d18:$\frac{1}{17}$:0)), N-heptadecanoyl-D-erythro-sphingosine (Cer(d18:$\frac{1}{17}$:0)), 1,2-diheptadecanoyl-sn-glycero-3-phosphocholine (PC(17:$\frac{0}{17}$:0)), 1-heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine (LPC(17:0)) and 1-palmitoyl-d31-2-oleoyl-sn-glycero-3-phosphocholine (PC(16:0/d$\frac{31}{18}$:1)) and, triheptadecanoylglycerol (TG(17:$\frac{0}{17}$:$\frac{0}{17}$:0)). The samples were vortexed and let stand on the ice for 30 min before centrifugation (9400 rcf, 3 min). 60 μL of the lower layer of was collected and diluted with 60 μL of CHCl3: MeOH. The samples were kept at −80°C until analysis.
The samples were analyzed using an ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOFMS from Agilent Technologies; Santa Clara, CA, USA). The analysis was carried out on an ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, particle size 1.7 μm) by Waters (Milford, USA). Quality control was performed throughout the dataset by including blanks, pure standard samples, extracted standard samples and control plasma samples. The eluent system consisted of (A) 10 mM NH4Ac in H2O and $0.1\%$ formic acid and (B) 10 mM NH4Ac in ACN: IPA (1:1) and $0.1\%$ formic acid. The gradient was as follows: 0–2 min, $35\%$ solvent B; 2–7 min, $80\%$ solvent B; 7–14 min $100\%$ solvent B. The flow rate was 0.4 mL/min.
Data were processed using MZmine 2.39 Mass spectrometry data processing was performed using the open source software package MZmine 2.53.40 The following steps were applied in this processing: (i) Mass detection with a noise level of 100, (ii) Chromatogram builder with a minimum time span of 0.08 min, minimum height of 1000 and an m/z tolerance of 0.006 m/z or 10.0 ppm, (iii) Chromatogram deconvolution using the local minimum search algorithm with a $70\%$ chromatographic threshold, 0.05 min minimum RT range, $5\%$ minimum relative height, 1200 minimum absolute height, a minimum ration of peak top/edge of 1.2 and a peak duration range of 0.08–5.0, (iv), Isotopic peak grouper with an m/z tolerance of 5.0 ppm, RT tolerance of 0.05 min, maximum charge of 2 and with the most intense isotope set as the representative isotope, (v) Join aligner with an m/z tolerance of 0.009 or 10.0 ppm and a weight for of 2, an RT tolerance of 0.15 min and a weight of 1 and with no requirement of charge state or ID and no comparison of isotope pattern, (vi) Peak list row filter with a minimum of $10\%$ of the samples (vii) Gap filling using the same RT and m/z range gap filler algorithm with an m/z tolerance of 0.009 m/z or 11.0 ppm, (vii) Identification of lipids using a custom database search with an m/z tolerance of 0.008 m/z or 8.0 ppm and an RT tolerance of 0.25 min. Identification of lipids was based on in house laboratory based on LC-MS/MS data on retention time and mass spectra. The identification was done with a custom database, with identification levels 1 and 2, i.e. based on authentic standard compounds (level 1) and based on MS/MS identification (level 2) based on Metabolomics Standards Initiative. Quality control was performed by analysing pooled quality control samples (with an aliquot pooled from each individual samples) together with the samples. In addition, a reference standard (NIST 1950 reference plasma), extracted blank samples and standards were analyzed as part of the quality control procedure.
## Analysis of polar metabolites
40 μL of serum sample was mixed with 90 μL of cold MeOH/H2O (1:1, v/v) containing the internal standard mixture (Valine-d8, Glutamic acid-d5, Succinic acid-d4, Heptadecanoic acid, Lactic acid-d3, Citric acid-d4. 3-Hydroxybutyric acid-d4, Arginine-d7, Tryptophan-d5, Glutamine-d5, each at at $c = 1$ μgmL-1 and 1D4-CA,1D4-CDCA,1D4-CDCA,1D4-GCA,1D4-GCDCA,1D4-GLCA,1D4-GUDCA,1D4-LCA,1D4-TCA, 1D4-UDCA, each at 0.2 1 μgmL-1) for protein precipitation. The tube was vortexed and ultrasonicated for 3 min, followed by centrifugation (10000 rpm, 5 min). After centrifuging, 90 μL of the upper layer of the solution was transferred to the LC vial and evaporated under the nitrogen gas to the dryness. After drying, the sample was reconstituted into 60 μL of MeOH: H2O (70:30).
Analyses were performed on an Agilent 1290 Infinity LC system coupled with 6545 Q-TOF MS interfaced with a dual jet stream electrospray (dual ESI) ion source (Agilent Technologies, Santa Clara, CA, USA) was used for the analysis. Aliquots of 10 μL of samples were injected into the Acquity UPLC BEH C18 2.1 mm × 100 mm, 1.7-μm column (Waters Corporation)), fitted with a C18 precolumn (Waters Corporation, Wexford, Ireland. The mobile phases consisted of (A) 2 mM NH4Ac in H2O: MeOH (7:3) and (B) 2 mM NH4Ac in MeOH. The flow rate was set at 0.4 mLmin-1 with the elution gradient as follows: 0–1.5 min, mobile phase B was increased from $5\%$ to $30\%$; 1.5–4.5 min, mobile phase B increased to $70\%$; 4.5–7.5 min, mobile phase B increased to $100\%$ and held for 5.5 min. A post-time of 5 min was used to regain the initial conditions for the next analysis. The total run time per sample was 20 min. The dual ESI ionization source was settings were as follows: capillary voltage was 4.5 kV, nozzle voltage 1500 V, N2 pressure in the nebulized was 21 psi and the N2 flow rate and temperature as sheath gas was 11 Lmin-1 and 379°C, respectively. In order to obtain accurate mass spectra in MS scan, the m/z range was set to 100–1700 in negative ion mode. MassHunter B.06.01 software (Agilent Technologies, Santa Clara, CA, USA) was used for all data acquisition.
## Quantification
Quantification of lipids was performed using a 7-point internal calibration curve (0.1–5 μg/mL) using the following lipid-class specific authentic standards: using 1-hexadecyl-2-(9Z-octadecenoyl)-sn-glycero-3-phosphocholine (PC(16:0e/18:1(9Z))), 1-(1Z-octadecenyl)-2-(9Z-octadecenoyl)-sn-glycero-3-phosphocholine (PC(18:0p/18:1(9Z))), 1-stearoyl-2-hydroxy-sn-glycero-3-phosphocholine (LPC(18:0)), 1-oleoyl-2-hydroxy-sn-glycero-3-phosphocholine (LPC(18:1)), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (PE(16:$\frac{0}{18}$:1)), 1-(1Z-octadecenyl)-2-docosahexaenoyl-sn-glycero-3-phosphocholine (PC(18:0p/22:6)) and 1-stearoyl-2-linoleoyl-sn-glycerol (DG(18:$\frac{0}{18}$:2)), 1-(9Z-octadecenoyl)-sn-glycero-3-phosphoethanolamine (LPE(18:1)), N-(9Z-octadecenoyl)-sphinganine (Cer(d18:$\frac{0}{18}$:1(9Z))), 1-hexadecyl-2-(9Z-octadecenoyl)-sn-glycero-3-phosphoethanolamine (PE(16:$\frac{0}{18}$:1)) from Avanti Polar Lipids, 1-Palmitoyl-2-Hydroxy-sn-Glycero-3-Phosphatidylcholine (LPC(16:0)), 1,2,3 trihexadecanoalglycerol (TG(16:$\frac{0}{16}$:$\frac{0}{16}$:0)), 1,2,3-trioctadecanoylglycerol (TG(18:$\frac{0}{18}$:$\frac{0}{18}$:)) and 3β-hydroxy-5-cholestene-3-stearate (ChoE(18:0)), 3β-Hydroxy-5-cholestene-3-linoleate (ChoE(18:2)) from Larodan, were prepared to the following concentration levels: 100, 500, 1000, 1500, 2000 and 2500 ng/mL (in CHCl3:MeOH, 2:1, v/v) including 1250 ng/mL of each internal standard.
Quantification of BAs was performed using a 7-point internal calibration curve using metabolites specified in key resources table. The identification was done with a custom data base, with identification levels 1 and 2, based on Metabolomics Standards Initiative. Quality control was performed by analysing pooled quality control samples (with an aliquot pooled from each individual samples) together with the samples. In addition, a reference standard (NIST 1950 reference plasma), extracted blank samples and standards were analysed as part of the quality control procedure.
## Statistical analyses
Missing values were replaced by half of the minimum value. Metabolites with a relative standard deviation >$30\%$ in pooled QC samples were removed from further analysis for unsatisfactory analytical robustness. The metabolomics data was scaled and logarithmic transformed prior the statistical analysis to ensure normal distribution of the data.
## Model-based metabolite clustering
Clustering of the ECs, lipidomic and metabolomics data obtained in this study was performed by using the ‘mclust’ R package (v.5.4.6). Mclust is a model-based clustering method, where the model performances are evaluated by the Bayesian Information Criterion (BIC). The models with the highest BICs were chosen.
## Linear regression analysis
Linear regression analysis using Limma available from MetaboAnalyst 5.0 was used to estimate mean differences between the control and individual disease groups and to identify differentially expressed metabolites.41,42 A two-sided t-test was performed to calculate p values for each metabolite and multiple testing correction using the Benjamini-Hochberg method was applied to control the false discovery rate (FDR). The log-fold change in expression (logFC) between the groups was also calculated using Limma. Metabolites with p values less than 0.05 and adjusted P-values less than 0.05 were considered significant and further analyzed. Heatmaps were used to show the fold changes in metabolite levels between control and individual disease groups, where the control group was used as the baseline for the heatmap.
## Pathway analysis
Pathway overrepresentation analysis was performed using the MetaboAnalyst 5.0 web platform using the Functional Analysis (MS Peaks)” module.41 For the input data for pathway analysis the complete high-resolution LC-MS spectral peak data obtained in negative ionization mode was used (mass tolerance of 10 ppm). A Welch’s t-test was performed to assess significant mean differences in the concentration of metabolites between cases and controls, and the whole input peak list with p values and T score was used for the pathway analysis. The relative significance of the overrepresented pathways against the background human scale metabolic model MNF (from MetaboAnalyst Mummichog package) and Kyoto Enzyclopedia of Genes and Genomes (KEGG) pathways [9] for Homo sapiens were estimated. The ‘Pathway Impact Scores’ were calculated by the metabolomics pathway analysis (MetPA) tool43 encoded in MetaboAnalyst 5.0.41,44
## Supplemental information
Document S1. Tables S3 and S4 Table S1. Spearman correlations between metabolites and lifestyle parameters, related to Figure 1 Table S2. Linear regression model for lipids, adjusted for birth weight, gestational age, and maternal age, related to Figure 2
## Data and code availability
•This paper does not report original code.•The metabolomics data reported in this paper will be shared by the lead contact upon request.•Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request and an appropriate institutional collaboration agreement. These data are not available to access in a repository owing to concern that the identity of patients might be revealed inadvertently.
## Author contributions
T.H., M.O., J.L., and E.W.T. designed and conceptualized the study. J.L. was responsible for the management of the clinical cohort study. T.H. and T.G. had a major role in the acquisition of the metabolomics data. T.H. and B.S.K. conducted statistical analysis. T.H. and M.O. provided significant statistical advice. T.H. and M.O. drafted the manuscript and verified the underlying data. All authors interpreted the data, reviewed and approved the manuscript for intellectual content.
## Declaration of interests
The authors have no conflict of interest to declare.
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|
---
title: Exercise-acclimated microbiota improves skeletal muscle metabolism via circulating
bile acid deconjugation
authors:
- Wataru Aoi
- Ryo Inoue
- Katsura Mizushima
- Akira Honda
- Marie Björnholm
- Tomohisa Takagi
- Yuji Naito
journal: iScience
year: 2023
pmcid: PMC10005909
doi: 10.1016/j.isci.2023.106251
license: CC BY 4.0
---
# Exercise-acclimated microbiota improves skeletal muscle metabolism via circulating bile acid deconjugation
## Summary
Habitual exercise alters the intestinal microbiota composition, which may mediate its systemic benefits. We examined whether transplanting fecal microbiota from trained mice improved skeletal muscle metabolism in high-fat diet (HFD)-fed mice. Fecal samples from sedentary and exercise-trained mice were gavage-fed to germ-free mice. After receiving fecal samples from trained donor mice for 1 week, recipient mice had elevated levels of AMP-activated protein kinase (AMPK) and insulin growth factor-1 in skeletal muscle. In plasma, bile acid (BA) deconjugation was found to be promoted in recipients transplanted with feces from trained donor mice; free-form BAs also induced more AMPK signaling and glucose uptake than tauro-conjugated BAs. The transplantation of exercise-acclimated fecal microbiota improved glucose tolerance after 8 weeks of HFD administration. Intestinal microbiota may mediate exercise-induced metabolic improvements in mice by modifying circulating BAs. Our findings provide insights into the prevention and treatment of metabolic diseases.
## Graphical abstract
## Highlights
•Habitual exercise modulates the composition of the gut microbiota•The modulation of microbiota ameliorates diet-induced metabolic disturbance•Deconjugated BAs activate AMPK signaling and glucose uptake in skeletal muscle•*Altered microbiota* improves metabolism by modifying circulating bile acids in mice
## Abstract
Musculoskeletal medicine; Microbiome; Microbial metabolism
## Introduction
Metabolic dysfunction is involved in the pathogenesis of several non-communicable diseases, including type 2 diabetes, cardiovascular diseases, and cancer. Physical inactivity, along with overeating and an unbalanced diet, results in pre-conditions of non-communicable diseases such as hyperglycemia and dyslipidemia.1,2 By contrast, habitual exercise reduces the risk of non-communicable diseases3,4 through the improvement of metabolic states including insulin sensitivity, mitochondrial respiration, and protein synthesis.5 Skeletal muscle is an important metabolic and exercise-responsive organ, responsible for respiratory mechanics, maintaining posture and balance in addition to protecting vital organs. Regulation of protein synthesis via growth factors/mTOR pathway and glucose uptake via insulin receptor/Akt and the AMP-activated kinase (AMPK) pathways are well known as crucial exercise-inducible signal transduction processes in skeletal muscle. Hence, metabolic improvement of skeletal muscle maintains glucose, protein, and energy homeostasis in the entire body. Exercise-responsive mechanisms that enhance skeletal muscle metabolism are beneficial for the maintenance of systemic metabolic states.
Accumulating evidence shows that microbiota regulates intestinal conditions, systemic immune functions, and metabolic systems of the host. The gut microbiota is formed from bacteria colonizing the guts of animals and humans, consisting of approximately 300 trillion bacteria from more than 1,000 species. Dysbiosis of gut microbiota causes metabolic dysfunction in the host, including obesity, insulin resistance, and dyslipidemia.6,7 In reverse, daily physical activity modulates the microbiota profile whereby cross-sectional studies indicate that subjects who perform physical activity have more bacteria associated with metabolism and immune functions than subjects who led a sedentary lifestyle.8,9 Exercise intervention also beneficially changes the microbiota composition, suggesting immune and metabolic regulation in humans and animals.9,10 In addition, previous reports show that modulation of exercise-induced microbiota contributes to reduced pathogenic bacterial communities and increased bacteria that produce beneficial metabolites such as short-chain fatty acids, butyrate, and antioxidants.11 Hence, the microbiota composition may be closely involved in exercise-induced metabolic benefits. Nevertheless, the association between exercise-induced metabolic improvement and microbiota changes remains unknown.
Skeletal muscle metabolism is influenced by various hormones, cytokines, and nutrients. Moreover, metabolites secreted from the intestine into the circulation affect muscle metabolic functions as proven in leaky gut and dysbiotic conditions whereby typical gut-derived metabolites and endotoxins are regularly elevated, leading to low-grade inflammation and metabolic dysfunction.12,13,14 Given that exercise-induced microbiota change is more established in diet-induced obese mice and humans,15,16 this may prevent inflammation and metabolic dysfunction in skeletal muscle. Nevertheless, improvements in microbiota-derived muscle metabolism due to habitual exercise remain unclear. Here, we hypothesized that exercise-acclimated microbiota may ameliorate muscle metabolism by affecting circulating metabolites. To test this hypothesis, comprehensive information on blood metabolites and skeletal muscle mRNA expression in recipients was essential, which in turn enabled our discovery on the unique mechanisms and microbiota functions that support the communication between muscle and gut. We further postulate that our findings may guide the development of potential strategies for the prevention and treatment of metabolic diseases. Herein, we examined the effect of transplanting fecal microbiota from exercise-trained mice into diet-induced obese mice, on the circulating metabolite profile and skeletal muscle metabolism.
## Microbiota modulation in donor and recipient mice by habitual exercise
In germ-free mice, the first exposure to microbiota dominantly affects its composition.17 Thereafter, continuous exposure further establishes the composition even in conventional housing, as described in a previous study.18 Thus, we examined the microbiota profile obtained from donor-trained (DT) and donor-sedentary (DS) mice as well as their recipient germ-free mice. We found 21 dominant genera in the microbiota profile from donor and recipient mice (relative abundance >$2\%$ in at least one group) (Figure 1A). The dominant genera were propagated from donors to recipients at week 1 and 8 in both sedentary and training conditions except for three unclassified genera belonging to the families Porphyromonadaceae, Paludibacter, and Akkermansia that were undetected in recipient mice. Chao1 index (amplicon sequence variant [ASV] richness estimation) and Shannon index (ASV evenness estimation) were used to compare the α-diversities and were found unaltered between DT and DS (Figures 1B and 1C). These indices were also similar between the recipient-trained (RT) and the recipient-sedentary (RS) mice (Figures 1B and 1C).Figure 1Microbiota profile in donor and recipient miceThe relative abundance of genera in fecal microbiota profiles from donor and at weeks 1 and 8 of recipient mice (abundant >$2\%$ amplicon sequence variant [ASV] in at least one group) ($$n = 6$$–8, A). The α-diversity (Chao1 index [$$n = 6$$–8, B], ASV richness estimation and the Shannon index [$$n = 6$$–8, C], ASV evenness estimation) between the donor and recipient groups. The relative abundance of the genera that were increased (D) and decreased (E) by training in donor and at week 1 of recipient mice ($$n = 6$$–8). DS; sedentary donor, DT; trained donor, RS1; week 1-recipient from DS, RT1; week 1-recipient from DT, RS8; week 8-recipient from DS, RT8; week 8-recipient from DT. # $p \leq 0.1$ and ∗$p \leq 0.05.$ Results are presented as means ± SE.
Our comparison between groups showed that a proportion of the three microbial genera showed more abundant trend, while a downward trend was observed in seven other genera ($p \leq 0.1$) (Table S1) in the DT group than in the DS. In the RT group, a proportion of seven genera showed a more abundant trend after 1 week than the RS group (Table S2). In contrast, at 1 week of fecal microbiota transplantation (FMT), there were 45 genera lower trend in abundance in RT mice (Table S2). At 8 weeks of FMT, a proportion of 11 genera were more abundant trend in RT mice than RS mice, while seven other genera were less abundant trend (Table S3). Within the dominant genera altered by training, two higher genera were commonly found in both donor and week 1-recipient mice: Lactobacillus ($p \leq 0.05$) and Lactococcus ($p \leq 0.1$) (Figure 1D, Tables S1 and S2). It is important to note that the two genera underwent more frequent alterations in their composition in DT and RT compared to DS and RS. Conversely, we indicate that the genus *Parabacteroides is* regularly decreased in DT ($p \leq 0.1$) and week-1 RT ($p \leq 0.05$) group (Figure 1E, Tables S1 and S2). Furthermore, at 1 week after FMT in RT group, we found a higher genus [Ruminococcus] belonging to the family Lachnospiraceae ($p \leq 0.05$) and two lower genera, an unclassified genus belonging to the family Ruminococcaceae in addition to an unclassified genus belonging to the order Bacteroidales ($p \leq 0.05$) (Figures 1D and 1E, Table S2).
## Ability of deconjugation of circulating BAs in recipient mice
Changes in the microbiota frequently affect various tissue functions in the host by impacting the circulating factors. Therefore, plasma metabolites obtained from recipient mice at 1 week after transplantation were analyzed using comprehensive metabolome analysis. In total, the levels of 22 factors were 1.5-fold higher in RT than in RS (Figure 2A, Table S4). Among them, cholic acid (CA), a representative primary bile acid (BA), showed the highest elevation. Hence, we focused our analysis on BAs and examined their profile. Figure 2Deconjugation of plasma BA increased upon transplantation of exercise-acclimated fecal microbiotaThe plasma metabolites elevated by exercise-acclimated fecal microbiota ($$n = 3$$, A) and plasma free-form BAs ($$n = 6$$, B–F), tauro-conjugated BAs ($$n = 6$$, G–K), the deconjugated ratio ($$n = 6$$, L), and fecal bile salt hydrolase activity ($$n = 6$$, M) in recipient mice at week-1. Positive correlation of the deconjugation ratio of BAs with the presence of certain microbiota propagated from donors and bile salt hydrolase activity ($$n = 6$$, N) are shown in red, and negative correlations are shown in blue. MNAM; Methylnicotinamide, 3-IS; 3-Indoxylsulfuric acid, PCr; Phosphocreatine, DCIPAHs; 1H-Imidazole-4-propionic acid, K3MVA; Methyl-2-oxovaleric acid, 2MHA; 2-methylhippuric acid, Isobutyryl-CAR; Isobutyrylcarnitine, S-Sulfo-Cys; S-Sulfocysteine, pGlu; Pyroglutamic acid, TMAO; Trimethylamine N-oxide, Glu; Glutamic acid, GABA-His; γ-aminobutyryl-histidine, GAA; Guanidoacetic acid, 2-OG; 2-Oxoglutaric acid, 4PY; N1-Methyl-4-pyridone-5-carboxamide, MelmAA; 1-Methyl-4-imidazoleacetic acid, RS; recipient from sedentary donor, RT; recipient from trained donor. # $p \leq 0.1$, ∗$p \leq 0.05$, and ∗∗$p \leq 0.01.$ Results are presented as means ± standard errors.
Comprehensive BA analysis revealed that the free forms of CA, chenodeoxycholic acid (CDCA), α-muricholic acid (αMCA), and ωMCA, were higher in RT than in RS (Figures 2B–2F). In contrast, the levels of tauro-conjugated forms were lower in RT than in RS, except for ωMCA and βMCA (Figures 2G–2K). To examine BA conversion between the free and conjugated forms, we calculated the product/(product + substrate) to obtain the ratio of deconjugated BAs. We found a higher deconjugated ratio in RT than in RS for all examined BAs except for βMCA (CA, $$p \leq 0.059$$; CDCA, $p \leq 0.01$; ωMCA, $p \leq 0.01$; αMCA, $p \leq 0.01$) (Figure 2L), concomitant with a higher trend in bile salt hydrolase activity in the feces ($p \leq 0.1$) (Figure 2M). In addition, we found the deconjugated ratio to be positively correlated with the presence of the three more abundant genera in DT and RT at 1 week after FMT and negatively correlated with the three less abundant genera in DT and RT (Figure 2N). Bile salt hydrolase activity was also positively correlated with the presence of the three more abundant genera and showed a negative correlation with the abundance of an unclassified genus belonging to the order Bacteroidales (Figure 2N).
## Activation of metabolic signaling in skeletal muscle from recipient mice
A typical adaptive change of habitual exercise is the metabolic improvement of the skeletal muscle.5 Hence, we examined the metabolic factors in the skeletal muscle of recipient mice using transcriptome analysis. Microarray analysis revealed that the expression of 3,450 genes was higher and that of 3,765 genes was lower in the gastrocnemius muscle of week 1-RT (Figure 3A). Of the upregulated genes, 440 were related to energy metabolic process, including “glucose metabolism”, “carbohydrate metabolism”, “lipid metabolism”, “glycerol metabolism”, “ATP catabolism”, “tricarboxylic acid cycle”, and “oxide-reduction process” as per the Gene Ontology Biological Process Term analysis. Ingenuity canonical pathway analysis revealed that AMPK and insulin growth factor-1 (IGF-1) signaling were highly ranked in pathways activated by FMT from DT (Figures 3A, S1, and S2). We also found positive correlations between factors related to AMPK and IGF-1 signaling and the deconjugated ratios, with higher levels present in RT mice (Figure 3G). Based on these findings, we next determined whether FMT altered the key factors related to those signaling pathways in skeletal muscle. AMPKαThr172 phosphorylation was increased in the skeletal muscles of RT ($p \leq 0.05$) (Figure 3B). In addition, the phosphorylation of CaMKIIThr286, an upstream regulator of AMPK, was increased ($p \leq 0.01$) in RT mice (Figure 3C). Conversely, phosphorylation of liver kinase B1 (LKB1)Ser428, another upstream regulator, was unaltered by FMT from DT (Figure 3D). Phosphorylation of AS160Thr642, a downstream factor of AMPK, was also higher in RT ($p \leq 0.05$) (Figure 3E). A network analysis of the transcriptome, plasma BAs, and the abundance of bacterial genera revealed that Lactobacillus, Lactococcus, and [Ruminococcus] affected AMPK signaling factors in the BA-dependent or independent routes (Figure S3). IGF-1 levels also trended higher in the RT mice ($$p \leq 0.058$$) (Figure 3F).Figure 3Upregulation of AMPK and IGF-1 in skeletal muscle by the transplantation of exercise-acclimated fecal microbiotaA scatterplot of probes with significant differences ($p \leq 0.05$, FDR <0.1) between both the recipient mice at week-1 ($$n = 6$$, A). mRNA levels of 3,450 genes were significantly higher in recipient mice transplanted with exercise-acclimated fecal microbiota than those in mice transplanted from sedentary donors. Of these, 440 genes were categorized in energy metabolic processes, including “glucose metabolic process,” “carbohydrate metabolic process,” “lipid metabolic process,” “glycerol metabolic process,” “ATP catabolism,” “tricarboxylic acid cycle,” and “oxidation-reduction process.” Ingenuity canonical pathway analysis indicated that AMPK and IGF-1 signals were positively regulated by FMT from trained donors. AMPKαThr172 (B), CaMKIIThr286 (C), LKB1Ser428 (D), and AS160Thr642 (E) phosphorylation and IGF-1 content (F) in gastrocnemius muscle from recipient mice ($$n = 5$$–6). The correlation of plasma BA profile with muscle metabolic factor level in recipients is shown ($$n = 6$$, G). High levels of important factors related to AMPK and IGF-1 signaling in the skeletal muscle of recipient mice in transcriptome analysis correlates with free-, tauro-BAs, and deconjugation ratios of BAs. Positive correlations are shown in red, and negative correlations are shown in blue. The correlation coefficient was calculated using Spearman’s correlation analysis. Phosphorylation levels were correlated with total content of each target in immunoblotting. RS; recipient from sedentary donor, RT; recipient from trained donor. # $p \leq 0.1$, ∗$p \leq 0.05$, and ∗∗$p \leq 0.01.$ Results are presented as means ± SE.
AMPK is a major exercise-inducible metabolic regulator in skeletal muscle. To examine the relationship between muscle AMPK activity and the intestinal microbiota, the microbiota profile of AMPKγ3-knockout mice was obtained, since AMPKγ3 is predominantly expressed in skeletal muscle.19 At the genus level, AMPKγ3-knockout mice showed a trend toward lower abundance for four genera, namely [Ruminococcus]and unclassified genera belonging to the family Lachnospiraceae, Clostridium, and Butyricicoccus compared to wild-type mice ($p \leq 0.1$) (Figure S4). Although the profile did not match recipient mice, the changes in [Ruminococcus] and the unclassified genus belonging to the family Lachnospiraceae were opposite to those in RT mice. In contrast, higher abundance of genera in AMPKγ3-knockout mice was not found.
## Different cholic acid forms alter inflammatory and metabolic responses in cultured myotubes and muscle tissues
The effect of free and tauro-conjugated αMCAs, the highest deconjugated BA, was examined in a palmitate-induced insulin-resistant culture experiment model that showed lower insulin signaling and higher expression of inflammatory factors in C2C12 myotubes (Figures S5A–S5D). Glucose uptake was elevated by treatment with free-form αMCA in the presence of palmitic acids ($p \leq 0.05$) (Figure 4A). Along with higher content of glucose transporter 4 (GLUT4) in the membrane fraction (Figure 4B), free-form αMCA treatment also increased the AMPKThr172 levels ($p \leq 0.05$) (Figure 4C). By contrast, elevated glucose uptake and AMPK activation were not observed following treatment with tauro-conjugated αMCA, which is supporting evidence for a direct effect of BAs. In our experiment with different content ratios of the two BA forms, the medium with higher free-form also showed higher activation of AMPK signaling than the medium with increased tauro-conjugated form (Figure S6A and S6B). However, the IGF-1 levels were comparable between the media with free and conjugated BA forms (Figure S7).Figure 4Different forms of BA modulate metabolic and inflammatory responses in cultured myotubes and muscle tissuesGlucose uptake in C2C12 myotubes incubated in the absence or presence of αMCA and tauro α-muricholic acid (TαMCA) (10 μM) with palmitic acid (200 μM) for 24 h or incubated with or without sotrastaurin (AEB071) (2 nM) ($$n = 5$$–8, A and J). The membrane content of glucose transporter 4 (GLUT4) ($$n = 4$$, B) and AMPKαThr172 phosphorylation ($$n = 7$$, C) in C2C12 myotubes incubated in the absence or presence of MCA and TMCA. mRNA levels of CCL-2, CXCL-1, and Tlr-4 ($$n = 5$$–6, F–H, K) with or without AEB071in myotubes, and levels of CCL-2 in the media ($$n = 8$$, I) in the absence or presence of αMCA and TαMCA. mRNA levels of TNF-α, IL1-β, Tlr-4, CXCL-1, and CCL-2, and F$\frac{4}{80}$ ($$n = 6$$, D) and CCL-2 protein level ($$n = 6$$, E) in gastrocnemius muscles from recipient mice at week-1. Phosphorylation levels were correlated with the total content of each target in immunoblotting. RS; recipient from sedentary donor, RT; recipient from trained donor. # $p \leq 0.1$ and ∗$p \leq 0.05$ between groups. Results are presented as the mean ± SE.
Inflammatory factors are involved in metabolic impairment. Typical inflammatory factors in muscle tissues were measured to examine the mechanism of BA deconjugation on metabolic signaling activation. We found lower expression levels of chemokine (C-C motif) ligand 2 (CCL-2) ($p \leq 0.05$) and F$\frac{4}{80}$ ($$p \leq 0.067$$) in the muscle tissues of RT at 1 week after FMT (Figures 4D and 4E), suggesting suppressed macrophage infiltration, a marker of low-grade inflammation. In cultured myotubes, the mRNA expression of inflammatory factors was higher upon treatment with tauro-conjugated BA than with the free-form ($p \leq 0.05$) (Figures 4F–4H) whereby CCL-2 concentration in media was also higher ($p \leq 0.05$) (Figure 4I). Sotrastaurin, an inhibitor of protein kinase C-theta (PKCθ), which is a possible factor of the inflammatory pathway in response to BA, prevented the effects of tauro-conjugated form on glucose uptake and CCL-2 levels ($p \leq 0.05$) (Figures 4J and 4K).
## Transplanting exercise-induced microbiota improves glucose metabolism in high-fat diet-induced obese mice
The effects of FMT on glucose metabolism were determined in high-fat diet (HFD)-induced obese mice. After taking HFD, body weight, blood glucose, and plasma insulin were gradually increased over 8 weeks (Figures S8A–S8C), in addition to decreased level of phopho-Akt in gastrocnemius muscle (Figure S8D). Body weight did not change between recipient groups after taking HFD for 8 weeks (Figure 5A). Although the gastrocnemius muscle weight was unaltered, RT mice showed lower epididymal fat weight ($p \leq 0.05$) (Figures 5B and 5C), concomitant with lower plasma glucose ($p \leq 0.05$) and unaltered insulin levels (Figures 5D and 5E). Blood glucose levels during oral glucose tolerance test (GTT) were lower in RT mice (30 min and 60 min, $p \leq 0.01$) (Figure 5F). Insulin tolerance test (ITT) showed gradual decrease in blood glucose levels after insulin injection, and the relative decrease rate was larger in RT mice (30 min, $p \leq 0.05$) (Figure 5G). These improvements in metabolic parameters in HFD-induced obese mice correlated with enhanced glycogen content, mRNA expressions of peroxisome proliferator-activated receptor gamma coactivator-1α (PGC1α), cytochrome c oxidase 4-1 (COX4-1), and AMPKαThr172, acetyl-CoA carboxylase (ACC)Ser212 phosphorylation, and membrane content of GLUT4, as well as COX activity in the gastrocnemius muscle of RT mice compared to the RS group ($p \leq 0.05$) (Figures 5H–5N).Figure 5Exercise-acclimated microbiota treatment improves glucose tolerance in HFD-fed mice(AN) Body and tissue weights ($$n = 8$$, A–C), blood chemistry ($$n = 8$$, D and E), blood glucose concentrations during oral glucose tolerance tests ($$n = 8$$, GTT) (F) and insulin tolerance tests (ITT) ($$n = 8$$, G), glycogen content ($$n = 7$$–8, H), mRNA levels of PGC1α and COX4-1 ($$n = 8$$, I and J), AMPKαThr172 ($$n = 7$$, K) and ACCSer212 ($$n = 6$$–7, L) phosphorylation, membrane content of GLUT4 ($$n = 7$$, M), and COX activity ($$n = 8$$, N) in gastrocnemius muscle in recipient mice fed chow or HFD for 8 weeks. Phosphorylation levels were correlated with total content of each target in immunoblotting (K–M). The solid line shows absolute values, and the dotted line shows relative values in the ITT (G). RS; recipient from sedentary donor, RT; recipient from trained donor. ∗$p \leq 0.05$, and ∗∗$p \leq 0.01$ between groups. Results are presented as the mean ± SE.
## Discussion
In this study, we focused on the effect of gut microbiota transferred from trained to recipient mice via FMT, on the development of metabolic dysfunction induced by HFD. Given the characteristics of the mouse model, bacterial and molecular changes in the early phase of HFD feeding may be involved in glucose intolerance and obesity in later phases. Hence, we conducted transcriptome and metabolome analysis at 1 week of FMT and examined phenotypic parameters at week 8. We found that recipient mice transplanted with gut bacteria from DT mice showed reduced HFD-induced glucose intolerance. In the recipient mice, the glucose metabolic signaling pathway in skeletal muscle was activated with a higher ability of deconjugation of BA, which is involved in metabolic and anti-inflammatory responses. These results suggest that exercise-induced gut microbial modulation contributes to improved glucose metabolism in skeletal muscle through BA deconjugation. Furthermore, our findings confirm that bacterial composition in the gut influences the organ functions throughout the body.
Cross-sectional and interventional studies in animals and humans show that habitual exercise alters the composition of the intestinal microbiota.8,9 However, it is unclear whether microbiota alterations contribute to exercise-induced metabolic improvements. In the present study, we observed an increase in the abundance of the common bacterial genera Lactobacillus and Lactococcus in both DT and RT mice. These microbes are frequently more abundant in exercise habituation20,21 and less abundant in metabolic disorders in animals and humans22,23 whereby dietary supplementation with Lactobacillus and Lactococcus improves glucose tolerance.24,25 Conversely, we report a regular decrease of the genus Parabacteroides that is commonly elevated in obesity as well as in metabolic dysfunction22,23 and is decreased by exercise.26 In addition to these commonly altered genera, 44 microbial species showed lower presence, while 5 were higher in RT mice. FMT to germ-free mice is a typical model used to promote the propagation of microbes and evaluate their function; however, recipients do not yield an entire similar microbiota profile as the donor. Furthermore, HFDs may also interfere with the propagation of microbiota in the recipients as it is established that a daily HFD causes dysbiosis, associated with metabolic dysfunction.27 By contrast, the metabolic benefits of exercise were better obtained in the HFD group than in the normal-diet group.28 Therefore, exercise-induced changes in these genera may contribute to metabolic improvement under HFD conditions.
One of the crucial benefit of habitual exercise is improved metabolic capacity, such as insulin sensitivity and mitochondrial oxidation.5 Because skeletal muscle is the principal site for blood glucose disposal, it is a practical target in therapies to prevent and treat obesity and type 2 diabetes. In the present study, we found that various metabolic activators, including enzymes, transcription factors, and myokines, were upregulated in the skeletal muscle in RT mice. Particularly, AMPK signaling was activated following FMT from DT. AMPK is a major sensor of intracellular energy demand that stimulates glucose uptake and mitochondrial biogenesis.29 It activates insulin-independent glucose uptake and lipid oxidation in skeletal muscle in response to exercise and muscle contraction.29,30,31 In contrast, it has been suggested that a permanent reduction in the AMPK pathway leads to insulin resistance32 and contributes to muscle dysfunction in diet-induced obesity.33 Our results showed that phospho-AMPK and phospho-AS160 levels were elevated in the skeletal muscle of the RT mice. In addition, because one of the primary insulin target organs is skeletal muscle, our GTT and ITT results support a beneficial improvement in skeletal muscle glucose uptake. Hence, these findings indicate that the alterations in metabolic functions as a result of FMT involve AMPK signaling in skeletal muscle. Although the genera affected by AMPKγ3 knockout did not necessarily match the genera that were influenced by FMT, the lower genus [Ruminococcus] in AMPKγ3-knockout mice corresponds to the higher result in RT mice. However, because the whole composition rather than changes in individual bacteria affects the host, further studies are needed in order to corroborate present results and confirm whether the exercise-induced change in microbiota can be induced by AMPK activation in skeletal muscle.
While AMPK phosphorylation is reduced in skeletal muscle derived from animal models of obesity or type 2 diabetes,34 humans with type 2 diabetes retain AMPK activation in response to exercise or cellular stress.35 Thus, agents that mimic the effects of exercise on AMPK activation may improve insulin sensitivity of skeletal muscle in metabolic diseases. Our results show that gut microbiota from trained mice prevents metabolic disruption in recipient mice under HFD conditions. Therefore, long-term FMT treatment may enhance skeletal muscle insulin sensitivity via AMPK activation, thereby preventing the development of type 2 diabetes. Furthermore, our results suggest that gut microbiota are involved in improved skeletal muscle metabolism during exercise training, which partly explains the health-promoting effects of physical activity in the prevention of peripheral insulin resistance. Because the elevated glycogen content leads to the enhancement of endurance capacity,36,37 the exercise-acclimated microbiota may also have a potential benefit for athletic performance. However, the main objective of this study was to examine the effect of FMT on diet-induced metabolic dysfunction; thus, appropriate experiment protocol was not set up to assess endurance.
Several microbiota-produced metabolites play critical roles in immune and metabolic functions,12,38 namely, short-chain fatty acids, trimethylamine, amino acids, hydrogen peroxides, and lactate,12,38,39,40 which are released into the circulation. In metabolome analysis of circulating metabolites, we found that CA was the maximum enhanced factor in the RT group compared to the RS group. Primary BAs such as CA and CDCA are generated and conjugated with glycine or taurine (Glyco-CA, Glyco-CDCA, Tauro-CA, Tauro-CDCA) in the liver. These primary BAs are exported into the bile, where glycine and taurine are deconjugated by specific bacteria that express bile salt hydrolases, such as Lactobacillus and Bifidobacterium.41 In mice, MCAs are the primary BAs that are conjugated with glycine and taurine and deconjugated by bacteria. Accumulating evidence suggests that BAs can regulate nutrient metabolism by regulating the activation of the BA-specific receptors, farnesoid X receptor and transmembrane G protein-coupled receptor (TGR)-5, in metabolic tissues.42 We found that plasma taurine-conjugated BA levels were decreased and free-form BA levels were increased in HFD-fed RT mice, which was confirmed by a higher deconjugation ratio. Incidentally, the deconjugation ratio showed a positive correlation with increased bacterial genera in both DT and RT mice, supporting the relationship between microbiota and circulating modifications in BAs, in particular, the genus Lactobacillus as it has a high activity of bile salt hydrolase.41,43 A previous study also showed that HFDs increase the total level of taurine-conjugated BAs and decrease the level of free BAs.44 Taurine-conjugated BAs have been suggested to induce higher inflammatory responses than free BAs in adipocytes, which is associated with increased CCL-2 expression.45 Therefore, exercise-induced microbiota changes suppressed metabolic dysfunction caused by HFDs, which may be partly mediated by deconjugated BAs. In cultured myotubes, we found that free MCA treatment activated glucose uptake and AMPK signaling more than the treatment with tauro-conjugated MCA. Network analysis also supported the relationship between the microbiota, BA deconjugation, and AMPK signaling. βMCA did not show a higher deconjugation ratio in the RT mice or a correlation of the deconjugation ratio with metabolic activators in skeletal muscle. In contrast to other MCAs, it has been reported that the tauro-conjugation form of βMCA protects against tissue damage and induces metabolic improvement.46 Hence, the higher tauro-conjugated form of βMCA may contribute to metabolic activation in the muscle.
Recently, several studies have examined exercise effects using the FMT approach. Zoll et al.47 showed that the metabolic-disrupting effect of high-fat and high-sugar diet was propagated from donors to recipients fed with normal diet weekly by FMT. However, this did not elucidate the benefits of exercise training. In contrast, Lai et al.48 reported the transmission of training-induced metabolic improvement in donors by higher frequency (5 times/week) FMT on recipients fed with HFD after bacterial elimination with antibiotic treatment. This result indicated that exercise-acclimated microbiota can contribute to improved metabolic dysfunction under HFD conditions. Meanwhile, the present study showed that even lower frequency (twice weekly) of FMT to germ-free mice fed with HFD successfully achieved the advantageous metabolic effects. Germ-free conditions in the initial state, diet, and FMT method may have led to a more efficient transmission. Furthermore, the training program and the type of donor mice may also affect the gut microbiota profile and efficient transmission to the recipients. In particular, Institution of Cancer Research (ICR) mice can perform high-intensity exercise regime better compared to B6J and Balb/c mice; hence, sufficient training load may have also influenced the results.
Various inflammatory factors impair glucose metabolism in skeletal muscle. Animal models and patients with type 2 diabetes show higher inflammatory cytokine levels,49 and chronic low-grade inflammation causes insulin resistance.50 Our results showed a lower expression of CCL-2 and F$\frac{4}{80}$, a macrophage infiltration marker, in the RT mice in the early period of FMT, i.e., the pre-stage onset of glucose intolerance. Furthermore, tauro-conjugated BA treatment also resulted in more CCL-2 expression and secretion than treatment with free-form BA in cultured myotubes. CCL-2 suppresses macrophage-dependent and independent glucose uptake.49,51 Therefore, these observations suggest that CCL-2 mediates taurocholic acid-induced suppression of glucose uptake via an inflammatory response. Although we observed the association of deconjugation with the abundance of bacterial genera, a detailed analysis with bacterial species class would have provided more insights.
Generally, circulating BAs act as signaling factors through farnesoid X receptor and TGR-5. Particularly in skeletal muscle, TGR-5 mediates signal activation in protein anabolism and energy utilization.52,53 BA composition does not lead to a large difference in TGR-5-mediated metabolic signaling.52,53 However, the present results are not necessarily limited to TGR-5-mediated effects but rather suggest that the induction of inflammatory factors such as CCL2 attenuates glucose metabolic signals. Given that CCL2 is induced by conjugated BA in adipocytes,45 ability to deconjugate may contribute to metabolic improvement via attenuated inflammatory responses. BAs can directly disrupt the plasma membrane and activate the protein kinase C (PKC) pathway, which results in inflammatory responses.54,55 In skeletal muscle, inflammatory responses are mediated by activated PKCθ.56,57 We found that pretreatment with PKCθ inhibitor prevented taurocholic acid-induced suppression of glucose metabolism and elevated CCL-2. Collectively, our findings suggest that exercise-acclimated microbiota improves glucose tolerance by suppressing the tauro-conjugated BA-induced inflammatory response. BAs can regulate intracellular calcium levels.58 Because AMPK phosphorylation is activated by calcium-dependent signals,59 intracellular calcium may also be involved in metabolic improvement. By contrast, IGF-1 signaling, another predicted signaling pathway activated by FMT, did not change between free and conjugated BAs. As shown in a previous study,52 this signaling is activated through TGR-5, which occurs via both free and conjugated BAs. Although the different responses of IGF-1 observed between RS and RT are unclear, we speculate that other microbiota-responsive factors may have mediated this effect. However, further studies employing various conditions in ratios, concentrations, and species of BAs would be required to provide more insight. Apart from BAs, other metabolite levels can be increased by transplanting microbiota from DT, which in turn may have several metabolic benefits and other multiple effects. In addition to short-chain fatty acids and organic acids suggested in previous studies,38,40 metabolites found in the present metabolome analysis may be potential factors. As this study only demonstrated the effect of BAs as a contributing factor, more research on the mechanism of metabolic action in skeletal muscle is necessary to fully determine the effect of exercise-induced microbiota changes. Furthermore, it is necessary to confirm the potential application of these findings in human studies. In addition to the concept of fecal transplantation from trained subjects, the consumption of probiotics with high bile salt hydrolases activity may provide metabolic benefits.
In conclusion, microbiota transplantation of feces from trained mice plays a role in whole-body glucose tolerance, concomitant with AMPK activation in skeletal muscle. These effects are mediated by anti-inflammatory properties of BA deconjugation. Microbiota transplantation improves glucose homeostasis and insulin sensitivity in HFD-induced obese mice, which highlights the metabolic benefits obtained by habitual exercise and FMT application in the management of metabolic disease. Thus, we concluded that microbiota mimics the health-promoting effects of exercise by improving glucose metabolism in skeletal muscle through the “muscle and gut axis.”
## Limitations of the study
Although our study provides a crucial link between the influence of exercise on microbiota and in turn its effect on the metabolic functions of skeletal muscles, there are a few limitations. As mentioned previously, the effects of the whole composition of the microbiota on metabolism could be much broader than those observed upon altering a small proportion. Therefore, further studies are requried to address this on a larger scale. Moreover, although we have found the correlation between AMPK signaling and the abundance of certain bacterial genera, further studies are required to establish a mechanistic link between the two. More importantly, based on the metabolome analysis, we focused our study on the BAs. Further experiments are required to elucidate the mechanistic details of microbiota-mediated metabolic regulation in the skeletal muscle. Furthermore, the effect of other microbiota-released metabolites must also be studied in this context.
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERAntibodiesRabbit polyclonal anti-phospho AMPKα (Thr172)Cell Signaling TechnologyCat#2531S; RRDI: AB_330330Rabbit polyclonal anti-AMPKαCell Signaling TechnologyCat#2532S; RRID: AB_330331Rabbit polyclonal anti-phospho ACC (Ser212)Cell Signaling TechnologyCat#3661S; RRID: AB_330337Rabbit polyclonal anti-ACCCell Signaling TechnologyCat#3662S; RRID: AB_2219400Rabbit polyclonal anti-phospho Akt (Ser473)Cell Signaling TechnologyCat#9271S; RRID: AB_329825Rabbit polyclonal anti-AktCell Signaling TechnologyCat#9272S; RRID: AB_329827Rabbit polyclonal anti-phospho Akt substrate (Ser/Thr)Cell Signaling TechnologyCat#9611S; RRID: AB_330302Rabbit polyclonal anti-phospho AS160 (Thr642)Cell Signaling TechnologyCat#8881S; RRID: AB_2651042Rabbit polyclonal anti-AS160Cell Signaling TechnologyCat#2670S; RRID: AB_2199375Rabbit polyclonal anti-phospho CaMKII (Thr286)Cell Signaling TechnologyCat#12716S; RRID: AB_2713889Rabbit polyclonal anti-CaMKIICell Signaling TechnologyCat#4436S; RRID: AB_10545451Rabbit polyclonal anti-phospho LKB1 (Ser428)Cell Signaling TechnologyCat#3482S; RRID: AB_2198321Rabbit polyclonal anti-LKB1Cell Signaling TechnologyCat#3047S; RRID: AB_2198327Rabbit polyclonal Anti-GLUT-4 C-terminusMerck MilliporeCat#07-1404; RRID: AB_1587080Mouse monoclonal Anti-GAPDHAbcamCat#ab8245; RRID: AB_2107448Biological samplesMouse skeletal muscleThis paperN/AMouse bloodThis paperN/AMouse stoolThis paperN/AChemicals, peptides, and recombinant proteinsαMCACaymanCat#20291Tauro-αMCACaymanCat#20288Tauro-αMCAToronto ResearchCat#T009130SotrastaurinSelleckCat#S2791Critical commercial assaysMouse MCP-1/CCL2 ELISA kitSigmaCat#RAB0055-1KTMouse IGF-1 ELISA kitProtein techCat#KE10032Mouse Insulin ELISA kitMercodiaCat#10-1247-01Glucose Uptake-Glo AssayPromegaCat#J1341Proteoxtra Transmembrane Protein Extraction KitNovagenCat#71772-3CNF-kit glucoseRoche DiagnosticsCat# 716251ATaqman Gene Expression Assay CCL-2Thermo Fisher ScientificID#Mm00441242_m1Taqman Gene Expression Assay CXCL-1Thermo Fisher ScientificID#Mm04207460_m1Taqman Gene Expression Assay TLR-4Thermo Fisher ScientificID#Mm00445273_m1Taqman Gene Expression Assay TNF-αThermo Fisher ScientificID#Mm00443258_m1Taqman Gene Expression Assay IL-1βThermo Fisher ScientificID#Mm00434228_m1Taqman Gene Expression Assay F$\frac{4}{80}$Thermo Fisher ScientificID#Mm00802529_m1Taqman Gene Expression Assay COX4-1Thermo Fisher ScientificID#Mm01250094_m1Taqman Gene Expression Assay PGC1-αThermo Fisher ScientificID#Mm01208835_m1Taqman Gene Expression Assay β-actinThermo Fisher ScientificID#Mm 00607939_s1Deposited dataRaw and analyzed dataThis paperGEO: GSE201202Experimental models: Cell linesMouse: C2C12 cellsECACat#EC91031101-F0Software and algorithmsQIIME2Bolyen et al., 201960Version 2020.8CytoscapeShannon et al., 200361Version3.8.2Transcriptome Analysis Console SoftwareAffymetrixVersion 4.0Ingenuity Pathway AnalysisQIAGENVersion 76765844ImageJSchneider et al., 201262https://imagej.nih.gov/ij/OtherMiSeqIlluminaCat#SY-410-1003Agilent time-of-flight mass spectrometerAgilent TechnologiesID#6210SpectraMax microplate readerMolecular DevicesSpectraMac i3xStepOne Plus Real-Time PCR systemLife TechnologiesCat#4376598Bond Elut C18 cartridgeAgilent TechnologiesCat#12102025
## Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Wataru Aoi ([email protected]).
## Materials availability
This study did not generate unique new reagents or mouse lines.
## Animal studies
Animal studies were performed according to the guidelines of the Japanese Council on Animal Care and the Regulations and General Advice of Laboratory Animals of the Swedish Board of Agriculture. All animal experiments were approved by the Committees for Animal Research of Kyoto Prefectural University, Kyoto Prefectural University of Medicine (KPU20200402-R, M25-39) (Kyoto, Japan), and by the Stockholm Ethical Committee (Stockholm, Sweden).
Male Institution of Cancer Research (ICR) mice were used as donors to obtain feces. Mice were acclimatized in an air-conditioned (22 ± 2°C) room with a 12:12 h light–dark cycle. The mice in the exercise group ran on a motorized treadmill five times per week for four weeks. During this period, the level of exercise was gradually increased from running for 20 min at 18 m·min−1 to 60 min at 30 m·min−1. Fresh fecal samples were collected the day after the last exercise session. The mice were then euthanized, and samples of the gastrocnemius muscle and plasma were collected. The feces were immediately weighed and diluted 10-fold with sterile phosphate buffer that was pre-deoxygenated under an argon gas stream. The suspension was centrifuged (100 × g, 2 min, 4 °C), and the upper layer was collected and mixed with sterile $10\%$ glycerol for FMT. The samples were dispensed into vials, immediately frozen in liquid nitrogen, and stored at −80°C until further use.
On arrival, twenty-eight male germ-free mice (10 weeks old, Sankyo Labo Service, Tokyo, Japan) were divided into two groups and administered fecal samples from sedentary or training donor mice by gavage under germ-free conditions. Then, the recipient mice were kept under specific-pathogen-free conditions and allowed free access to autoclaved water and a laboratory HFD (Research Diets, New Brunswick, NJ) containing protein, $20\%$ kcal; fat, $45\%$ kcal; and carbohydrate, $35\%$ kcal. FMT was conducted twice a week throughout the housing period. The mice were housed together in cages with filtered tops and received identical fecal samples. After 1 week, six mice from each recipient group were fasted for 4 h. The mice were then euthanized, and muscle tissues, blood, and fecal samples were collected, immediately frozen, and stored at −80°C. For the remaining eight mice of each recipient group, oral GTT and intraperitoneal ITT were performed at week 8. Subsequently, muscle tissues and plasma were collected for biochemical assays.
Male AMPK γ3-knockout mice were generated using gene-targeting techniques.63 Mice were acclimatized in an air-conditioned (22 ± 2 °C) room with a 12:12 h light–dark cycle. Both knockout and wild-type littermate mice were used for high throughput 16S rRNA amplicon sequencing of fecal samples collected after fasting for 4 h.
## Cell culture
C2C12 myocytes were cultured as previously described.64 Briefly, C2C12 myoblasts were grown at 37 °C in a $5\%$ CO2 incubator in Dulbecco’s modified Eagle’s medium (DMEM) (4.5 g/L glucose) (Nacalai Tesque Corp., Kyoto, Japan) supplemented with $10\%$ fetal bovine serum (Equitech-Bio, Inc., Kerrville, TX, USA) and $1\%$ penicillin-streptomycin (Nacalai Tesque Corp.). To induce cell differentiation into myotubes, the medium was changed to DMEM (1.0 g/L glucose) (Nacalai Tesque Corp.) supplemented with $2\%$ horse serum (Thermo Fisher Scientific Inc.) and $1\%$ penicillin-streptomycin (Nacalai Tesque Corp.) for 4 days. Then, experiments using BAs (αMCA and tauro-αMCA; Cayman Chemical, Ann Arbor, MI, USA; Toronto Research Chemical, ON, Canada), palmitic acid (TCI Chemicals, Tokyo, Japan), and sotrastaurin, a PKCθ inhibitor (AEB071; Selleck Chemicals, Houston, TX, USA), were performed following incubation for 4 h in non-supplemented medium.
## GTTs and ITTs
In the recipient mice, oral GTT was performed after an overnight fast. The mice received $20\%$ glucose solution (FUSO Pharmaceutical Industries, Osaka, Japan) by gavage at 100 μL/10 g body weight. Tail vein blood was collected immediately before and at 30, 60, and 120 min after gavage. Glucose levels were measured using Glutest Ace R (Sanwa Kagaku Kenkyusho Co. Ltd. Nagoya, Japan). For ITT, mice were starved for 4 h and 0.33 U/kg human insulin (Humulin R; Eli Lilly Japan K.K., Kobe, Japan) was injected intraperitoneally. Glucose levels were measured from tail vein blood samples collected immediately before and at 30, 60, and 120 min after insulin injection.
## Analysis of fecal microbiota by high throughput 16S rRNA amplicon sequencing
Extraction of bacterial DNA from feces was conducted as described previously.65 Library preparation and deep sequencing were also performed as described previously.65 In the DNA sequencing, the V3-4 region of 16S rRNA genes in each sample was amplified by two-step PCR. The prepared library pool combined with phiX Control (expected $20\%$) was sequenced using a 285-bp paired-end strategy on the MiSeq (Illumina KK, Tokyo, Japan) according to the manufacturer’s instructions. Afterward, sequencing data analysis was performed with QIIME2 (version 2020.8) as described previously.66 In the process, the sequence was denoised with a DADA2 plugin of QIIME2. The Sklearn classifier was used for taxonomic assignment against the Greengenes database (13_8).
## Plasma glucose and insulin
Plasma glucose levels were measured using a glucose CII test kit (Wako, Osaka, Japan). Plasma insulin levels were measured using an enzyme-linked immunosorbent assay (ELISA) kit as per the manufacturer’s instructions (Mercodia AB, Uppsala, Sweden).
## Plasma metabolome analysis
Plasma samples from three recipient mice that received FMT for 1 week were used for targeted metabolomic analysis at Human Metabolome Technology Inc. (Tsuruoka, Yamagata, Japan). Capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS) analysis to detect approximately 1,000 water-soluble and ionic metabolites including glucose phosphates, amino acids, peptides, nucleic acids, organic acids, vitamins, and fatty acids was performed using an Agilent CE capillary electrophoresis system equipped with an Agilent 6210 time-of-flight mass spectrometer (Agilent Technologies, Waldbronn, Germany). Briefly, plasma (50 µL) was added to methanol (200 µL) containing internal standards (H3304-1002, HMT). The solution was mixed with Milli-Q water (150 µL) and centrifugally filtered through a Millipore 5-kDa cutoff filter (Millipore, Bedford, MA, USA). The filtrate was then resuspended in Milli-Q water (50 µL) for CE-TOFMS analysis using an Agilent CE capillary electrophoresis system equipped with an Agilent 6210 time-of-flight mass spectrometer (Agilent Technologies, Inc., Santa Clara, CA, USA). The spectrometer was scanned from m/z 50 to 1,000, and peaks were extracted using the MasterHands automatic integration software (Keio University, Tsuruoka, Yamagata, Japan) to obtain peak information, including m/z, peak area, and migration time (MT). Signal peaks were annotated according to HMT’s metabolite database based on their m/z values and MTs. Areas of the annotated peaks were then normalized to internal standards and sample amount in order to obtain the relative levels of each metabolite.
## Plasma BA analysis
Plasma BA profiles were detected using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system.67 Briefly, mouse plasma (20 µL) was diluted 100-fold with 2H-labeled internal standards and 0.5 M potassium phosphate buffer (pH 7.4). The mixture was applied to a Bond Elut C18 cartridge (200 mg; Agilent Technologies, Santa Clara, CA, USA). The target molecules were eluted in water/ethanol (1:9, v/v). The eluate was evaporated under nitrogen until dry and dissolved in 20 mM ammonium acetate buffer (pH 7.5)/methanol (1:1, v/v). An aliquot of each sample was injected into the LC-MS/MS system for analysis. Chromatographic separation was performed using a Hypersil GOLD column (Thermo Fisher Scientific, Waltham, MA). A mixture of 20 mM ammonium acetate buffer (pH 7.5), acetonitrile, and methanol (70:15:15, v/v) was used for the initial mobile phase, which was gradually changed to 30:35:35 (v/v/v) over 30 min.
## Microarray analysis
Total RNA was extracted from the frozen gastrocnemius muscle of recipient mice using an RNeasy Mini Kit (Qiagen, Valencia, CA, USA). Target hybridization and cRNA preparations were performed according to the Affymetrix GeneChip® Technical Protocol (Affymetrix, Santa Clara, CA, USA). Affymetrix GeneChip® Mouse Gene 1.0 ST arrays were stained and washed in an Affymetrix Fluidics Station 450 and scanned using a GeneChip® Scanner 3000 7G (Affymetrix). Expression levels were analyzed using Transcriptome Analysis Console Software 4.0 (Affymetrix) following background correction, signal summarization, and normalization by SST[EMS1]-RMA. Pathways that were significantly enriched in the list of differentially expressed genes were identified using Ingenuity® Pathway Analysis (IPA®, QIAGEN).
## Glucose uptake assay
2-deoxy-glucose uptake was examined using a Glucose Uptake-Glo Assay (Promega Corp., Madison, WI, USA). Differentiated myotubes were prepared in 96 well plates. After washing with PBS, cholic acids were added to the cells and cultured with palmitic acid for 24 h. Thereafter, reagents were added according to the manufacturer’s instructions. 2-deoxy-glucose uptake was measured as luminescence intensity using a SpectraMax microplate reader (Molecular Devices, LLC., Sunnyvale, CA, USA).
## Glycogen assay
The gastrocnemius muscle was homogenized with 0.3 M percholic acid on ice. Then, 200 mM sodium acetate and 4.2 mg/mL amyloglucosidase were added and the sample was incubated for 2 h at 55°C. Thereafter, 2 M Tris-HCl was added at room temperature. After centrifugation, glucose content was examined using a D-glucose measurement kit as per the manufacturer’s instructions (F-kit glucose, Roche Diagnostics, Mannheim, Germany).
## Protein assay
Extracted proteins were separated by SDS-PAGE and transferred onto nitrocellulose membranes. Subsequently, the blots were incubated with primary antibodies against phospho-AMPKα (Thr172), total AMPKα, phospho-ACC (Ser212), total ACC, phospho-Akt (Ser473), total Akt, phospho-Akt substrate (Ser/Thr) (AS), phospho-AS160 (Thr642), total AS160, phospho-calcium/calmodulin-dependent protein kinase II (CaMKII) (Thr286), total CaMKII, phospho-LKB1 (Ser428), total LKB1 (all from Cell Signaling Technology, Beverly, MA), glucose transporter 4 (GLUT4) (Merck Millipore, Darmstadt, Germany), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Abcam, Cambridge, MA, USA). Subsequently, membranes were incubated with horseradish peroxidase-conjugated secondary antibody and visualized using enhanced chemiluminescence substrate (Immobilon, Millipore). Each band was detected using an image analyzer (Lumino Graph I, ATTO Corp., Tokyo, Japan). Signal intensities were quantified using the ImageJ software (National Institutes of Health, Bethesda, MD). C-C motif chemokine ligand 2 (CCL-2) and IGF-1 in tissues and culture media were measured using ELISA kits (MCP-1; Sigma, IGF-1; Proteintech, Chicago, IL, USA).
## RNA extraction and real-time PCR
Total RNA was extracted using Sepazol (Nacalai Tesque). After reverse transcription, quantitative PCR was performed using a StepOne Plus Real-Time PCR system (Life Technologies, Carlsbad, CA, USA) with the THUNDERBIRD Probe qPCR Mix (ToYoBo, Tokyo, Japan) and TaqMan primers CCL-2: ID Mm00441242_m1, C-X-C motif ligand 1 [CXCL-1]: ID Mm04207460_m1, toll-like receptor 4 [TLR-4]: ID Mm00445273_m1, tumor necrosis factor-α [TNF-α]: ID Mm00443258_m1, interleukin-1β [IL-1β]: ID Mm00434228_m1, F$\frac{4}{80}$: ID Mm00802529_m1, cytochrome c oxidase subunit IV isoform 1 [COX4-1]: ID Mm01250094_m1, and peroxisome proliferator-activated receptor gamma coactivator 1-α [PGC1α]: ID Ms01208835_m1, Thermo Fisher Scientific). Threshold cycle (Ct) values were determined using the StepOne software, version 2.3 (Thermo Fisher Scientific). *Relative* gene expression was calculated by the comparative Ct method relative to the β-actin reference gene.
## Fecal bile salt hydrolase activity
Total fecal protein sample was extracted with pre-deoxygenated phosphate buffer added with bacterial and mammalian protease inhibitors (Sigma) and 1 mM dithiothreitol. The bile salt hydrolase activity was measured using a modification a precipitation-based assay described previously.68,69 The extracted samples (500 μg protein) were incubated with 1 mM tauro-αMCA in PBS (pH 5.8) at 37 °C for 6 h. The insoluble free-form product was measured as absorbance intensity using a microplate reader (Molecular Devices). The protein samples incubated with PBS or αMCA were set as negative and positive controls, respectively.
## Network analysis
To identify possible networks between microbiota, the network analysis of plasma BA deconjugation, and muscle AMPK signaling was analyzed with Spearman’s correlations, and visualized with the Cytoscape v3.8.2 open-source software. Bacteria that showed higher abundance in RT than RS at 1 week of FMT were used for the analysis. Spearman correlation coefficients with a minimal cutoff threshold of 0.6 ($p \leq 0.05$, false discovery rate corrected) were calculated.
## Quantification and statistical analysis
All data are reported as the mean ± standard error. ANOVA or Student’s t-test was performed to determine statistical significance between groups. If ANOVA indicated statistical significance, post hoc multiple comparisons were conducted using Tukey’s honestly significant difference test to determine the significance of the differences among the mean values. Microarray data were normalized using the robust multi-array average method (Expression Console 1.3.0.187, Affymetrix) and analyzed using one-way between-subject ANOVA (recipient from sedentary donor [RS] vs. recipient from trained donor [RT]). Screening of significant changes in expressed transcripts was considered at $p \leq 0.05$ and false discovery rate (FDR) < 0.1. Correlations between microbiota and BAs and between mRNA and BAs were evaluated using Spearman’s correlation analysis. When a normal distribution was not obtained, a non-parametric analysis was used for each comparison. Statistical significance was set at $p \leq 0.05$, a trend at $p \leq 0.1.$
## Supplemental information
Document S1. Figures S1–S8 and Tables S1–S4
## Data and code availability
Complete data of the microarray analysis have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.
This paper does not report original code.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
## Author contributions
W.A. and Y.N. designed and coordinated the study. W.A., R.I., and K.M. contributed to experimental design and performance. W.A., R.I., A.H., M.K., and T.T. analyzed and evaluated the data. M.B. supported animal preparation and sample collection. W.A. drafted the manuscript with input from the other authors. All authors critically reviewed and approved the final version of the manuscript.
## Declaration of interests
The authors declare no competing interests.
## Inclusion and diversity
We support inclusive, diverse, and equitable conduct of research.
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|
---
title: Parents’ digital skills and their development in the context of the Corona
pandemic
authors:
- Badr A. Alharbi
- Usama M. Ibrahem
- Mahmoud A. Moussa
- Mona A. Alrashidy
- Sameh F. Saleh
journal: Humanities & Social Sciences Communications
year: 2023
pmcid: PMC10005916
doi: 10.1057/s41599-023-01556-7
license: CC BY 4.0
---
# Parents’ digital skills and their development in the context of the Corona pandemic
## Abstract
We investigate parents’ and guardians’ digital skills and the extent of their development in the context of the spread of the Corona epidemic. In addition, we sought to explore the differences in digital skills between parents and their employment status, age, and responsibility in teaching children. We sought to rely on the descriptive-analytical approach and prepared a scale of eight theoretical dimensions with the participation of 250 students’ Saudi parents. The application of the study was by online submission form (via Edit Submission). Our findings showed that there was a discrepancy in the performance of the sample, which was very high in the dimensions of operational skills, instrumental skills, and cognitive constructivism skills. There were also differences between the effect of computers on the instrumental skills and cognitive constructivism skills of the parents. Parents’ dependence on alternative digital sources in exploring for information, formulating knowledge, manipulating it, and criticizing. The learner can reach the cognitive level in a more flexible manner, which allows him to gain learning objectives. The knowledge navigation can be developed because of different online outdoor exercises and software familiar. This requires self-organization to search for appropriate knowledge to use in the renewal of the cognitive structure.
## Introduction
Students, teachers, parents, and other people with a stake in higher education are paying attention to developing digital skills because it is one of the most important skills for work and school. Conclusions about digital skills showed how digital technologies could support easy access to data and enhance social status (Passey and Tatnall, 2014). In addition, digital technology has changed the form of communication with others, ways of accessing information, navigating, and sharing information to solve problems (Fetaji, 2021). Thus, it also entailed digital literacy, which included a wide range of skills in using social networking sites and mobile applications, creating electronic content, and understanding cybercrime and safety issues. Developing digital skills is often viewed as encouraging participation in digital life safely and effectively (Fetaji, 2021). The study shows that training on digital skills is, in most cases, significant to help individuals identify the resources that best suit their needs (Titan et al., 2021). The ubiquitous use of digital technologies may lead to changing relations in social life and open new avenues for higher education institutions to ensure the innovation, digitization, exploration, and dissemination of new knowledge in society. This may also provide impetus to sustain innovation and support for scientific talents, research, management skills, and knowledge transfer (Magni et al., 2021; Titan et al., 2021).
Warschauer and Matuchniak [2010] talked about the effects of information technology in real-life, especially when they linked the interactive parts of speech with the archival parts of writing. This made it possible for many people to talk to each other, no matter where they were or what time it was. It also lets people participate in the social editing of texts, helps people learn more, and makes it easier to make and share content in speech and writing. Na and Chia [2008] looked at parents of 0–6-year-old children to see how informal learning from online resources could change their confidence in parenting and understanding of their children’s development. They saw that parents who used online resources could figure out their children’s linguistic and emotional needs and trust their ability to help with the child’s cognitive, behavioral, and emotional development (Na and Chia, 2008). Digital skills may also help families who have children with chronic diseases or neurological or developmental syndromes like autism to find helpful information and solutions on the Internet as a safe outlet to alleviate stress, anxiety, and depression (Douma et al., 2021).
From an economic and environmental point of view, learning through digital skills provides material possibilities. Learning can also be marketed to other countries and cultures that need these educational stimuli to master new learning courses (Alvarado et al., 2021). So, learning based on digital skills, whether for parents or children, saves waste and helps export learning by making it informal and focusing on providing studying as a service outside the boundaries of study in the Kingdom of Saudi Arabia (Muhammad and Khan, 2021).
## Research questions
COVID-19 pandemic has created the largest disruption ever on education systems, affecting nearly 1.6 billion learners in more than 190 countries across the world. Many schools and other learning centers closed that influenced 94 percent of the world’s student population, up to 99 percent in low and lower-middle income countries (Burkadze, 2022).
The emergence of the Corona epidemic in its first, second, and third waves led to the complete or partial closure of schools and educational institutions at all levels. Online teaching and assessment approved during this period spelled the development of learners’ abilities to create digital content and parents’ involvement in teaching their children digital learning skills or identifying new motivational skills that were rarely used during physical classes. Thus, this study aimed to identify how parents dealt with their children during the pandemic and how they used their educational and digital skills to teach and monitor their education at home. The study summarized in the following questions:What were parents’ digital skills used to teach children at home during the spread of the Corona epidemic?What were the differences in digital skills between parents and their employment status, age, and responsibility in teaching children?
## The concept of digital skills
The combination of knowledge, skills, and attitudes is essential for an individual’s self-realization, development, citizenship, social integration, and employment (Jashari et al., 2020). The core digital skills, however, include the critical use of information technology for work, entertainment, and communication (Andriole, 2018). Supported by communication technology, digital skills generally refer to information retrieval, evaluation, storage, production, presentation, exchange, communication, and participation in collaborative networks over the Internet (Law et al., 2018). Digital skills or digital literacy also mean using digital technologies to access, manage, understand, integrate, communicate, evaluate, and create information safely and appropriately for work (Jashari et al., 2020). Digital skills, or twenty-first-century skills, including problem-solving, digital citizenship, cooperation, and communication, are essential to entering the labor market successfully (Van Laar et al., 2017). Digital skills can also be cognitive, which you can get by reflecting on what you know, or practical, which you can get by using methods and getting experience. Practical skills that include autonomy or independence are learned via self-experience and self-directed activities, and participatory skills are learned via collaborative, directed, and team-led learning processes.
Whereas Van Deursen et al. [ 2014] identifies a series of three general types of skills:A.Automation skills include the operational manipulation of technology that is used for operating hardware and software and dealing with networks. Van Dijk [2006] recognizes digital skills such as information handling and content creation as the key to fully acquiring new technologies and software for coexistence, entertainment, and learning in a knowledge society; These skills include handling information and content creation (Van Laar et al., 2017).B.The cognitive structure of information and the ways to make cognitive schemas that can be used to solve problems are examples of structural skills. They are information-functional skills that include the ability to find, process, and evaluate information. C.Strategic skills mean the ability to use resources to achieve an objective that includes a willingness to proactively search for information to make information-based decisions.
Strategies for lifelong learning need to address the growing need for advanced digital skills in all jobs and for all learners, including those who grew up with technology and older people. This is important because it helps fill in the gaps in teaching and learning in all fields of science and technology. Digital skills start with ICT integration and learning sooner (Van Laar et al., 2017). Using it more critically and creatively mean investing in human capital to benefit from knowledge economics, with an emphasis on privacy, security, and safety levels (Jashari et al., 2020; Van Laar et al., 2017).
## Factors causing the need for DS
Sunita [2020] identified the following challenges to promoting digital skills:Limited internet availability and limited access to devices: Online platforms may be the only way to reach learners during the closure, but teachers and students reported slow internet and connectivity problems interfering with the seamless flow of learning. Poor infrastructure hampered the design development of attractive and appropriate content suitable for the various levels of learning and different ages of the learner via the Internet. Online learning is not about throwing the book at the learner, nor should the scientific material be reformulated, compiled, and adapted to the needs of the educated audience, which ensures that attention and their interests are captured and preserved.
## Types of digital skills
Technological skills: The Internet has helped access the knowledge, thaphics, static or animated images, video, and audio to enable parents to follow their children’s learning paths. It also involved parents in personal learning to improve their awareness of their educational responsibilities for their children and follow-up (Na and Chia, 2008). Educational technologies during the pandemic helped compensate for the teacher’s physical absence. They enhanced the learner’s educational experiences, helped verbal and nonverbal communication through video learning and gestures, and obtained supportive feedback, whether with materials and scientific resources that provide opportunities for student participation and motivation. This also made parents more satisfied with the levels of learning of their children (Daugvilaite, 2021). The platforms have also stimulated online music learning that required participation in playing, visual reading and auditory skills to meet training difficulties. It also helped to analyze the auditory skills of melodies and tones in more detail (Pike and Shoemaker, 2013; Rutkowski et al., 2021).Cybersecurity Skills: *It is* divided into two parts: (a) Personal security skills: When parents have digital questions and dilemmas, they frequently turn to a few sources of support and advice (Livingstone et al., 2018). The adoption of educational technologies supports learning and assessment, providing general satisfaction to parents and guardians and the ability to quickly adapt their children to these educational applications (Ocaña-Fernández et al., 2019). [ 2] Information security skills: Often because of this stage, some psychological traits are generated in parents, including application anxiety and technophobia, and this may be due to the forced use of technology during the epidemic (Van Dijk, 2006).Critical skills: They help self-criticism and social withdrawal from contexts that cause a crisis in adapting to reality, such as searching for solutions to the child’s troubled behaviors and improving relations between the child’s interactions with parents. Parents were required to monitor their children’s education and behavior, which the teacher would otherwise take care of in regular school (Douma et al., 2021).Virtual environments also help competitive learning by improving the design of learning materials. ( Skulmowski and Xu, 2021) These materials were made with learners’ cognitive abilities in mind so that knowledge could be used and remembered for a long time. This skill requires making informed judgments about the quality and accuracy of the information and knowledge to be produced. Information is evaluated for its validity and value and needs theoretical and practical justifications for acceptance or refusal (Van Deursen and Van Dijk, 2014). The evaluation also depends on the complexity of the information and the tasks to be criticized (Chen, 2013).Also, we can judge information well if we fully understand what it says or means behind the words. Information with a lower cognitive load and density is relatively easier to evaluate, and ideas unrelated to digital content lead to cognitive load (Kilic, 2014). Interactive digital learning environments are easier on the brain and promote deep learning, motivation, realism, and fluency. The interaction is also more hands-on, which makes it easier to learn concepts. This, in turn, could develop higher thinking skills of students and their parents, such as reasoning, reflective thinking, critical thinking, and creative thinking (Skulmowski and Xu, 2021).Information skills: During the pandemic, the internet has helped parents navigate economic and social problems and inequality in accessing formal learning by creating learning opportunities and facilitating learning experiences as an alternative to tutoring by browsing different educational websites and different online study groups (DiSalvo et al., 2016).Parents’ motive for using the *Internet is* also to search for information to understand health problems in virtual platforms and read suggestions and comments from parents in cases like their children, to improve their knowledge of the disease and quality of life (Camden et al., 2016). Parents also looked for textual content and related images for clarity and tried to make sense of information with real-life and daily applications in the real world, increasing information navigation and knowledge sharing among colleagues and the public (Chen, 2013). Parents may sometimes push to use artificial intelligence (AI) for accurate knowledge (Ocaña-Fernández et al., 2019). AI may also help parents with their children’s learning through more realistic and diverse applications for children that rely on sound and visual effects compatible with the child’s cognitive development (Lutz, 2019; Ocaña-Fernández et al., 2019).Communication skills: *It is* defined as the ability to encode and decode messages to build meaning and understand and exchange information in all interactive applications (Van Deursen and Van Dijk, 2014). It is divided into two parts: automatic communication skills (visual and auditory), and alternative communication skills (skills like real-life skills) (Chen et al., 2021; Lappan et al., 2020).It refers to the use of aspects of the environment and manipulation of sensory input variables, where learning can reach higher levels than available by navigating spatial contexts to study abstract knowledge, as it provides accurate representations of learning stimuli in three-dimensional electronic environments (Kuhrt et al., 2021; Koehorst et al., 2021). Navigating knowledge is a tool for linking the skills of the twenty-first century, which are technology, information management, communication, collaboration, creativity, critical thinking, problem-solving, and contextual skills for learning, including cognitive flexibility, cultural awareness, moral awareness, self-direction, and lifelong learning (Van Laar et al., 2017). Also, parents’ mastery of digital skills varies according to different social strata (Van Deursen et al., 2014; (Skulmowski and Xu, 2021).
## Methodology
To analyze the extent to which parents and guardians mastered digital skills and knowledge development during COVID19, the study used the descriptive-analytical approach.
## Design
The study employed a questionnaire with three parts. The first part is demographic information about respondents, including gender, education level, age, work state, experience, teaching responsibilities, and work dependency on PC. The second one is about technological skills. The third section is about measuring personal security skills. The fourth is about critical skills, and the fifth is measuring hardware locking skills. The following is about information skills, and the seventh is about communication skills. The last two parts are about knowledge navigation and electronic social skills.
## Participants
Two hundred fifty students’ Saudi parents participated in the study. An available sample has been drawn. The questionnaire items were answered through the Google Forms platform. Everyone was free to withdraw whenever they saw that this measure did not fit their preferences. The study classified demographic variables as in Table 1:Table 1Demographic variables description. VariablesLevelsFrequenciesPercentGenderFathers$18373.2\%$Mothers$6726.8\%$Education levelDiploma$4116.4\%$Bachelors$13252.8\%$Post-graduate study$31.2\%$Master’s degree$3915.6\%$Ph. D$3514\%$Age level20–30 years$145.6\%$30–40 years$8333.2\%$40–50 years$11445.6\%$More than 50 years$3915.6\%$Work stateNo job$3815.2\%$*Has a* job$21284.8\%$Experience yearsLess than 5 years$249.6\%$5–10 years$2911.6\%$10–15 years$4919.6\%$More than 15 years$14859.2\%$Teaching responsibilitiesfully responsible$7530\%$Partially responsible$7329.2\%$Share with father and mother$8634.4\%$You have no direct involvement in teaching$166.4\%$Work dependency on PCYes$20381.2\%$No$4718.8\%$
## The first stage
The scale aimed to verify the degree of mastery of digital skills for parents during COVID-19 and the extent of prior or current knowledge of these skills through practical work skills. Previous studies that dealt with digital culture, virtual skills, and digital skills have been reviewed, such as (Rodríguez-de-Dios et al., 2016; Van Deursen, et al., 2014; Van Laar et al., 2018; Van Deursen and Van Dijk, 2014). The skills scale is included in Supplementary Appendix 1.
## The second stage
The study used content analysis to look at digital skills in many fields. It also looked at eight theoretical skills. At the same time, from an empirical point of view, it was confirmed that these are the main three-factor model skills: operational skills, cognitive constructivism skills, and instrumental skills. The study used a five-point Likert scale for collecting responses (1 = does not correspond at all, and 5 = corresponds exactly).
## The third stage
51 items were distributed over the eight theoretical subscales, and exploratory factor analysis (EFA) was accompanied to verify whether the item factor loadings was regular on the nature of the phenomenon from an empirical view.
## Procedures and data analysis
Responses on subscales were done by IBM SPSS V.23 software. The cut-score of item factor loadings below 0.50 was rejected. The parsimonious criteria were verified. Parsimonious criteria assumed by saturating each item with only one factor. The principal components analysis (PCA) method was analyzed. The correlation matrix was rotated in the varimax orthogonal rotation. The stability coefficient was performed by Cronbach’s alpha coefficient. The chi-square statistic value of independence of two variables was conducted to examine the associations between demographic variables and the educational responsibility variable of parents towards their children. The MANOVA test was computed to test the differences in digital skills in favor of demographic variables. The following figure shows the overall procedures in the study (Fig. 1).Fig. 1The overall procedures in the study.
## Ethical considerations
All procedures performed in the study were following the ethical standards of the institutional research committee of scientific Research Dean of Hail University (IRB Log Number: RG-21-064) and with the 1964 Helsinki declaration and its later amendments.
## Content validity
The item analysis was analyzed using EFA, using the PCA method, without determining the number of factors explained which items were organized. In this case, the items can be loaded freely on the dimensions that describe the phenomenon from an empirical view. The Varimax orthogonal rotation was done, given that the resulting factors are independent of each other.
The analysis results reached the KMO criterion value of 0.94, indicating the sample’s suitability and ability to understand what is required to explain the phenomenon under study. The commonality values ranged from 0.4 to 0.75, which means that the items have discrimination between the individual’s performance on the scale.
The results revealed eight factors that could explain the phenomenon before rotation. These factors explained $68.80\%$ of the total variance that explains the phenomenon. The eigenvalues of the analysis were 23.35, 2.86, 2.20, and 1.78. The excess of the underlying eigenvalues approximated about 1.2. The factors’ explained variances were $45.9\%$, $5.6\%$, $4.3\%$, and $2.7\%$, and the excess of the explained variances neared $2.2\%$, which is very small in expressing the phenomenon. It was noted that the eigenvalue of the first factor is statistically inflated, which means that this factor is polarizing for the items. In other words, these eight factors are just theoretical factors organized into a more extensive general factor that explains these digital skills. The resulting structure describes the digital skills of the custodian in searching for knowledge, caring for and managing it in the form of texts, and summarizing it to show the knowledge in a coded way.
Statistically expressed, this is what necessitated the use of orthogonal rotation. Since the first factor made the items polarized, the rotation made three factors with eigenvalues of 10.38, 10.19, and 7.85. The total variance explained by the main three-factor model was $55.71\%$. Statistically, this is logical since digital adequacy does not mean complete mastery of transferring and using knowledge management only, but those digital skills depend on the novelty and strangeness of concepts and the individual’s ability to enrich and apply that knowledge, and the difference in individual differences between individuals in abilities and traits, which critically presents knowledge. The item factor loadings are as in Table 2:Table 2Item factor loading of digital skills subscale. ItemsItem factor loadingFactor [1]Factor [2]Factor [3]Technological skills 10.67 20.59 30.66 40.57 5––– 6.53 70.65 8*0.54 90.60 100.55 110.66Personal security skills 120.51 130.54 14––– 150.64 160.63 170.65Critical skills 180.57 190.61 200.65 21––– 220.62Hardware locking skill 230.68 240.77 250.62 260.81 27*0.71 28*0.73Information skills 29––– 30––– 310.56 320.60 330.61 34*0.62Communication skills 350.68 360.63 37*0.57 38––– 39*0.65 40––– 410.57Knowledge navigation 420.53 430.65 440.65 450.59 460.60Electronic social skills 470.54 480.66 490.70 500.67 510.72 Eigenvalue10.3810.197.85 Explained variance (%)$20.34\%$$19.98\%$$15.39\%$ Alpha coefficients0.950.950.91 Mean Std deviation(*) means revised items.
The items’ factor loadings for personal, critical, and hardware security skills on a single factor for operational skills are shown in the table above. This factor says that operational skills describe the security of information, data, and devices, as well as the security of an individual.
Confirmatory factor analysis used to test the EFA model of digital skills. The results as the followings (Table 3):Table 3Digital skills model goodness of fit. IndexRSMEAGFINNFIPNFISRMRX2Value0.110.920.950.900.0695718.4**Means the significance of x2 (df) as a bad index for the digital skills model. The x2 (df) index is sensitive to the normality of data and sample size.
The results of the confirmatory factor analysis resulted in good fitting indicators, except for the chi-square and the RMSEA indices, which went out of range, and this is due to the violation of the multivariate normality indicated by the LISREL program in the statistical analysis. The item factor loadings as Table 4:Table 4Item factor loading of digital skills subscale. ItemsFactor loadingsStd errort-valueCCS10.630.04314.73CCS20.610.04314.15CCS30.700.04216.75CCS40.610.04314.14OPS60.640.04314.95CCS70.750.04018.50CCS80.710.04117.18CCS90.770.04019.11CCS100.710.04117.30CCS110.740.04118.06CCS120.640.04314.97ES130.690.04216.45ES150.640.04215.18OPS160.750.04018.40OPS170.730.04118.04OPS180.740.04118.17OPS190.700.04116.82ES200.730.04117.75ES220.770.04019.30ES230.680.04216.35ES240.780.04019.64ES250.710.04117.18ES260.800.03920.43ES270.830.03921.51ES280.810.03920.68CCS310.560.04412.73CCS320.790.03920.18CCS330.760.04018.90ES340.820.03921.06OPS350.840.03921.89OPS360.820.03920.86OPS370.540.04512.14CCS390.790.03920.01ES410.810.03920.72ES420.770.04019.14CCS430.830.03821.61CCS440.750.04018.67CCS450.700.04016.83CCS460.790.03920.03OPS470.820.03920.98OPS480.770.04019.14OPS490.800.04020.12OPS500.600.04413.62OPS510.730.04117.74OPS operational skills, ES elementary skills, CCS cognitive constructivism skills.
All vocabulary saturations were statistically significant on all dimensions. The saturations on the scale ranged from 0.56 to 0.84, which are medium-to-high saturations, which indicate the reliability of the scale and its relevance to the nature of the sample. The items’ factor loadings for personal, critical, and hardware security skills on a single factor for operational skills are shown in the table above. This factor says that functional skills describe the security of information, data, and devices, as well as the security of an individual.
The reliability coefficient was performed by Cronbach’s alpha and equaled 0.954, and the items’ reliability coefficients ranged from 0.950 to 0.954.
It is clear from the results that technological skills, informational skills, and knowledge navigation skills can be called cognitive constructivism skills. Although technical skills depend on programs and applications, they are concerned with knowledge, whether searching for it or treating it. Cronbach’s alpha had a reliability coefficient of 0.949, and the other reliability coefficients were between 0.945 and 0.948.
The social and technological skills and the first three items of the communication skills are saturated in one dimension that can be named after the automated skills. The first item concerns the automated technological aspect in interactions or the production or output of texts. It was pointed out that the communication skills dimension is unstable and that its items are split into two dimensions, which is different from the automated skills dimension. Cronbach’s alpha showed that the reliability coefficient was 0.911, and the reliability coefficients ranged from 0.893 to 0.911.
## Descriptive statistics for the study
The descriptive statistics indices were estimated for the digital skills subscale such as mean, median, standard deviation, variance, and skewness, and the results were as in the Table 5:Table 5The descriptive indices for digital skills subscale. Elementary skillsCognitive constructivism skillsOperational skillsMean66.8936.9865.04Median673864Std deviation14.026.3714.21Variance196.6440.52201.83Skewness–0.23–0.36–0.19 By using frequencies, the results showed that there were no outliers in the data. The score variance was higher than the mean index, which means there was a massive variance in individuals’ skills. It was noted from the rates of torsion that the individuals’ degrees follow the average distribution. The Fig. 2. Showed the digital skills subscales mean:Fig. 2The average value of digital skills subscales.
Shapiro- Wilk test performed to test the normality of the dependent variables. The elementary skills were non- normal data (statistic = 0.971, $$P \leq 0.000$$). the Cognitive constructivism skills has no normal data (statistic = 0.976, $$P \leq 0.000$$). Operational skills were no normality (statistics = 0.927, $$P \leq 0.000$$).
## The relationship between children’s teaching responsibility and experience
Chi-square test statistic for the two variables’ independence was performed to examine the association between the adoption of teaching responsibility for children in hybrid learning, depending on the years of experience of the parents. The results revealed statistical significance (X2 = 26.16, df = 9; $$P \leq 0.002$$). The teaching responsibility of teaching children during the COVID19 relied on experience with digital skills.
## The relationship between the teaching responsibility of the children and the dependence of the parents’ job on use of the computer
The chi-square results were not statistically significant (X2 = 2.53, df = 3; $$P \leq 0.470$$). It means that the participation of parents in the educational responsibility of teaching their children in the conditions of the epidemic does not depend on the parent’s reliance on the use of the computer, and therefore this means coexisting and adapting to the conditions of the epidemic. It means that the experience received by the learner occurred during the teacher’s guidance. The learner receives his grades continuously after being subjected to a typical assessment process, and therefore there is no effect on the nature of the parents’ profession being linked to computer skills. Support may be received through the internet for children with special needs in the form of instructions or assignments performed by parents.
## The relationship between the educational responsibility of the children and the educational level of the parents
The chi-square test showed that there were statistically significant (X2 = 26, df = 12; $$P \leq 0.011$$). This means that the parental teaching responsibility during a pandemic is associated with the parent’s educational level. In the sense that the digital skills mastered through education may help the child to advance in the learning process, as it acquires higher-order thinking skills by training his parents.
## The relationship between the educational responsibility of the children and the age of the parents
The results of the chi-square statistic between the adoption of the teaching responsibility of the children and the chronological age of the parents were not statistically significant (X2 = 10.33, df = 9; $$P \leq 0.325$$). The result implications that the participation between parents’ responsibility in the teaching during the epidemic is not dependent on the parent’s age.
## Differences in digital instrumental skills are due to the parent’s gender, employment state, age, and responsibility for teaching children
The study used the multiple analysis of variance (MANOVA) test to determine the differences in the instrumental skills of parents during the ongoing Corona pandemic. The results revealed the following in Table 6:Table 6Differences in digital instrumental skills according to demographic variables. SourceSum of squaresdfMean squareFSig.interceptHypothesis31745.553131745.55394.8890.000Error3961.92811.842334.554astatusHypothesis7.41017.4100.0500.823Error34195.848231148.034beducation levelHypothesis967.0684241.7671.6330.167Error34195.848231148.034bage levelHypothesis1816.4783605.4934.0900.007Error34195.848231148.034bjob statusHypothesis323.9931323.9932.1890.140Error34195.848231148.034bexperienceHypothesis1168.6823389.5612.6320.051Error34195.848231148.034bkind_of_jobHypothesis924.4562462.2283.1220.046Error34195.848231148.034binstructional responsibilitiesHypothesis4102.50931367.5039.2380.000Error34195.848231148.034bdepend_on_PCHypothesis1293.75611293.7568.7400.003Error34195.848231148.034baThe error refers to the variance that occurred by variance between groups.bThe error refers to the variance that occurred by variance within groups.
The results concluded that there were no differences between parents in digital instrumental skills due to the ongoing Corona pandemic, and the mastery of these skills was not affected by the parents’ academic levels. These skills upgraded from a minor elementary skill to educational adequacy. In addition, age affected this skill, and this can be explained in two ways:*Age is* an influential index in mastering skills due to the hybrid learning conditions during pandemic situations. Or, through the individual’s pursuit of non-formal education to achieve educational competencies for continuous learning. Younger parents are more proficient in these skills, as their combination of social media and other software is behind this proficiency in the use of technology.
The study considers the second proposition to be logical. The value of the non-indicative effect of the number of work experience years justifies this. Furthermore, mastering technology and machine skills is not a requirement imposed on the elderly based on years of work experience. The nature of the work (governmental, private) requires training in these skills. Then, the confidential work imposes a set of competitive advantages between institutions. Therefore, the individual constantly attempts to achieve better levels of proficiency, which may have happened because of the dependency on computers.
## The differences in cognitive constructivism skills are due to the parent’s gender, employment status, age, and teaching responsibilities in teaching
The study depended on MANOVA to compute the differences in the cognitive constructivism skills of parents during the ongoing Corona pandemic. The results identified in Table 7:Table 7Differences in cognitive constructivism skills according to demographic variables. SourceSum of squaresdfMean squareFSig. InterceptHypothesis30426.815130426.81577.7820.000Error3021.8667.725391.180astatusHypothesis475.0171475.0173.3420.069Error32834.827231142.142beducation levelHypothesis4829.72641207.4328.4950.000Error32834.827231142.142bage_levelHypothesis1476.8853492.2953.4630.017Error32834.827231142.142bjob statusHypothesis447.4931447.4933.1480.077Error32834.827231142.142bexperienceHypothesis515.0973171.6991.2080.308Error32834.827231142.142bkind_of_jobHypothesis173.948286.9740.6120.543Error32834.827231142.142binstructional responsibilitiesHypothesis1638.6243546.2083.8430.010Error32834.827231142.142bdepend_on_PCHypothesis1997.76411997.76414.0550.000Error32834.827231142.142baThe error refers to the variance that occurred by variance between groups.bThe error refers to the variance that occurred by variance within groups.
The results agreed on the significance of each of the effects of cognitive constructivism skills depending on the age, education levels, the educational responsibility of the parent towards their children, and the computer skills-based work. This is maintained by several reasons, including:The individual’s ability to formulate knowledge in models, figures, schemas which facilitates children skipping the stage of education during the ongoing epidemic. Parents’ dependence on alternative digital sources in searching for information, formulating knowledge, managing it, and criticizing. It may have helped the learner to achieve a specific educational goal. The learner can reach the cognitive level in a more flexible manner, which helps him achieve learning goals. Parents’ ability, throughout life, is to know their children’s knowledge requirement, transfers to the learner the skills of the codified search for rich sources of knowledge. The ability to navigate knowledge because of mastering computer skills and, of course, because of practicing different technologies and software, made it an automated process in the search for knowledge.
## Differences in operational skills are due to the parent’s gender, employment status, age, and responsibility for teaching children
MANOVA results tested the differences in the operational skills of parents during the ongoing Corona pandemic. The results reached as in Table 8:Table 8Differences in operational skills according to demographic variables. SourceSum of squaresdfMean squareFSig. InterceptHypothesis10974.469110974.469182.4530.000Error563.9709.37660.150astatusHypothesis46.557146.5571.3810.241Error7789.75523133.722beducation levelHypothesis614.2954153.5744.5540.001Error7789.75523133.722bage levelHypothesis226.674375.5582.2410.084Error7789.75523133.722bjob statusHypothesis161.4061161.4064.7860.030Error7789.75523133.722bexperienceHypothesis109.897336.6321.0860.356Error7789.75523133.722bkind_of_jobHypothesis66.117233.0590.9800.377Error7789.75523133.722binstructional_responsibilitiesHypothesis468.2733156.0914.6290.004Error7789.75523133.722bdepend_on_PCHypothesis19.867119.8670.5890.444Error7789.75523133.722baThe error refers to the variance that occurred by variance between groups.bThe error refers to the variance that occurred by variance within groups.
According to the previous table, the parents’ educational level, work in a specific job, and teaching responsibility to the children during the pandemic created an incentive to work with them in a way that enabled them to achieve their learning goals. Perhaps the performance goals and the mastery goals improved among the children because of the parents’ efforts to improve the assignments required of the children. The presentation, interrelationship, management, and summarization of the learning contexts may have contributed to achieving children’s learning goals, and the operational skills of the parents may also have created perceived pleasure in learning for the children.
## Discussion and delimitations
The study aimed to verify the digital skills of parents of students in Saudi society, considering the Corona pandemic. The study made a scale with eight theoretical dimensions that were based on the results of other studies. Moreover, it was verified empirically and reached three dimensions approved by some previous studies (Van Deursen et al., 2014).
The results concluded that there was a discrepancy in the performance of the sample, which was very high in the operational skills dimension, instrumental skills, and finally cognitive constructivism skills. The reason for the low variance among participants can be explained that parents had benefited from the same amount of knowledge in solving problems. Either information processing processes may have reduced the level of the learner’s cognitive load in acquiring knowledge, which causes high mastery or achievement performance and permits the performance, and this is logical with the study (Kilic, 2014).
The results also seem logical regarding the existence of digital skills differences due to the teaching responsibility of parents, and this may be due to the main goal, whether for the learner or parents, is to solve educational problems or to provide the appropriate support when it comes to health or education with the ease that individuals proceed to pursue informal learning (Camden et al., 2016; DiSalvo et al., 2016).
There are also differences between the parents’ dependence on the computer in affecting instrumental skills and cognitive constructivism skills. It means that the parents’ familiarity with some digital skills helps them gain experience and find new ways to solve problems more realistically and wisely. The study identified that, where possible, parents used digital platforms with an interest in solving educational problems or to obtain stimuli that act as educational scaffolding that expand the reflective thinking of children as observed elsewhere (Chen, 2013; Ocaña-Fernández et al., 2019; Skulmowski and Xu, 2021).
The results of the study can be applied through online courses to educate parents, especially mothers. Digital skills are cultural learning frameworks that are compatible with traditional learning. Digital skills facilitate the learner to generate content that may work as a transition between knowledge, increased knowledge structure, and deeper knowledge representations. The study also identified some limitations. The participants in the study sample consisted of a higher number of fathers than mothers, which makes for a type I error in the cross-validation of the study results. The study also included $65\%$ of participants over 40 years old in terms of experience in raising children may be behind the $55\%$ explained variance of the three factors of the digital skills scale. The unexplained variance may be because of the sample identified by its wisdom in the creasing age. The participants’ responses suffered from social approval because of the large sample size or the participants’ feeling of low social self if they responded logically on the scale, which justifies the highest inequalities in instrumental skills and operational skills.
## Limitations
The study results can be generalized to parents, especially those aged 30 to 50 years, and families with students at the university, secondary, and preparatory stages, as the learner can use his parents as mediators in directing him to the stimuli of learning. Parents’ digital skills also provide avenues for learning outside of the classroom. It also lets the learner take courses through MOOCs to get deeper knowledge in different areas of education, especially at the post-graduate level. It is possible to have parents with low educational levels in digital skills search for and navigate knowledge methods to improve the standards of the cognitive structure associated with digital skills. Also, when parents are involved in their children’s education plans, it makes learning more satisfying for both the parents and the children. When parents keep an eye on their children, the children become more responsible.
## Educational applications
The school establish WhatsApp groups for parents meeting to communicate, change with them points of view concerned to students’ performance, their adaptation of rules, provide safe environment for their children. These meeting can be solved problem to help them to overcome the problem or difficult they face while dealing with digital learning.
## Supplementary information
digital skills scale The online version contains supplementary material available at 10.1057/s41599-023-01556-7.
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|
---
title: Indoor air surveillance and factors associated with respiratory pathogen detection
in community settings in Belgium
authors:
- Joren Raymenants
- Caspar Geenen
- Lore Budts
- Jonathan Thibaut
- Marijn Thijssen
- Hannelore De Mulder
- Sarah Gorissen
- Bastiaan Craessaerts
- Lies Laenen
- Kurt Beuselinck
- Sien Ombelet
- Els Keyaerts
- Emmanuel André
journal: Nature Communications
year: 2023
pmcid: PMC10005919
doi: 10.1038/s41467-023-36986-z
license: CC BY 4.0
---
# Indoor air surveillance and factors associated with respiratory pathogen detection in community settings in Belgium
## Abstract
Currently, the real-life impact of indoor climate, human behaviour, ventilation and air filtration on respiratory pathogen detection and concentration are poorly understood. This hinders the interpretability of bioaerosol quantification in indoor air to surveil respiratory pathogens and transmission risk. We tested 341 indoor air samples from 21 community settings in Belgium for 29 respiratory pathogens using qPCR. On average, 3.9 pathogens were positive per sample and $85.3\%$ of samples tested positive for at least one. Pathogen detection and concentration varied significantly by pathogen, month, and age group in generalised linear (mixed) models and generalised estimating equations. High CO2 and low natural ventilation were independent risk factors for detection. The odds ratio for detection was 1.09 ($95\%$ CI 1.03–1.15) per 100 parts per million (ppm) increase in CO2, and 0.88 ($95\%$ CI 0.80–0.97) per stepwise increase in natural ventilation (on a Likert scale). CO2 concentration and portable air filtration were independently associated with pathogen concentration. Each 100ppm increase in CO2 was associated with a qPCR Ct value decrease of 0.08 ($95\%$ CI −0.12 to −0.04), and portable air filtration with a 0.58 ($95\%$ CI 0.25–0.91) increase. The effects of occupancy, sampling duration, mask wearing, vocalisation, temperature, humidity and mechanical ventilation were not significant. Our results support the importance of ventilation and air filtration to reduce transmission.
Surveillance of respiratory pathogens in air may improve understanding of indoor transmission risks but impacts of context-specific factors on pathogen abundance are not well understood. Here, the authors investigate factors associated with 29 respiratory pathogens through surveillance of 21 community settings in Belgium.
## Introduction
Many respiratory infections are transmitted via the airborne route1–7. Airborne transmission is almost exclusively an indoor phenomenon3,8–10. Its risk to susceptible attendants depends on pathogen, host, behavioural and environmental/building related factors9,10. Pathogens differ in their ability to colonise hosts and survive in the environment while retaining infectiousness11,12. The number of hosts, their respiratory activity, mask wearing and individual predisposition influence aerosol generation4,13–16. Environmental/building related factors such as room volume and airflow patterns, temperature, humidity, UV radiation, ventilation and air filtration may impact aerosol transport, settling, inactivation and removal9–11.
There is some evidence supporting the use of ventilation to reduce infectious disease incidence. High CO2 concentration, which reflects poor ventilation, was directly associated with school absence due to illness and with common cold symptoms15,17. Low air exchange rates per person through mechanical ventilation were associated with higher incidence of pneumococcal disease during a prison outbreak and with a higher risk of tuberculin conversion in healthcare workers4,18. The evidence to support transmission reduction by means of portable air filters—which are more affordable than pathogen removal by classical Heating, Ventilation and Air Conditioning (HVAC) systems19—is more limited. They were associated with a reduced incidence of invasive aspergillosis and reduced surface contamination with Methicillin-resistant Staphylococcus Aureus20,21.
The quantification of respiratory pathogens or their genetic material in indoor air has been used to study the influence of environmental factors on disease transmission. This approach has the advantage of not requiring clinical follow-up of attendants. Dinoi et al. [ 2022] recently combined data from 73 studies performing qPCR on indoor air samples and showed that the SARS-CoV-2 bioaerosol load was lowest in outdoor air, and higher in indoor air from hospitals than from community settings, which points at the link between pathogen detection, by qPCR, and the risk of transmission for occupants22. In other studies, indoor CO2 concentration was associated with higher detection of rhinovirus bioaerosols in ambient air, higher concentration of bacterial cell wall components and culturable bacterial colony forming units23,24. The presence of an advanced mechanical ventilation system with high-efficiency particulate absorbing (HEPA) filtration, directional flow or increased air changes per hour (ACH), correlated with lower fungal colony forming units per unit volume in hospital settings25. On the other hand, bacterial bioaerosol loads were similar across areas with mechanical, advanced mechanical and natural ventilation in the same study. Another recent study reported that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral copies were more abundant in aerosols collected in closer proximity to an infected individual placed in a controlled environment. They also correlated positively with nasopharyngeal viral copies and ambient CO2. On the other hand, they correlated inversely with ventilation, portable air filtration and increased humidity26. As for portable air filtration, experiments using particle counters showed that portable filters sped up the clearance of airborne particles27,28. Two small studies also suggested a reduction in detection of SARS-CoV-2 genetic material in ambient air, but the effect was not significant29,30. In contrast, Conway-Morris et al. [ 2022] did see a significant reduction in the detection, by qPCR, of SARS-CoV-2 and other respiratory pathogens31. No study has thus far performed a multivariate analysis which controlled for other important variables (e.g occupancy, seasonality, mask wearing, etc.) when assessing the influence of either ventilation or portable air filters on the load of respiratory pathogen bioaerosols in real-life settings.
In addition to quantifying transmission risk, sampling and testing of indoor air for respiratory pathogen bioaerosols may become an important add-on to other data sources for epidemiological surveillance, such as clinical samples, sentinel surveillance and sewage monitoring32,33. During the COVID-19 pandemic, pathogen detection in sewage was scaled and provided important policy insights. One benefit of environmental samples is their independence from clinical test indications, tendency for testing or laboratory capacity. Sewage sampling can surveil the population of entire cities, but also has disadvantages. Samples are highly contaminated with environmental microorganisms, runoff times may be long and variable, and—especially for respiratory pathogens—the relationship between gastrointestinal shedding and the risk of transmission may be complex32,34. Air sampling may be an interesting and complimentary alternative.
QPCR on ambient air has long demonstrated its ability to detect pathogen presence, concentration, and genotype3,22,33,35,36. A recent study demonstrated the promise of multiplex qPCR on indoor air samples from community settings to track the presence of SARS-CoV-2 and other respiratory pathogens33. However, before this approach can be rolled out at scale, the factors influencing pathogen detection and concentration need better characterisation.
We aimed to empirically identify the host, pathogen, behavioural and environmental/building factors which correlate with a higher respiratory pathogen bioaerosol load, as assessed by qPCR, in indoor ambient air. We hypothesised that factors shown or suspected to contribute to airborne transmission would be associated with higher bioaerosol loads. If so, this would validate the use of qPCR on air samples as a proxy to quantify transmission risk and the effect of transmission reduction efforts. Also, these same factors would need consideration when performing qPCR on indoor air samples for epidemiological surveillance.
In a prospective study over a 7-month period, we therefore tested indoor ambient air from community settings in Belgium for 29 respiratory pathogens using qPCR. We investigated which of the following pathogen, host, behavioural and environmental/building related variables influenced their detection and concentration: the number of attendees, attendee density (number of attendees divided by room volume), sampling duration, mask wearing, vocalisation (voice use), natural ventilation (opening of doors and windows), portable air filtration, presence of mechanical ventilation, local COVID-19 incidence, indoor CO2 concentration, temperature and relative humidity. See Methods for detailed definitions of each assessed variable. In an interventional sub-study, we evaluated the effect of portable air filters in a nursery. In an exploratory analysis, we investigated whether the pathogens found in ambient air samples from community settings corresponded to the pathogens found in patients with severe respiratory infections in the same region and period. We therefore retrieved the results of the same, 29 respiratory pathogen qPCR panel, performed on respiratory samples of patients at University Hospitals Leuven37.
## Pathogen detection varies with season and age of attendants
We collected 341 environmental air samples in 21 sampling sites between October 2021 and April 2022. See Supplementary Table 1 for sampling site characteristics. Sampling durations (mean of 133 min and median of 126 min) corresponded well with the 120 min target. Two samples had missing results of the respiratory pathogen panel, while 36 had a missing result of the TaqPath SARS-CoV-2 assay (Thermo Fisher Scientific, Waltham, MA). The number of missing values for all variables is listed in Supplementary Table 1. Procedures for inferring them are described in Supplementary Methods.
When comparing positivity rates of all air samples, the most frequently detected pathogens, in descending order, were *Streptococcus pneumoniae* ($58\%$), human enterovirus (incl. rhinovirus) ($54\%$), human bocavirus ($45\%$), human adenovirus ($40\%$) and human cytomegalovirus ($38\%$). The percentage of samples which were positive for at least one pathogen was highest in the 3- to 6-year-old age group ($\frac{30}{30}$, $100\%$) followed by 0-3 years ($\frac{122}{123}$, $99\%$), 25–65 years ($\frac{9}{10}$, $90\%$), 12–18 years ($\frac{19}{24}$, $79\%$), 18–25 years ($\frac{44}{57}$, $77\%$), 6–12 years ($\frac{21}{29}$, $72\%$) and over 65 years ($\frac{46}{68}$, $68\%$). Supplementary Fig. 1 shows a detailed picture of the detected pathogens by age group and time-period. As Supplementary Table 1 shows, location-specific positivity rates for at least one pathogen varied from 10 to $100\%$, with high variation within age categories.
Temporal variations in the positivity rates of pathogens are apparent in Fig. 1. This figure shows results from the nursery setting, which was the most stable of age groups regarding sampling frequency, occupancy and the specific individuals present in the sampling locations. Human bocavirus, human cytomegalovirus, human enterovirus (incl. rhinovirus) and *Streptococcus pneumoniae* were almost always positive. We observed a long peak of human adenovirus and *Pneumocystis jirovecii* over the winter. Other pathogens had shorter peaks, such as *Human coronavirus* 229E, *Human coronavirus* HKU-1, *Human coronavirus* OC43, enterovirus D68, influenza A virus, human parainfluenza virus 3, respiratory syncytial virus A/B and SARS-CoV-2. Supplementary Fig. 2 shows the corresponding results for all sites. Fig. 1Positivity rates of respiratory pathogens in ambient air in nursery locations (red) compared to clinical samples from a local hospital (black/grey).Each panel represents qPCR test results of one of the 29 targeted pathogens, plotted by sampling date. Each pathogen which was positive in at least one air sample is shown. For SARS-CoV-2, the TaqPath results are shown. The individual red datapoints represent the positive and negative ambient air samples taken in a nursery (0 = negative, 1 = positive). This was the most stable age group regarding sampling frequency, occupancy and the continued presence of the same group of individuals (Supplementary Fig. 1). Red lines and shaded areas show a corresponding LOcally weEighted Scatterplot Smoothing (LOESS) regression of the positivity rate for each pathogen with $95\%$ confidence intervals. We included 121 air samples. For comparison, we retrieved the 206 results of the same 29 pathogen multiplex qPCR respiratory panel, performed in 0–3 year old children with respiratory infections at University Hospitals Leuven between October 2021 and May 2022. This hospital is adjacent to the nursery. Individual black datapoints represent the respiratory samples (0 = negative, 1 = positive). Black lines and shaded areas show a corresponding LOESS regression of the positivity rate for each pathogen with $95\%$ confidence intervals. An association between results from both sample types can be observed for SARS-CoV-2 (N positive air samples = 46) and, with much less positive samples, for enterovirus D68 and influenza A virus (N positive air samples = 4 for each). Supplementary Fig. 2 shows the corresponding results for all sites.
In an exploratory analysis, we assessed whether pathogen detection rates in ambient air in community settings corresponded with those detected in patients with severe respiratory infections in University Hospitals Leuven. This hospital is adjacent to the nursery and drains most patients in the region. In the nursery, the visual association was observed most clearly for SARS-CoV-2 (N positive air samples = 46) and, with much less positive samples, for enterovirus D68 and influenza A virus (N positive air samples = 4 for each) (Fig. 1). When comparing pathogen detection rates across all sampling sites and age groups, a visual association was apparent for SARS-CoV-2 only (Supplementary Fig. 2).
## The influence of pathogen, host, behavioural and environmental/building related factors on indoor bioaerosol load
We determined independent effects of a range of variables on airborne pathogen detection and concentration, by considering the qPCR result of each pathogen in a sample as a separate observation. The pathogen was considered a covariate in the resulting models, several of which included corrections for within-sample correlation. Missing data were imputed, and for each model, backward elimination was performed until only statistically significant variables remained (p-value < 0.05). Subsequently, observations with imputed variables were removed to confirm the observed associations.
We excluded pathogens with less than 10 positive qPCR tests after grouping them—to increase statistical power—as follows: human parainfluenza virus 1 to 4 under ‘parainfluenza viruses’; Human coronaviruses 229E, HKU-1, NL63 and OC43 under ‘other coronaviruses’. At least 10 positive results were present for 14 pathogens before grouping and for 12 pathogens after.
## Factors associated with pathogen detection
First, positivity for any respiratory pathogen was the binary outcome in a logistic regression model. Supplementary Table 3 lists the p-values and odds ratios before backward elimination. Backward elimination on this data, which included imputed datapoints, left pathogen, month, age group, natural ventilation, CO2 and vocalisation as significant variables. Unexpectedly, increased vocalisation (on a Likert scale) was associated with decreased pathogen detection. However, after exclusion of observations with imputed variables from the resulting model, vocalisation was removed as significant variable (Table 1, panel a). The odds ratio of pathogen detection was 1.09 ($95\%$ CI 1.03 to 1.15) per 100 parts per million (ppm) increase in CO2 concentration. In addition, the odds ratio of pathogen detection was 0.88 ($95\%$ CI 0.80 to 0.97) per stepwise increase in natural ventilation (Likert scale). Significance levels and effect sizes were almost identical in the mixed effects logistic regression and generalised estimating equations models, both correcting for within-sample correlation (Supplementary Table 4).Table 1lists the pathogen, host, behavioural and environmental/building related factors significantly associated with indoor air bioaerosol load after backward elimination in (logistic)generalised linear modelsRemaining variablesp-valueAdjusted odds ratio and $95\%$ CI(a) Pathogen detection (all pathogens) in a logistic regression modelPathogen<0.0001Age group<0.0001Month0.0024CO20.00151.09 (CI 1.03–1.15) per 100 ppm increase in CO2Natural ventilation0.00970.88 (CI 0.80–0.97) per step increase (Likert scale)Remaining variablesp-valueCoefficient and $95\%$ CI(b) Pathogen concentration (qPCR Ct of positive samples, all pathogens) in a linear regression modelPathogen<0.0001Age group<0.0001Month0.0020CO2<0.0001−0.08 (CI −0.12 to −0.04) per increase of 100 ppmPortable air filtration0.00050.58 (CI 0.25–0.91)*Panel a* lists the factors significantly associated with pathogen detection in a logistic regression model. It also shows effect sizes (odds ratios and $95\%$ CI) for CO2 and natural ventilation, after adjustment for pathogen, age group and month. See Supplementary Table 4 for unadjusted estimates. Panel b) lists the factors significantly associated with pathogen concentration (measured in qPCR Ct values) in a linear regression model. It also shows effect sizes (change in Ct value and $95\%$ CI) for CO2 and portable air filtration, after adjustment for pathogen, age group and month. See Supplementary Table 4 for unadjusted estimates. P-values are two-sided. They were estimated using the Chi squared method (no adjustment for multiple comparisons). Almost identical results of alternative models are shown in Supplementary Table 4.
To assess whether these associations held true for pathogens individually, we used the retained independent variables from these models to run a logistic regression model with backward elimination for each pathogen. These analyses had less power due to lower sample sizes. However, a significant association remained between mean CO2 and detection of human enterovirus (incl. rhinovirus), other coronaviruses, *Pneumocystis jirovecii* and Streptococcus pneumoniae. Contradictorily, we found a negative association with the detection of human bocavirus. As for natural ventilation, it was negatively associated with the detection of *Pneumocystis jirovecii* and respiratory syncytial virus A/B. Supplementary Table 5 lists all model outcomes. Supplementary Fig. 3 shows the univariate correlations of CO2 and natural ventilation with pathogen detection.
## Factors associated with pathogen concentration
Here, pathogen concentration, measured by qPCR Ct value, was the numeric outcome in a linear regression model. Supplementary Table 3 lists the p-values and effect sizes before backward elimination. Backward elimination on this data, which included imputed datapoints, left pathogen, month, age group, CO2 and air filtration as significant variables. Each 100ppm increase in CO2 concentration was associated with a decreased qPCR Ct value of 0.08 ($95\%$ CI 0.04 to 0.12). Natural ventilation was not significantly associated with pathogen concentration, which contrasts with the previous analysis. However, air filtration was significantly associated with pathogen concentration, with a 0.58 ($95\%$ CI 0.25 to 0.91) increase in Ct value in its presence. Significance levels and effect sizes were almost identical when excluding imputed values, or when running a linear mixed effects model correcting for within-sample correlation (Supplementary Table 4, panel b).
Starting with the retained independent variables from this model, we then ran a linear regression model with backward elimination for each individual pathogen, again taking qPCR Ct values as numeric outcome. Mean CO2 remained positively associated with a higher concentration (lower Ct value) of human adenovirus, human bocavirus, human cytomegalovirus, and Streptococcus pneumoniae. Contradictorily, it was associated with a lower concentration (higher Ct value) of respiratory syncytial virus A/B. Air filtration was associated with lower concentrations of human bocavirus, human cytomegalovirus, other coronaviruses, and *Streptococcus pneumoniae* (See Supplementary Table 6 for all model outcomes).
## Portable air filtration reduced pathogen detection and concentration in an interventional comparison
Starting from February 7th, air samples were taken simultaneously at three locations in the nursery, for 8 consecutive weeks, 3 times per week (Mondays, Wednesdays, and Fridays). Location 1 had no air filtration, location 2 had three Blue PURE 221 filters (Blueair®) installed, with a total theoretical clean air delivery rate of 1770 m3/h and a resulting number of ACH of 10.7. Location 3 had three Philips 3000i filters (Philips®) installed, with a total theoretical clean air delivery rate of 999 m3/h and a resulting number of ACH of 6.1 (Supplementary Fig. 4 and Supplementary Table 7).
First, we compared the positivity for any respiratory pathogen between three phases of air filtration in each location separately: no ongoing filtration (Mondays), 48 h of continuous filtration (Wednesdays) and 96 h of continuous filtration (Fridays) (Fig. 2). Cochran’s Q test showed no significant difference between Mondays, Wednesdays, and Fridays in location 1 ($$p \leq 0.6762$$). In location 2, a significant difference was present across days ($$p \leq 0.0006$$). Pairwise comparisons demonstrated a difference between Mondays and Wednesdays ($$p \leq 0.0229$$) and Mondays and Fridays ($$p \leq 0.0009$$) but not between Wednesdays and Fridays ($$p \leq 1$$). In location 3, the difference between days did not reach significance, but there was a trend ($$p \leq 0.0701$$).Fig. 2The influence of portable air filtration on respiratory pathogen detection and concentration in ambient air. For 8 consecutive weeks, samples were taken in three nursery locations on Mondays ($$n = 8$$), Wednesdays ($$n = 8$$), and Fridays ($$n = 8$$). Location 1 = control group (no air filtration); Location 2 = air filtration at 10.7 air changes per hour (ACH) starting Mondays (after sampling) and ending Fridays (after sampling); Location 3 = air filtration at 6.1 ACH starting Mondays (after sampling) and ending Fridays (after sampling). No one was present over the weekends. Panel (a) shows the mean number of pathogens detected in each of the nursery locations in the absence of filtration (not shaded), after 48 h of filtration (Locations 2 and 3, shaded) and after 96 h of filtration (Locations 2 and 3, shaded). * indicates a significant difference. We used a Cochran’s Q to compare the three filtration phases in each location separately, followed by pairwise Cochran’s Q tests when a significant difference (two-sided P-value < 0.05) was found, with Holm correction for multiple testing. In location 1, there was no significant difference between Mondays, Wednesdays, and Fridays ($$p \leq 0.6762$$). In location 2, a significant difference was present across days (0.0006), between Mondays and Wednesdays (pairwise comparison: $$p \leq 0.0229$$), and between Mondays and Fridays (pairwise comparison: $$p \leq 0.0009$$), but not between Wednesdays and Fridays (pairwise comparison: $$p \leq 1$$). In location 3, the difference between days did not reach significance, but there was a trend ($$p \leq 0.0701$$). Panel (b) shows the evolution of Ct values of n positive pathogens (throughout the 8 weeks) in each of the nursery locations in the absence of filtration (not shaded), after 48 h of filtration (shaded) and after 96 h of filtration (shaded). Ct values for a particular pathogen were included only if the pathogen was detected in samples from all three filtration phases during the same week in one location. The horizontal line corresponds to the mean Ct value on Mondays. The central boxplot line corresponds to the median, and the lower and upper boxplot bounds to the 25th and 75th percentiles. The upper/lower whisker extend from the upper/lower boxplot bound to the largest/lowest value no further than 1.5 * IQR. Data beyond the whiskers are outliers, plotted individually. * indicates a significant difference in a linear mixed effects regression model, including week and pathogen as random effects. $95\%$ CI were calculated using the confint command in R, and p-values with the Kenward–Roger approximation of the t-distribution. Ct values did not differ significantly in location 1 ($$p \leq 0.9506$$ between Mondays and Wednesdays; 0.7101 between Mondays and Fridays). In location 2, they were significantly different between Mondays and Wednesdays ($p \leq 0.0001$), and Mondays and Fridays ($$p \leq 0.0002$$). In location 3, they were not significantly different between Mondays and Wednesdays ($$p \leq 0.3146$$), but were between Mondays and Fridays ($$p \leq 0.0026$$).
Next, we used linear mixed effects regression models to evaluate the change in average concentration of respiratory pathogens on Wednesdays and Fridays, compared to baseline on Mondays, in each location separately. We saw no significant change in average Ct values throughout filtration phases in location 1 ($$p \leq 0.9506$$ when comparing Mondays to Wednesdays and 0.7101 when comparing Mondays to Fridays). In location 2, there was a significant increase in Ct value of 1.22 ($95\%$ CI 0.65–1.79, $p \leq 0.0001$) from Mondays to Wednesdays, and 1.13 from Mondays to Fridays ($95\%$ CI 0.57–1.70, $$p \leq 0.0002$$). In location 3, the difference in Ct value was not significant when comparing Mondays and Wednesdays (Ct +0.33, $95\%$ CI −0.32 to 0.98, $$p \leq 0.3146$$). However, there was a significant increase on Fridays compared to Mondays (Ct +1.02, $95\%$ CI 0.37–1.67, $$p \leq 0.0026$$). Supplementary Table 8 lists all model outcomes.
## Discussion
Both the recent study by Ramuta et al. [ 2022] and ours demonstrate the scalability of performing multi-pathogen qPCR on air samples from community settings to highlight pathogen presence33. The age of attendants appears to be a key determinant of the type and number of detected pathogens. In both studies, the number of detected pathogens was highest in sites populated by young children. This corresponds to the incidence rate of respiratory infections across age groups and—for pathogens such as human bocavirus, human cytomegalovirus and Streptococcus pneumoniae—the occurrence of prolonged shedding from the respiratory tract of young chidren38–41. Several pathogens, such as human adenovirus, Pneumocystis jirovecii, *Human coronavirus* 229E, *Human coronavirus* HKU-1, *Human coronavirus* OC43, enterovirus D68, influenza A virus, human parainfluenza virus 3, respiratory syncytial virus A/B and SARS-CoV-2, showed clear temporal variations in their detection rates, suggesting changing incidence rates throughout the study (Fig. 1 and Supplementary Fig. 2). As did Ramuta et al. [ 2022], we observed a visual association between the detection of SARS-CoV-2 in ambient air from community settings and in clinical samples from patients in the same geographical area (Fig. 1, Supplementary Fig. 2). The same association was not readily seen for most other pathogens. As clinical respiratory panels were only performed on samples of severely ill patients in our study (see Methods), this may imply that the link between variations in community circulation and morbidity was stronger for SARS-CoV-2 than for other pathogens.
The current and previous studies clearly indicate that (multiplex) qPCR on ambient air from community settings can be a complementary surveillance tool to track the circulation of respiratory pathogens. However, in addition to local epidemiology, periodical differences in pathogen detection and quantity can be explained by variations in behaviour, environmental factors, technical/analytical factors, or a combination. This underscores the need to characterise the sampling sites and standardise air sampling and analysis, to interpret the epidemiological relevance of pathogen presence and concentrations in ambient air.
By testing more samples and pathogens than previous studies, we were able to show for the first time that both respiratory pathogen genomic material presence and concentration, as assessed by qPCR, were positively associated with CO2 concentration, after correcting for a range of confounding variables. Results were consistent across models (logistic regression, mixed effect logistic regression, generalised estimating equations, linear regression, and linear mixed effect). Natural ventilation was also negatively associated with pathogen detection, even though our models corrected for CO2 concentration. This may result from the fact that CO2 is an imperfect marker for ventilation10. These results confirm that bioaerosol load in indoor ambient air correlates strongly with low levels of ventilation (see Table 1 and Supplementary Tables 4, 9). Pathogen specific models were generally consistent with these results, even if statistical power was more limited (Supplementary Tables 5, 6). Two exceptions were human bocavirus and respiratory syncytial virus A/B. In the former, CO2 correlated negatively with pathogen detection, although positively with pathogen concentration. In the latter, natural ventilation correlated negatively with detection as expected, but CO2 was negatively correlated with concentration. Type I errors or uncorrected confounders may explain these inconsistencies. The strength of the correlation between the CO2 concentration and the presence of a particular pathogen was often mirrored in the strength of the inverse correlation between natural ventilation and detection of the same pathogen (Supplementary Fig. 3).
In several multivariate models, the presence of air filtration remained independently associated with a lower concentration of respiratory pathogens, measured in qPCR Ct values, even after controlling for natural ventilation and CO2 concentration (Table 1, Supplementary Tables 6, 9). When analysing positivity rates in the two nursery sites with air filters, we saw a significant reduction in the number of detected pathogens during filtration in the location equipped with the highest filtration capacity (theoretical ACH of 10.7). The concentration of positive pathogens was also significantly reduced. Ct values increased by 1.13 on average ($95\%$ CI 0.57–1.70) between Mondays, when filtration had been inactive for 3 days, and Fridays, after 4 days of continuous filtration. In the location equipped with less filtration capacity (theoretical ACH of 6.1), we saw a trend towards a reduction in the number of detected pathogens. Here, the pathogen concentration was reduced significantly after four days of continuous filtration, but not after two. On Fridays, the Ct values increased by 1.02 on average ($95\%$ CI 0.37–1.67) compared to Mondays. We saw no difference in the control group (Location 1) (Fig. 2, Supplementary Table 8). The observed effect and dose–response relationship confirm the efficacy of air filtration to reduce the respiratory pathogen bioaerosol load, given sufficient filtration capacity.
The current study demonstrates that the type of pathogen, seasonality, ventilation and air filtration influence the respiratory pathogen bioaerosol load. However, many questions remain before multiplex qPCR on ambient air samples can become a new standard to surveil the community circulation of respiratory pathogens.
Firstly, technical aspects related to sampling need consideration. The type of sampler and its flow rate, sampling duration and the volume of the sampled room may all influence pathogen detection. We did not compare air samplers, but did use one with a comparably high flow rate42,43. Within the narrow range in our study, the sampling duration was not independently associated with pathogen detection or concentration (Supplementary Table 3).
Secondly, laboratory analysis methods need to be standardised and validated. In our study, we observed a difference in SARS-CoV-2 detection rates between qPCR platforms, with the ORF1ab aimed qPCR in the respiratory panel being significantly less sensitive than the TaqPath COVID-19 assay (Supplementary Methods and Supplementary Table 10). This lower sensitivity had been observed in validation experiments on clinical samples, but did not negatively impact accuracy in routine clinical practice (Supplementary Methods and Supplementary Table 11). Using the alternative SARS-CoV-2 qPCR as input had no impact on the main multivariate regression models (Supplementary Table 9). Their statistical power may actually have been higher, had the multiplex qPCR panel been more sensitive. Low sensitivity may however be particularly important when using inherently diluted air samples for epidemiological surveillance. For our study specifically, we cannot exclude an additional effect of the different transport buffers used for the TaqPath qPCR as opposed to the respiratory panel, or longer turn-around-times for the latter, on SARS-CoV-2 detection rates. Both were however analysed in a reasonable timeframe. The median processing time was 0.92 days (range 0.26 to 14.25, IQR 0.47–1.45) for the TaqPath qPCR and 3.32 days (range 0.79 to 16.23, IQR 2.02–5.22) for the multi-pathogen respiratory panel.
Thirdly, environmental/building related factors which were either not significant or not assessed in our study, may still need consideration. The influences of temperature and humidity on bioaerosol load, which were not significant in our models, are known to be pathogen specific and often non-linear44. As our main analyses used either positivity or concentration of any respiratory pathogen as primary outcome to identify linear relationships, they may not have captured the importance of these variables. UV radiation was not assessed in our study, and is unlikely to influence bioaerosol loads measured by qPCR, as it neutralises the replication potential of pathogens without physically removing their genetic material from the environment3. The presence of an HVAC system in the sampling sites was similarly not significant in our models. This may result from the large variation in installations across sites (Supplementary Table 7) or the fact that their effect was obscured by other variables, such as CO2 concentration. The absence of a significant association between occupancy and bioaerosol load could be similarly explained. Behavioural factors such as mask wearing and vocalisation were not retained in our models, even if they are known to have an important influence on aerosol generation16. Contradictorily, increased vocalisation was even associated with decreased pathogen detection, although it was removed as a significant variable after exclusion of observations with imputed variables. Possible reasons for both not being associated with higher bioaerosol load are a lack of power, the fact that they were the variables most often imputed in the dataset (Supplementary Table 2), and a confounder effect, as mask wearing may coincide with the implementation of other mitigation measures.
Lastly, the sensitivity of an air sample depends on its positioning and air mixing patterns in the room. Samplers were always placed off the ground and at maximum distance from attendants to avoid sampling resuspended aerosols or large exhaled droplets rather than airborne particles (Supplementary Table 1). Resuspension of pathogens that either survive intact or whose genetic material is most stable may indeed skew environmental surveillance data based on qPCR11. Similarly, the concentration of airborne pathogens is known to be greater in proximity to an infectious individual9,26,45. This stresses the importance of distancing the sampler from attendants, perhaps even within the HVAC system, which may also allow the inclusion of more individuals per sample43.
Our study has several limitations. First, we did not attempt to isolate replication-competent virus, relying exclusively on qPCR analysis, or collect biological samples from attendants. This limits our ability to link risk factors with the risk of transmission directly. Second, we did not determine the exact concentrations of respiratory pathogens in ambient air as no standard curves were developed for each pathogen and qPCR platform. While this does not negate the conclusions on significant variables, it does influence the transferability of effect sizes in terms of changes in qPCR Ct values to other settings not using the exact same qPCR panels. Third, natural and mechanical ventilation rates and airflows were not assessed directly or modelled comprehensively. This limits our ability to determine whether the proximity of attendants to the sampling device may have influenced bioaerosol detection and concentration. Lastly, neither our convenience sample of community settings nor the clinical samples from patients admitted for severe respiratory infections in the nearby hospital can be considered entirely representative of the locally circulating respiratory pathogens. Our study therefore did not allow to directly compare ambient air sampling, syndromic surveillance, or sentinel sampling of clinical samples at a local level.
In conclusion, these results provide strong empirical support for the use of ventilation and air filtration to reduce transmission risk, consistent with previous studies. They further demonstrate that ambient air qPCR testing can scale to surveil community circulation of respiratory pathogens, if confounders such as CO2 concentration are accounted for.
## Air sample collection
Between October 2021 and April 2022, we collected indoor ambient air in a convenience sample of community settings in and around the city of Leuven, Belgium. Sampling sites covered different predominant age groups: nursery (0–3 years), preschool (3–6 years), primary school (6–12 years), secondary school (12–18 years), adults (18+) and nursing homes (65+). See Supplementary Table 1 for detailed characteristics of the sampling sites and Supplementary Table 7 for descriptions of the HVAC systems present in six sites. We focused on children and older people because of high incidence and morbidity from respiratory infections in these populations40. For university auditoria, rooms where high CO2 values were registered in the weeks prior to the start of the study were selected for inclusion.
We sampled for 2 h unless site-specific schedules required shorter sampling (e.g. lunch time in schools). An AerosolSense active air sampler collected air in standard AerosolSense Capture Media (Thermo Fisher Scientific, Waltham, MA) (see Supplementary Table 1 for its positioning). This is an impaction-based active air sampler with multiple nucleic acid collection media. Air was sampled at a rate of 200 L/min through a vertical collection pipe and impacted onto the collection media. The flow rate of the AerosolSense sampler is calibrated continuously by measuring the pressure drop across the nozzle and calculating the mass flow rate for orifice, and adjusted through a PID controller. We measured environmental parameters such as CO2 and humidity either manually (registering the highest recorded value while holding a Testo 435-4 device at arm’s length for 20 s) or using a remote climate sensor (Elsys®, placed adjacent to the air sampler at maximum distance from attendants). We used the former for 58 samples and the latter for 283.
## Clinical sample collection
We retrieved the results of respiratory panels performed in patients at University Hospitals Leuven in the same period37. This hospital drains most patients in the wider Leuven region. Respiratory panels are only performed for specific clinical indications. In immunocompetent individuals, they are performed for respiratory infections that require intensive care admission or that do not respond to initial therapy. In immunocompromised patients, they are performed more readily in the presence of lower respiratory infections.
## Air sample processing and analysis
After removal of the standard AerosolSense Capture Media cartridges (Thermo Fisher Scientific, Waltham, MA) from the sampler, they were transported to the lab on the day of collection. One of two sponges was lysed in transport buffer (DNA/RNA Shield, Zymo Research) to be used for the TaqPath qPCR assay for SARS-CoV-2. The other was lysed in Universal Transport Medium (UTM), to be used for the multiplex qPCR respiratory panel. Samples were stored at 4 degrees until processing. If they required storage over the weekend, they were frozen at −80 degrees Celsius.
## Nucleic acid extraction
For the TaqPath qPCR analysis, we used the MagMAX™ Viral/Pathogen II (MVP II) Nucleic Acid Isolation Kit for automated extraction (Thermo Fisher Scientific, AM1836) on 200 μl sample input. For internal control, samples were spiked with a purified MS2 bacteriophage as per the manufacturer’s instructions (Thermo Fisher Scientific, A47817). Extracted RNA was eluted from magnetic beads in 50 μl MagMAX Viral/Pathogen Elution Buffer.
For the multiplex respiratory panel, Total Nucleic Acid (TNA) extraction started from 500 µl of air sample in UTM with NucliSens extraction reagents on easyMAG or eMAG (BioMérieux, Lyon, France). We used the specific B protocol on the instrument after off-board lysis for 10 min and continuous shaking. A 10 μL mixture of Phocine Distemper Virus and Phocine Herpesvirus was added to the lysed sample before extraction as RNA and DNA internal controls46,47. The elution volume of TNA was 110 µl.
## Detection of SARS-CoV-2 in air samples by RT-qPCR (TaqPath)
Extracted RNA was eluted from magnetic beads in 50 μl of UltraPure DNase/RNase free distilled water. RT-qPCR testing was performed with the TaqPath COVID-19 CE-IVD RT-PCR kit (Thermo Fisher Scientific). Results were analysed using the FastFinder analysis software (Ugentec, Belgium) and expressed as a cycle threshold (Ct) for the ORF1ab, N, and S gene targets (see also Cuypers et al.48).
## Detection of 29 respiratory pathogens in air samples by multiplex qPCR (respiratory panel)
An in-house respiratory panel, consisting of 12 real-time multiplex qPCRs, was run in 96 well plates on QuantStudio DX (Thermo Fisher Scientific, Waltham, MA, USA). The end volume of each PCR reaction mix was 20 µL: 5 µL of TNA, 5 µL of master mix (TaqMan Fast Virus Mix, Thermo Fisher Scientific, Waltham, MA, USA) and 10 µL of primer/probe mix (Supplementary Table 12). The temperature profile used was as follows: 50 °C for 10 min followed by 20 s at 95 °C and 45 cycles of 3 s at 90 °C and 30 s at 60 °C.
The panel detects seven non-viral pathogens (Mycoplasma pneumoniae, Coxiella burnettii, Chlamydia pneumoniae, Chlamydia psittaci, Streptococcus pneumoniae, *Legionella pneumophila* and Pneumocystis jirovecii) and twenty-two viruses: influenza A virus, influenza B virus; human parainfluenza viruses 1 to 4; respiratory syncytial virus A/B; human enterovirus (incl. rhinovirus); enterovirus D68; herpes simplex virus type 1; herpes simplex virus type 2; Human metapneumovirus; human adenovirus; human bocavirus; human parechovirus; Human coronaviruses 229E, HKU-1, NL63 and OC43; human cytomegalovirus; Middle East respiratory syndrome coronavirus (MERS-CoV); SARS-CoV-$\frac{1}{2}$ through the ORF1ab target. Since all positive results for ORF1ab were attributed to SARS-CoV-2, rather than SARS-CoV-1, the panel could detect 22 viruses and 29 pathogens in practice. Both an RNA (Phocine Distemper Virus) and DNA internal control (Phocine Herpesvirus-1) were run with each panel46,47. Two internal quality control samples (Respi 3 and Respi 4) were run on alternating days with the respiratory panel. This is standard practice in clinical routine. Respi 3 contains positive material for human bocavirus, Chlamydia psittaci, Human coronaviruses NL63, 229E and HKU-1, MERS-CoV, human enterovirus (incl. rhinovirus), enterovirus D68, herpes simplex virus type 1, Human metapneumovirus, influenza A virus, human parechovirus and respiratory syncytial virus A/B. Respi 4 contains positive material for human adenovirus, Chlamydia pneumoniae, *Human coronavirus* OC43, SARS-CoV-1/SARS-CoV-2 Wuhan, Coxiella burnettii, human cytomegalovirus, herpes simplex virus type 2, influenza B virus, Legionella pneumophila, Mycoplasma pneumoniae, Streptococcus pneumoniae, and Pneumocystis jirovecii, human parainfluenza viruses 2 and 3, respiratory syncytial virus A/B.
Supplementary Table 12 lists all target genes, primer/probe sequences and final concentrations, amplicon sizes and Ct thresholds.
The specificity was validated using External Quality Control (EQC) samples, cultures and clinical samples. The analysis was performed under ISO15189:2012 accreditation. Supplementary Methods and Supplementary Tables 11, 12 and 13 provide further details on experiments conducted to validate the respiratory panel in clinical practice and the methods used to exclude non-specific amplification in air samples.
Test results were downloaded from the University Hospitals Leuven laboratory information system as CSV files.
## Detection of 29 respiratory pathogens in clinical human respiratory samples by multiplex qPCR
Clinical samples which are analysed using the multiplex qPCR respiratory panel undergo the same procedure as air samples did in our study. They are transported to the laboratory in UTM immediately after collection. Lysis, storage, nucleic acid extraction and the detection of pathogens follow identical procedures.
Test results were downloaded from the University Hospitals Leuven laboratory information system as CSV files.
## Detailed definition of host, behavioural and environmental/building related factors collected for each sample
Weekly COVID-19 incidence Leuven: COVID-19 incidence for the city of Leuven in the seven days until the day before sampling, per 100,000 inhabitants49.The following variables were registered before and after each collected air sample:Predominant age group at the sampling site: 0–3 years, 3–6 years, 6–12 years, 12–18 years, 18–25 years, 25–65 years, +65 years. ( Supplementary Table 1).Month of sampling. Number of attendees, measured at the start and end of each sample and averaged. Attendee density: averaged number of attendees divided by sampling room volume (m3). The number of attendees was estimated by headcount both at the start and end of sampling and averaged per sample. Sampling duration: in minutes, manual entry per sample. Mask wearing: estimated by Likert scale (no one, almost no one, minority, majority, almost everyone, everyone) at the start and end of sampling. Average per sample. Vocalisation: estimated by Likert scale (no one talks, only teacher talks, minority talks, majority talks, everyone talks, singing) at the start and end of sampling. Average per sample. Natural ventilation: estimated by Likert scale (no natural ventilation, one window open, door open, multiple windows open, door and window open) at the start and end of sampling. Average per sample. Air filtration: binary (enabled, disabled), manual entry per sample. Mechanical ventilation: binary (absent/present), manual entry per site. See Supplementary Tables 1, 7.Indoor CO2 concentration: numeric (parts per million/ppm). Either measured manually at the start and end of each sample and averaged or measured continuously and averaged over the total sampling duration (<11 min before start of sampling until <11 min after end of sampling).Indoor temperature: numeric (degrees Celsius, °C). Either measured manually at the start and end of each sample and averaged or measured continuously and averaged over the total sampling duration (<11 min before start of sampling until <11 min after end of sampling).Relative humidity: numeric (%). Either measured manually at the start and end of each sample and averaged or measured continuously and averaged over the total sampling duration (<11 min before start of sampling until <11 min after end of sampling).
Manual data collection took place on paper, after which it was inputted in Excel version 16.68 (Microsoft®). Continuous measurements of ambient air parameters were collected on the web based platform (Grafana, Grafana Labs®) and downloaded as CSV files.
## Portable air filters
To test the effectiveness of portable air filters to reduce bioaerosol load, we placed them in two separate locations in a nursery (Locations 2 and 3). Another separate space was the control (Location 1). The same group of up to 20 toddlers and 1 to 4 caregivers occupied each location during sampling. No one was present over the weekends. Supplementary Fig. 4 shows the placement of air filters.
The study assessed two types of air filters. The Blue PURE 221 (Blueair®) is a HEPA and carbon filter-based device with a clean air delivery rate of 590 m3/h. From January 17 onwards, three devices were present in nursery location 2. On the first 7 days of air filtration in this location, the air was sampled without filtration, filtered for several hours, then sampled again with active filtration. From February 7 onwards, three Philips 3000i (Philips®) devices were additionally placed in location 3 (Supplementary Tables 1, 7). This is another HEPA and carbon filter-based device with a clean air delivery rate of at least 333 m3/h when operated in “turbo” mode as per manufacturer specifications. The devices were used in stage 2, which corresponds to a CADR of 186.7 m3/h per device. From this moment onwards, sampling took place concurrently in all three locations. On Mondays, air filtration started after the completion of 2 h of sampling. Air filtration then continued uninterrupted for 96 h. On Wednesdays and Fridays, sampling was repeated in each location, again for 2 h per day. Air filtration was discontinued after sampling on Fridays.
## Samples inclusion and exclusion
When describing pathogen detection patterns across sampling sites, age groups, and time, we considered each of the 29 target pathogens separately.
Only the TaqPath SARS-CoV-2 qPCR was considered for SARS-CoV-2, to avoid duplication and because it is more sensitive (see Supplementary Methods, Supplementary Tables 10, 13). The TaqPath qPCR was not performed on $\frac{35}{341}$ samples between January 3rd and 14th due to financial constraints. The TaqPath SARS-CoV-2 result was missing for one sample and the respiratory panel for two samples due to failed transport between labs.
When analysing the influence of pathogen, host, behavioural and environmental/building related factors on bioaerosol load, we excluded pathogens with less than 10 positive qPCR results after grouping them—to increase statistical power—as follows: human parainfluenza virus 1 to 4 under ‘parainfluenza viruses’; Human coronaviruses 229E, HKU-1, NL63 and OC43 under ‘other coronaviruses’.
Supplementary Table 2 lists the missing environmental/building related and behavioural factors and how the missing data was handled.
Supplementary Methods describes the procedure for imputing the missing variables.
For filtration, all datapoints from January 17th onwards were included in the main analyses assessing the influence of pathogen, host, behavioural and environmental/building related factors on bioaerosol load. For the interventional sub-study, only samples from February 7th onwards were included.
## Statistical analysis
To assess the influence of pathogen, host, behavioural and environmental/building related variables on pathogen detection, we used a logistic regression model, a generalised estimating equations model and a mixed effects logistic regression model. Each result for a particular pathogen was one observation, while positivity was the binary outcome. Each pathogen group had equal weight. The pathogen was a variable in the models. Both the generalised estimating equations model and mixed effects logistic regression model corrected for within-sample correlation of tests.
To assess the influence of the same variables on pathogen concentration, we used a linear regression model and mixed effects linear regression model. Pathogen concentration was measured by the qPCR Ct value of a positive pathogen. Again, each test for a particular pathogen was one observation, while the pathogen was considered a covariate in the model.
After imputing missing variables, as described in Supplementary Methods, we used backward elimination (until all remaining variables reached a p-value of < 0.05) in all models to estimate effect sizes of the most important variables. $95\%$ confidence intervals were computed as follows: coefficient estimate ± standard error * 1.96. We used the Wald test to estimate p-values in generalised estimating equations models and the Chi squared test for (mixed effects) linear and logistic regression models. P-values were not corrected for multiple hypothesis testing. After backward elimination, we removed observations with imputed variables to confirm the results.
In an exploratory analysis, we evaluated whether the influence of variables found to be significant in the above models differed by pathogen. We ran logistic and linear regression models with, respectively, pathogen detection and Ct value as outcomes. Models were run for each detected pathogen separately, only using the retained significant variables from the models including all pathogens.
Lastly, we evaluated the effectiveness of portable air filtration by focusing on repeated samples taken in the three nursery locations. We used a Cochran’s Q test to compare pathogen detection rates between three phases of air filtration for each location separately: no ongoing filtration (Mondays), 48 h of continuous filtration (Wednesdays) and 96 h of continuous filtration (Fridays). Pairwise Cochran’s Q tests followed when the difference in phases was significant. We used Holm correction for multiple testing. We used mixed effects linear regression models to evaluate the effect of different air filtration phases on pathogen concentration, including week and pathogen as random effects, for each location separately. Ct values for a particular pathogen were included only if the pathogen was detected in samples from all three filtration phases during the same week. Confidence intervals were calculated using the confint command in R, p-values were obtained using the Kenward–Roger approximation of the T-distribution (pbkrtest package in R50).
A two-sided p-value of ≤0.05 was considered significant in all analyses.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Peer Review File Supplementary Data 1 Supplementary Data 2 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-36986-z.
## Peer review information
Nature Communications thanks Benjamin Anderson, Linsey Marr and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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|
---
title: Effect of olmesartan and amlodipine on serum angiotensin-(1–7) levels and kidney
and vascular function in patients with type 2 diabetes and hypertension
authors:
- Kyuho Kim
- Ji Hye Moon
- Chang Ho Ahn
- Soo Lim
journal: Diabetology & Metabolic Syndrome
year: 2023
pmcid: PMC10005920
doi: 10.1186/s13098-023-00987-1
license: CC BY 4.0
---
# Effect of olmesartan and amlodipine on serum angiotensin-(1–7) levels and kidney and vascular function in patients with type 2 diabetes and hypertension
## Abstract
### Background
Recent studies suggest that angiotensin-converting enzyme 2 (ACE2) and angiotensin-(1–7) [Ang-(1–7)] might have beneficial effects on the cardiovascular system. We investigated the effects of olmesartan on the changes in serum ACE2 and Ang-(1–7) levels as well as kidney and vascular function in patients with type 2 diabetes and hypertension.
### Methods
This was a prospective, randomized, active comparator-controlled trial. Eighty participants with type 2 diabetes and hypertension were randomized to receive 20 mg of olmesartan ($$n = 40$$) or 5 mg of amlodipine ($$n = 40$$) once daily. The primary endpoint was changes of serum Ang-(1–7) from baseline to week 24.
### Results
Both olmesartan and amlodipine treatment for 24 weeks decreased systolic and diastolic blood pressures significantly by > 18 mmHg and > 8 mmHg, respectively. Serum Ang-(1–7) levels were more significantly increased by olmesartan treatment (25.8 ± 34.5 pg/mL → 46.2 ± 59.4 pg/mL) than by amlodipine treatment (29.2 ± 38.9 pg/mL → 31.7 ± 26.0 pg/mL), resulting in significant between-group differences ($$P \leq 0.01$$). Serum ACE2 levels showed a similar pattern (6.31 ± 0.42 ng/mL → 6.74 ± 0.39 ng/mL by olmesartan treatment vs. 6.43 ± 0.23 ng/mL → 6.61 ± 0.42 ng/mL by amlodipine treatment; $P \leq 0.05$). The reduction in albuminuria was significantly associated with the increases in ACE2 and Ang-(1–7) levels (r = − 0.252 and r = − 0.299, respectively). The change in Ang-(1–7) levels was positively associated with improved microvascular function ($r = 0.241$, $P \leq 0.05$). Multivariate regression analyses showed that increases in serum Ang-(1–7) levels were an independent predictor of a reduction in albuminuria.
### Conclusions
These findings suggest that the beneficial effects of olmesartan on albuminuria may be mediated by increased ACE2 and Ang-(1–7) levels. These novel biomarkers may be therapeutic targets for the prevention and treatment of diabetic kidney disease.
Trial registration: ClinicalTrials.gov NCT05189015.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13098-023-00987-1.
## Introduction
The renin-angiotensin system (RAS) plays important roles in the regulation of normal physiology and the pathogenesis of cardiovascular diseases (CVDs), including atherosclerosis, hypertension, myocardial infarction, and cardiac remodeling [1, 2]. The well-known components of “classical” RAS include angiotensinogen, angiotensin I, angiotensin II (AngII), renin, and angiotensin-converting enzyme (ACE). Among these, AngII, which is a major effector molecule, exerts its biological actions via the AngII type 1 receptor, contributing to the development of CVD [3].
There are “nonclassical” RAS pathways, which include ACE2, its product angiotensin-(1–7) [Ang-(1–7)], and Mas receptor [4]. These components are thought to have protective effects on CVDs, although the exact mechanisms underlying such effects are not completely understood [5]. In rodent models, ACE2, through catabolism of AngII [6–8] and Ang-(1–7) [9–12], showed beneficial effects on blood pressure (BP), atherosclerosis, cardiac remodeling, and heart failure. A few animal model studies suggested that ACE inhibitor or AngII receptor blocker (ARB) treatments can cause ACE2 upregulation with consequential beneficial effects on CVDs [13]. More specifically, a mice study showed that treatment with olmesartan, which is an ARB, inhibits cardiac hypertrophy independently of BP via AngII type 1 receptor blockade and partly through the enhancement of ACE2/Ang-(1–7)/*Mas axis* pathway [14]. A human study also showed that among many antihypertensive drugs, only olmesartan treatment led to significantly higher urinary ACE2 levels than those in the control group [15]. In that study, olmesartan was an independent predictor of urinary ACE2 levels, with potential additional renoprotective effects [15]. However, the study was an observational study with a small sample size, and it did not measure the plasma levels of ACE2 or Ang-(1–7).
Considering that ACE inhibitors or ARBs are the first-line drugs for antihypertensive treatment in patients with type 2 diabetes (T2D) and have potential “nonclassical” RAS-mediated benefits, it is meaningful to measure the change in the plasma levels of “nonclassical” RAS components after drug treatment. This could help clinicians select the more appropriate drugs between ACE inhibitors and ARBs with solid evidence.
Recently, ACE2 has received much attention because it can serve as an entry receptor for severe acute respiratory syndrome coronavirus 2 [16]. ACE2 is widely expressed in humans, including in the myocardium, vasculature, pancreas, kidneys, intestines, and lungs [17]. Alteration of ACE2 levels by RAS blocking agents can be associated with coronavirus disease 2019 (COVID-19) occurrence and its severity.
In the present study, we aimed to investigate the changes in serum ACE2 levels, Ang-(1–7) levels, and their association with kidney and vascular function after using olmesartan, compared with a conventional antihypertensive drug (amlodipine) in patients with T2D and hypertension.
## Study design
This was a 24-week, prospective, randomized, active comparator-controlled trial conducted at the Seoul National University Bundang Hospital. After 1:1 randomization, patients received 20 mg of olmesartan or 5 mg of amlodipine once daily. After 12 weeks, the dose was titrated to 40 mg of olmesartan or 10 mg of amlodipine once daily for the next 12 weeks for the participants with systolic blood pressure (SBP) > 160 mmHg or diastolic blood pressure (DBP) > 100 mmHg. During this study period, medications except for olmesartan and amlodipine were not changed or added. The study was registered at ClinicalTrials.gov (NCT05189015).
## Study participants
The inclusion criteria were age ≥ 30 years; T2D and glycated hemoglobin (HbA1c) levels = 6.5–$10.0\%$; SBP = 140–180 mmHg or DBP = 85–110 mmHg, considering a target BP of < $\frac{140}{85}$ mmHg for patients with T2D according to the guidelines of Korean Diabetes Association [18], to exclude patients with severe hypertension [19]; and no change in the dose of statins in the previous 3 months. The exclusion criteria were a history of taking RAS inhibitors (ACE inhibitors or ARBs) or calcium channel blockers (CCBs) in the previous 3 months, pregnancy, lactation, confirmed CVDs within 3 months of screening, active liver disease (aspartate transaminase/alanine transaminase levels > threefold of the upper limit of normal), hyperkalemia (serum potassium levels > 5.0 mEq/L), and any previous cancer within 5 years. Patients with serum creatinine levels > 2.0 mg/dL (advanced chronic kidney disease) were also excluded for their safety. Participants meeting all inclusion and none of the exclusion criteria were randomized to the study. Randomization was conducted sequentially as the participants became eligible.
## Study objectives
The primary objective of this study was to measure changes in serum Ang-(1–7) from baseline to week 24. The key secondary objectives were to measure the following: [1] changes in BP from baseline to week 24; [2] changes in serum ACE2 levels, plasma renin activity (PRA), and aldosterone from baseline to week 24; [3] changes in the ratio of urinary protein-to-creatinine concentration (UPCR, mg/g) or urinary albumin-to-creatinine concentration (UACR, mg/g); [4] changes in flow-mediated vasodilatation (FMD) and microcirculation from baseline to week 24; [5] changes in body mass index and body fat percentile from baseline to week 24; and [6] changes in glucose metabolism parameters (HbA1c, fasting plasma glucose [FPG], and insulin), lipid profiles (total cholesterol, triglycerides, high-density lipoprotein [HDL] cholesterol, and low-density lipoprotein [LDL] cholesterol), and high-sensitivity C-reactive protein (hsCRP) from baseline to week 24.
## Anthropometric parameters
Height was measured while wearing no shoes (in cm). Weight was measured while wearing light clothes and no shoes (in kg). Waist circumference (in cm) was measured midway between the lowest rib and the iliac crest, in the morning before having breakfast. Body mass index was calculated as weight (in kg) divided by the square of the height (in meters). Body composition was estimated using a multifrequency whole-body bioelectrical impedance analysis (InBody 720, InBody Co., Seoul, South Korea).
## Measurement of blood pressure and pulse
BP and heart rate were measured in a seated position with the arms raised to the level of the heart and in a supported position. One pulse measurement was taken after the participant had been sitting and resting for at least 5 min and before blood samples were taken. BP was measured using a standardized cuff adapted to the size of the participant’s arm.
## Laboratory assessments
Blood samples for laboratory measurement were collected after fasting for at least 8 h. Fasting plasma levels of glucose, total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol, and serum creatinine were measured using standard automated laboratory methods (Hitachi 747; Hitachi, Tokyo, Japan). The estimated glomerular filtration rate (eGFR) was calculated using the creatinine-based Chronic Kidney Disease Epidemiology Collaboration equation. HbA1c levels were measured using a high-performance liquid chromatography Variant II Turbo analyzer (Bio-Rad Laboratories, Hercules, CA, USA) in the National Glycohemoglobin Standardization Program level II certified laboratory at the Seoul National University Bundang Hospital. Plasma C-peptide and insulin levels were measured using a radioimmunoassay (RIA; Linco, St. Louis, MO, USA). Serum aspartate transaminase and alanine transaminase levels were measured using an autoanalyzer (TBA-200FR, Toshiba, Tokyo, Japan). Serum hsCRP levels were measured using an automated latex turbidimetric immunoassay method (CRP Latex X2, Denka Seiken, Tokyo, Japan). Urinary protein or albumin levels were measured using a turbidimetric assay (A&T 502X, A&T, Tokyo, Japan). Urinary creatinine levels were measured using the Jaffe method (Hitachi 7170, Hitachi, Tokyo, Japan). Proteinuria and albuminuria were assessed based on UPCR and UACR, respectively.
Insulin resistance (IR) index and pancreatic β-cell function (β) assessed from the homeostasis model assessment (HOMA) were calculated using the following formula: HOMA-IR = fasting plasma insulin (μU/mL) × FPG (mg/dL)/405; HOMA-β = 360 × fasting plasma insulin (μU/mL)/[FPG (mg/dL) − 63] [20].
PRA was measured using a PRA RIA kit (TFB Inc., Tokyo, Japan), and plasma aldosterone levels were measured using the SPAC-S aldosterone RIA kit (TFB Inc.). Serum levels of Ang-(1–7) and ACE2 were measured using ELISA kits (Human Angiotensin (1–7) ELISA kit Cat. No. MBS084052 and Human ACE2 ELISA kit Cat. No. MBS824839, respectively; MyBioSource, San Diego, CA, USA) according to the manufacturer’s instructions [21].
## Flow-mediated vasodilation
Endothelial-dependent FMD was measured using high-resolution ultrasonography according to the guidelines [22]. After supine rest for at least 5 min, a baseline rest image of the brachial artery was acquired. Then, the cuff was inflated to at least 50 mmHg higher than the SBP of the upper arm for 2 min. The longitudinal image of the artery was recorded continuously from 30 s before to 2 min after cuff deflation. The diameter of the artery was measured from one media-adventitia interface to the other. FMD change (%) was defined using the change in artery diameter between baseline and 1 min after cuff deflation.
## Microcirculation
To assess microvascular function, postocclusive reactive hyperemia (PORH) was measured using the PeriFlux 400 laser Doppler (Perimed, Stockholm, Sweden) [23]. The laser Doppler probe was applied at the dorsum of the foot between the first and second metatarsal bones. After the patients had rested for 10 min, a 4-min occlusion of the lower limb was performed using a cuff placed on the ankle. The pressure of the cuff was at least 50 mmHg above the SBP of the ankle. Then, the flow within 1 min after cuff deflation was recorded. PORH was expressed as arbitrary perfusion units, and PORH change (%) was defined using the change in PORH between baseline and 1 min after cuff deflation.
## Safety assessments
Safety assessments were performed throughout the study and included regular monitoring of physical examination, pregnancy evaluation, and clinical laboratory data, including an electrocardiogram. All and serious adverse events were recorded for all participants.
## Statistical analysis
No previous interventional studies investigating the effect of amlodipine or olmesartan on Ang-(1–7) levels exist. Therefore, we used the changes in ACE2 levels instead of Ang-(1–7) levels in the sample size calculation. In a previous study, urinary ACE2 levels were significantly increased by > $50\%$ after olmesartan treatment [24]. We assumed a $40\%$ increase in Ang-(1–7) levels after olmesartan treatment in a conservative manner. Moreover, no study investigating the effect of amlodipine on Ang-(1–7) or ACE2 levels exists. Considering that amlodipine is a CCB, no change in Ang-(1–7) or ACE2 levels after amlodipine treatment is expected. For this study, we assumed a $10\%$ increase in Ang-(1–7) levels after amlodipine treatment in a conservative manner. Based on this criterion, the sample size was calculated with the assumption of a $30\%$ intergroup difference in the changes in serum ACE2 levels from baseline to week 24 with a standard deviation (SD) of $15\%$, yielding 34 patients per group for a $90\%$ statistical power with α = 0.05. Assuming a $15\%$ dropout rate, a minimal sample size of 40 patients per group (1:1 randomization) was estimated to be required. Eligible participants at screening who met the inclusion criteria were randomly assigned into either the olmesartan or the amlodipine group aiming for an equal number of participants per treatment group. The randomization scheme in blocks was generated using IBM SPSS software, version 25.0 (IBM Corp., Armonk, NY, USA). Per-protocol analyses were performed except analyses of baseline characteristics and safety assessment of study participants.
Data were expressed as mean ± SD, median (25–75th percentile), or number (%) as indicated. Comparisons of continuous variables were performed using the two-sample t test (or Wilcoxon rank sum test) or paired t test (or Wilcoxon signed-rank test). Comparisons of categorical variables were performed using the chi-square test (or Fisher’s exact test) or McNemar’s test. Spearman’s and Pearson’s correlation analyses were used to evaluate the correlation between variables. Logarithmically transformed values of triglyceride, HOMA-IR, UACR, UPCR, hsCRP, Ang-(1–7), PRA, and PORH were used for statistical analysis. Using established risk factors for proteinuria or albuminuria and variables of interest, univariate regression analyses and stepwise multivariate regression analyses were performed to identify independent determinants for the changes in UPCR and UACR. $P \leq 0.05$ was considered statistically significant. Statistical analyses were performed using the IBM SPSS software, version 25.0 (Armonk, NY).
## Results
A total of 80 patients were randomized to the olmesartan ($$n = 40$$) or amlodipine ($$n = 40$$) group. Of these, 71 patients ($88.5\%$) completed 24 weeks: 36 in the olmesartan group and 35 in the amlodipine group (Additional file 1: Fig. S1). No differences in baseline clinical and biochemical characteristics were observed between the two groups (Table 1). The number of men was 23 and 30 in the olmesartan and amlodipine groups, respectively ($P \leq 0.05$). At the baseline, SBP was > 150 mmHg and DBP was > 85 mmHg in both groups. After 24 weeks of treatment, SBP decreased by 21.3 mmHg and 18.0 mmHg in the olmesartan and amlodipine groups, respectively, leading to no between-group difference in the extent of SBP change. Similarly, DBP decreased by 12.1 mmHg and 8.7 mmHg in the olmesartan and amlodipine groups, respectively, leading to no between-group difference in the extent of DBP change (Fig. 1 and Table 2). No difference was observed in the proportion of maximum dose in olmesartan ($36.1\%$) or amlodipine ($28.6\%$) groups ($P \leq 0.05$).Table 1Baseline characteristics of the study participantsOlmesartan ($$n = 40$$)Amlodipine ($$n = 40$$)PAge, year56.5 ± 14.956.3 ± 12.5NSBody mass index, kg/m226.0 ± 3.726.3 ± 3.5NSBody fat percentile, %30.2 ± 5.929.4 ± 6.3NSWaist circumference, cm91.4 ± 8.992.5 ± 8.6NSSystolic blood pressure, mmHg156.1 ± 15.4154.2 ± 12.6NSDiastolic blood pressure, mmHg89.7 ± 13.889.7 ± 10.4NSHeart rate, beats/min85.0 ± 12.785.3 ± 12.6NSFasting glucose, mg/dL146.8 ± 47.5161.0 ± 41.5NSPostprandial 2 h glucose, mg/dL250.5 ± 65.9237.9 ± 61.5NSHbA1c, %7.4 ± 1.47.5 ± 1.3NSC-peptide, ng/mL2.6 ± 1.22.7 ± 1.2NSInsulin, μIU/mL9.1 ± 5.19.8 ± 5.3NSTotal cholesterol, mg/dL182.3 ± 44.3178.8 ± 49.3NSTriglyceridea, mg/dL119.0 (97.3–265.3)166.5 (113.3–223.0)NSHDL-cholesterol, mg/dL50.0 ± 10.551.8 ± 13.0NSLDL-cholesterol, mg/dL111.0 ± 30.0106.2 ± 34.5NSAST, IU/L31.6 ± 14.233.9 ± 18.5NSALT, IU/L24.9 ± 21.838.5 ± 23.5NSeGFR, ml/min/1.73 m294.3 ± 25.999.0 ± 19.6NShsCRPa, mg/dL0.06 (0.03–0.18)0.06 (0.04–0.12)NSUACRa, mg/g29.5 (11.1–84.4)28.3 (11.0–148.0)NSUPCRa, mg/g109.8 (77.2–249.9)120.5 (86.8–275.8)NSMedication Metformin, N (%)36 (94.7)37 (92.5)NS Sulfonylurea, N (%)15 (39.5)15 (37.5)NS DPP-4 inhibitor, N (%)15 (39.5)14 (35.0)NS SGLT-2 inhibitor, N (%)3 (7.9)6 (15.0)NS GLP-1 receptor agonist, N (%)0 (0.0)1 (2.5)NS Thiazolidinedione, N (%)3 (7.9)2 (5.0)NSData are expressed as mean ± standard deviation (SD), median (25–75th percentile), or number (%). aP value obtained after log transformation of the data. eGFR estimated glomerular filtration rate; UACR urinary albumin-to-creatinine ratio; UPCR urinary protein-to-creatinine ratio, DPP-4 dipeptidyl peptidase-4, SGLT-2 sodium-glucose cotransporter-2, GLP-1 glucagon like peptide-1, NS not significantFig. 1Changes in blood pressure 12 and 24 weeks after treatment with olmesartan ($$n = 36$$) or amlodipine ($$n = 35$$). BP blood pressureTable 2Changes in biochemical parameters after treatment from baseline to week 24Olmesartan ($$n = 36$$)Amlodipine ($$n = 35$$)BaselineWeek 24Change*PBaselineWeek 24Change*P†PSystolic BP, mmHg156.9 ± 15.8135.6 ± 18.2 − 21.3 ± 19.5 < 0.001153.6 ± 12.2135.6 ± 11.3 − 18.0 ± 11.5 < 0.0010.395Diastolic BP, mmHg90.7 ± 14.078.6 ± 14.1 − 12.0 ± 14.0 < 0.00190.0 ± 10.681.3 ± 10.6 − 8.7 ± 11.3 < 0.0010.273Heart rate, beats/min84.9 ± 11.986.7 ± 14.41.8 ± 10.50.32585.3 ± 13.384.1 ± 12.7 − 1.3 ± 8.50.3900.191Body mass index, kg/m226.1 ± 3.626.1 ± 3.6 − 0.1 ± 1.00.62126.7 ± 3.526.5 ± 3.4 − 0.2 ± 0.60.0730.625Body fat percentile, %30.0 ± 5.529.6 ± 5.2 − 0.4 ± 2.10.23328.9 ± 5.328.9 ± 5.4 − 0.1 ± 1.50.8450.392Total cholesterol, mg/dL182.4 ± 42.6174.5 ± 44.7 − 7.9 ± 33.70.171173.3 ± 47.5169.3 ± 37.0 − 4.0 ± 30.60.4450.615Triglyceridea, mg/dL119.0 (97.3–207.0)132.5 (74.5–261.3)8.0 (–37.8–58.8)0.845164.0 (109.0–233.0)127.0 (110.0–206.0) − 11.0 (− 59.0–37.0)0.1700.245HDL-cholesterol, mg/dL50.6 ± 10.749.7 ± 11.2 − 0.9 ± 6.90.41651.4 ± 13.651.3 ± 14.7 − 0.1 ± 9.20.9560.657LDL-cholesterol, mg/dL111.3 ± 30.0101.9 ± 33.1 − 9.4 ± 22.40.016101.8 ± 32.1100.4 ± 31.6 − 1.4 ± 22.90.7250.138Fasting glucose, mg/dL144.9 ± 41.2142.5 ± 40.7 − 2.4 ± 45.10.749159.0 ± 42.3168.6 ± 54.29.6 ± 48.40.2490.282PP2, mg/dL253.9 ± 62.4243.1 ± 74.0 − 10.8 ± 90.70.478235.8 ± 63.3265.0 ± 83.229.2 ± 81.30.0410.055HbA1c, %7.3 ± 1.27.4 ± 1.30.1 ± 0.70.4377.2 ± 0.77.6 ± 1.30.3 ± 1.10.0750.279Insulin, μIU/mL8.9 ± 5.29.6 ± 4.50.7 ± 3.90.2739.9 ± 5.39.9 ± 5.30.0 ± 2.20.9880.341HOMA-IRa2.6 (1.9–5.0)3.1 (1.8–4.2)0.4 (–1.5–0.7)0.1893.2 (2.7–4.3)3.4 (2.5–5.1)0.2 (–1.0–0.7)0.3940.420HOMA-β49.2 ± 40.853.8 ± 39.24.6 ± 39.90.49752.3 ± 59.845.1 ± 43.7 − 7.2 ± 34.30.2230.188AST, IU/L32.0 ± 14.829.5 ± 12.0 − 2.5 ± 10.80.17435.0 ± 19.134.1 ± 17.7 − 0.9 ± 15.00.7380.598ALT, IU/L35.4 ± 22.935.2 ± 24.3 − 0.2 ± 18.00.94141.4 ± 23.848.5 ± 32.17.1 ± 21.30.0560.121Na, mEq/L140.3 ± 1.8139.8 ± 2.0 − 0.5 ± 1.90.113140.3 ± 1.9140.4 ± 2.00.1 ± 1.70.7610.158K, mEq/L4.3 ± 0.44.5 ± 0.40.2 ± 0.40.0084.5 ± 0.34.5 ± 0.30.1 ± 0.30.2410.005Cl, mEq/L102.8 ± 2.6102.7 ± 2.7 − 0.1 ± 2.60.748102.8 ± 2.0102.4 ± 2.1-0.4 ± 1.90.1980.593eGFR, ml/min/1.73m294.4 ± 22.595.6 ± 27.11.2 ± 16.80.671100.5 ± 18.0101.5 ± 17.51.0 ± 17.60.7380.962UACRa, mg/g29.5 (10.2–84.4)19.6 (8.9–44.0) − 3.9 (− 37.1–2.1)0.00535.0 (11.1–157.4)26.7 (10.0–206.4)–1.6 (–4.3–22.6)0.1550.002UPCRa, mg/g109.8 (76.9–249.9)113.5 (67.7–152.2)3.0 (− 54.0–38.4)0.249120.6 (93.4–292.3)133.9 (88.3–353.1)4.8 (–3.64–14.4)0.1420.047hsCRPa, mg/dL0.05 (0.03–0.13)0.06 (0.03–0.11)0.00 (–0.02–0.02)0.6210.06 (0.04–0.12)0.06 (0.04–0.12)0.00 (–0.02–0.01)0.4730.436Renin-angiotensin system Ang-(1–7)a, pg/mL14.7 (5.1–30.8)30.3 (15.2–51.7)10.7 (0.2–22.3) < 0.00118.8 (13.4–29.6)26.7 (13.7–38.1)3.3 (− 4.3–16.3)0.2120.010 ACE2, ng/mL6.31 ± 0.426.74 ± 0.390.43 ± 0.32 < 0.0016.43 ± 0.236.61 ± 0.420.18 ± 0.340.0030.002 PRAa, ng/ml/hr0.8 (0.5–1.7)4.2 (1.6–8.7)2.7 (0.3–6.8) < 0.0011.2 (0.6–1.9)1.4 (0.9–2.5)0.6 (–0.4–0.8)0.089 < 0.001 Aldosterone, ng/dL18.0 ± 7.813.5 ± 7.2 − 4.5 ± 7.70.00117.9 ± 6.019.0 ± 8.71.0 ± 7.00.3880.002Vascular function FMD baseline (mm)4.8 ± 0.64.7 ± 0.6 − 0.1 ± 0.40.4855.0 ± 0.75.0 ± 0.7–0.0 ± 0.40.5470.897 FMD after 1 min (mm)5.2 ± 0.65.2 ± 0.7 − 0.1 ± 0.50.7445.4 ± 0.75.4 ± 0.7–0.1 ± 0.40.2240.919 FMD change (%)9.7 ± 5.210.3 ± 7.50.6 ± 5.70.5199.2 ± 4.28.5 ± 4.1–0.7 ± 5.50.4420.317 PORH baseline (PU)17.4 ± 7.519.5 ± 9.42.1 ± 10.40.23417.4 ± 7.419.6 ± 7.72.2 ± 10.70.2400.981 PORH after 1 min (PU)56.7 ± 20.261.4 ± 31.94.6 ± 28.30.33262.1 ± 21.458.8 ± 33.9–3.3 ± 35.20.5830.298 PORH changea (%)223.2 (107.1–366.8)191.9 (122.1–343.6)–19.5 (–205.1–92.5)0.726261.3 (175.8–359.7)177.5 (105.3–253.4)–75.2 (–146.3–46.3)0.0260.189Data are expressed as mean ± standard deviation (SD) or median (25–75th percentile). aLog-transformed values were used for comparison. * P values were calculated using a paired t test between the values at the baseline and after treatment. †P values were calculated using Student’s t test for delta changes between the two groupsPP2, postprandial 2-h glucose; HOMA-IR, homeostasis model assessment of insulin resistance; HOMA-β, homeostasis model assessment of β-cell function; UACR, urinary albumin-to-creatinine ratio; UPCR, urinary protein-to-creatinine ratio; hsCRP, high-sensitivity C-reactive protein; PRA, plasma renin activity; ACE2, angiotensin-converting enzyme-2; FMD, flow-mediated vasodilation; PORH, postocclusive reactive hyperemia; PU, perfusion unit Notably, Ang-(1–7) levels, which were the primary endpoint of this study, increased significantly by olmesartan treatment but they did not change much by amlodipine treatment. Thus, the extent of changes was significantly different between the groups. Similarly, serum ACE2 levels increased significantly in both groups, but the increase was greater in the olmesartan group than in the amlodipine group, leading to a significant difference between the groups. In the olmesartan group, PRA increased significantly but aldosterone levels decreased significantly. These changes were not observed in the amlodipine group (Fig. 2 and Table 2).Fig. 2Changes in serum Ang-(1–7) and ACE2 levels 24 weeks after treatment with olmesartan ($$n = 36$$) or amlodipine ($$n = 35$$). ACE2 angiotensin-converting enzyme 2; Ang-(1–7), angiotensin-(1–7) In the assessment of FMD for vascular function, the FMDs at baseline and after 1-min stimulation did not change after 24-week olmesartan or amlodipine treatment. In the PORH measurement for the assessment of microvascular circulation, the baseline value increased in both groups. However, the stimulated value of PORH after 1 min of stimulation was greater in the olmesartan group than in the amlodipine group, leading to a significant decrease in PORH change (%) by amlodipine treatment but not by olmesartan treatment. Nevertheless, no difference was observed in the extent of change in PORH between groups (Table 2).
No differences were observed for body mass index, body fat percentile, fasting glucose, HbA1c, insulin, HOMA-IR, HOMA-β, lipid profiles, and hsCRP between the groups. However, 2-h postprandial glucose level was significantly increased in the amlodipine group while it was maintained in the olmesartan group, resulting in a borderline significant difference between the groups ($$P \leq 0.055$$). In addition, urinary albumin and protein excretion rates showed a tendency to decrease in the olmesartan group whereas they were not changed in the amlodipine group, resulting in a significant difference between the groups (Table 2).
In the correlation analysis using all participants, change in serum Ang-(1–7) levels was negatively associated with changes in UPCR (r = − 0.394, $$P \leq 0.001$$) and UACR (r = − 0.299, $$P \leq 0.011$$). Change in serum ACE2 levels was also negatively associated with a change in UACR (r = − 0.252, $$P \leq 0.034$$; Fig. 3). In vascular function assessment, change in serum Ang-(1–7) levels was positively associated with an increase in PORH change ($r = 0.241$, $$P \leq 0.043$$) but not with an increase in FMD change. Change in serum ACE2 levels was not associated with an increase in FMD change or PORH change (Additional file 1: Fig. S2).Fig. 3Correlation between Δ log Ang-(1–7) and Δlog UPCR or Δlog UACR, and between ΔACE2 and Δlog UPCR or Δlog UACR. ACE2 angiotensin-converting enzyme 2, Ang-(1–7) angiotensin-(1–7), UACR urinary albumin-to-creatinine ratio, UPCR urinary protein-to-creatinine ratio; ∆ change (value at week 24—value at baseline) In simple regression analyses, changes in UPCR were positively correlated with the changes in HOMA-IR and aldosterone levels but negatively correlated with the changes in fasting glucose and Ang-(1–7). Changes in UACR were positively correlated with the changes in aldosterone levels but negatively correlated with the changes in SBP, DBP, fasting glucose, HbA1c, Ang-(1–7), and ACE2 levels. In multivariate regression analyses, changes in HOMA-IR, Ang-(1–7), and aldosterone levels were the significant determinants of the changes in UPCR (all $P \leq 0.05$) (Table 3). In addition, changes in Ang-(1–7), fasting glucose, PRA, and aldosterone were the significant determinants of the changes in UACR (all $P \leq 0.05$).Table 3Univariate and multivariate regression analyses for changes in UPCR and UACRUnivariateMultivariateVariableCoefficientPStandardized coefficientP∆ log UPCR Age0.0040.478–– ∆ Systolic BP − 0.0070.129–– ∆ Diastolic BP − 0.0030.525–– ∆ Fasting glucose − 0.0030.021–– ∆ HbA1c − 0.0920.232–– ∆ log HOMA-IR0.3330.0050.2580.010 ∆ eGFR0.0030.427–– ∆ log hsCRP0.1120.199–– ∆ log Ang-(1–7) − 0.2290.001 − 0.3500.001 ∆ ACE20.0000.320–– ∆ log PRA − 0.0420.480–– ∆ Aldosterone0.033 < 0.0010.406 < 0.001Model R2 = 0.400∆ log UACR Age0.0010.988–– ∆ Systolic BP − 0.0190.004–– ∆ Diastolic BP − 0.0230.005–– ∆ Fasting glucose − 0.0060.004 − 0.2480.015 ∆ HbA1c − 0.2320.045–– ∆ log HOMA-IR0.3570.053–– ∆ eGFR − 0.0010.911–– ∆ log hsCRP0.1580.223–– ∆ log Ang-(1–7) − 0.2650.011 − 0.2020.040 ∆ ACE2 − 0.0010.034–– ∆ log PRA − 0.1520.090 − 0.2650.008 ∆ Aldosterone0.052 < 0.0010.452 < 0.001Model R2 = 0.426UPCR urinary protein-to-creatinine ratio, UACR urinary albumin-to-creatinine ratio, HOMA-IR homeostasis model assessment of insulin resistance; eGFR estimated glomerular filtration rate, hsCRP high-sensitivity C-reactive protein, ACE2 angiotensin-converting enzyme-2, PRA plasma renin activity; R2 multiple coefficient of determination, ∆ change (the value at week 24 minus the value at baseline) Treatments with both olmesartan and amlodipine were generally well tolerated. The incidence of adverse events was not different between groups ($5.0\%$ vs. $5.0\%$). No serious adverse events were observed in both groups. One case of dizziness and one case of hypotension were reported in the olmesartan group. One case of dizziness and one case of pruritus were reported in the amlodipine group (Additional file 1: Table S1).
## Discussion
In this randomized controlled trial, both olmesartan and amlodipine treatment for 24 weeks decreased SBP and DBP significantly, without any between-group differences. However, olmesartan treatment reduced urinary protein- or albumin-excretion rates, but amlodipine did not, leading to significant differences between the two groups. Notably, Ang-(1–7) and ACE2 levels increased more in the olmesartan group than in the amlodipine group. The increases in Ang-(1–7) and ACE2 levels were significantly correlated with the decrease in albuminuria. Based on the multivariate regression models, the increase in Ang-(1–7) levels was associated with a reduction in proteinuria and albuminuria. The increase in Ang-(1–7) levels was also associated with improvement in microcirculation measured using PORH change. These findings support the significant beneficial effects of olmesartan on the “nonclassical” RAS pathways that include Ang-(1–7) and ACE2.
It has been suggested that “nonclassical” RAS pathways, such as those involving ACE2, its product Ang-(1–7), and Mas receptor, might play a protective role in the cardiovascular continuum [5]. The cardiovascular and renal systems are the major sources of Ang-(1–7) production [25]. Ang-(1–7) and ACE2 have multifaceted effects on the heart and kidney, including vasodilatation, positive inotropic effects, myocardial protection, and inhibition of unfavorable cardiac remodeling and inflammation and fibrosis in kidneys [26, 27]. However, the favorable effect of Ang-(1–7) on the heart and blood vessels was only demonstrated in a preclinical study [5].
Regarding the effects of RAS blockade on Ang-(1–7) levels, previous studies showed that the ACE inhibitor captopril increased Ang-(1–7) levels in the tissue and plasma in rodents [28, 29] and plasma in humans [30]. Subcutaneous captopril treatment (5 mg/kg per 24 h) for 72 h significantly increased brain Ang-(1–7) levels in rats with focal cerebral ischemia [28]. In addition, oral captopril treatment (4.2 mg/kg) for 7 days significantly increased plasma Ang-(1–7) levels in rats [29]. In a human study, captopril treatment for 6 months significantly increased plasma Ang-(1–7) levels in participants with essential hypertension [30]. However, the effects of ARB on Ang-(1–7) level has not been investigated either in human or animal models. Our study is the first study that showed that an ARB, olmesartan, significantly increased plasma level of Ang-(1–7) in patients with diabetes and hypertension.
Few studies investigated the changes in ACE2 levels by treatment with RAS blocking agents. A hypertensive mouse model study showed that ACE inhibitor or ARB treatments increased ACE2 levels in tissue samples [13]. In a study with patients with type 1 diabetes, ACE inhibitor treatment increased serum ACE2 levels [31]. In contrast, another human study reported that circulating ACE2 levels were not changed by ACE inhibitor or ARB treatments [32]. This inconsistent finding might be explained by different study populations, such as those with heart failure [33], coronary artery disease [34], and myocardial infarction [35].
The relationship between serum Ang-(1–7) or ACE2 and albuminuria has not yet been studied in humans. A 2-week administration of Ang-(1–7) in hypertensive rats decreased proteinuria [36], while Ang-(1–7) receptor Mas knockout mice showed glomerular hyperfiltration and albuminuria [37]. The administration of recombinant human ACE2 attenuated albuminuria in a mouse model of diabetic nephropathy [38]. In addition, candesartan treatment for 4 weeks ameliorated albuminuria in a db/db mouse model, which was associated with increased plasma ACE2 levels [39]. In the present study, the increases in serum Ang-(1–7) and ACE2 levels as a result of olmesartan treatment were associated with a reduction in albuminuria, which is in accordance with the results of previous human and animal studies [14, 15]. In addition, multivariate regression analyses showed that increases in serum Ang-(1–7) levels were an independent predictor of a reduction in albuminuria.
Moreover, ACE2 levels might be related to an increased risk of morbidity and mortality in COVID-19. It was reported that severe acute respiratory syndrome coronavirus 2 binds to ACE2 and is internalized. This in turn leads to a downregulation of ACE2, which subsequently promotes AngII production [40], leading to an increased risk of CVDs [41]. ACE2 levels might also be involved in the association between COVID-19 and diabetes mellitus [17]. For example, ACE2-knockout mice were vulnerable to high-fat diet-induced pancreatic β-cell dysfunction [42]. Based on this finding, the increase in ACE2 levels by some RAS blocking agents, such as olmesartan, might help prevent or mitigate the development of CVDs in patients with COVID-19.
In the present study, endothelial function was measured using the FMD method and microcirculation was evaluated using PORH. These surrogate indices for vascular health were not significantly altered by olmesartan treatment. This may be explained by a relatively short period of treatment (24 weeks). It is noteworthy that the increase in Ang-(1–7) levels was positively correlated with improvement in PORH, possibly indicating the involvement of Ang-(1–7) in vascular health.
Previous studies have suggested beneficial effects of ARBs on metabolism and inflammation [43, 44]. On the contrary, our findings showed no significant changes in metabolic and inflammatory parameters after olmesartan treatment. LDL-cholesterol levels decreased in the olmesartan group but not in the amlodipine group, thereby leading to no between-group difference. Postprandial glucose levels were decreased in the olmesartan group while they were increased in the amlodipine group, resulting in a borderline significant between-group difference.
The overall incidence of adverse events was similar in both groups. The most commonly reported adverse effects of amlodipine and olmesartan were peripheral edema [45] and dizziness [46], respectively. In the present study, peripheral edema was not reported in both groups. However, one participant in the olmesartan group reported dizziness.
The present study has several strengths. To our knowledge, this study was the first randomized controlled trial study that investigated the effect of an ARB (olmesartan) and a CCB (amlodipine) on serum levels of Ang-(1–7) and ACE2. Albuminuria and vascular functions were also evaluated before and after treatment.
## Conclusion
Olmesartan treatment showed a significantly greater increase in the serum Ang-(1–7) and ACE2 levels, compared with amlodipine treatment. The increases in these novel RAS pathway parameters were correlated with a reduction in urinary albumin excretion in patients with T2D and hypertension. These findings suggest that Ang-(1–7) and ACE2 can be used as therapeutic targets for the prevention and treatment of diabetic kidney disease. Future definitive studies are needed to verify whether the effects of olmesartan on these novel RAS parameters would mediate renoprotective benefits.
## Supplementary Information
Additional file 1: Fig. S1. Study flow-chart. Fig. S2. Correlation between Δ log Ang-(1–7) and Δ FMD change or Δ PORH change, and between Δ ACE2 and Δ FMD change or Δ PORH change. ACE2 angiotensin-converting enzyme 2, Ang-(1–7) angiotensin [1-7]; FMD flow-mediated vasodilatation; PORH post-occlusive reactive hyperemia, ∆ change (value at week 24— value at baseline). Table S1. Number of patients with adverse events.
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|
---
title: Genetic, individual, and familial risk correlates of brain network controllability
in major depressive disorder
authors:
- Tim Hahn
- Nils R. Winter
- Jan Ernsting
- Marius Gruber
- Marco J. Mauritz
- Lukas Fisch
- Ramona Leenings
- Kelvin Sarink
- Julian Blanke
- Vincent Holstein
- Daniel Emden
- Marie Beisemann
- Nils Opel
- Dominik Grotegerd
- Susanne Meinert
- Walter Heindel
- Stephanie Witt
- Marcella Rietschel
- Markus M. Nöthen
- Andreas J. Forstner
- Tilo Kircher
- Igor Nenadic
- Andreas Jansen
- Bertram Müller-Myhsok
- Till F. M. Andlauer
- Martin Walter
- Martijn P. van den Heuvel
- Hamidreza Jamalabadi
- Udo Dannlowski
- Jonathan Repple
journal: Molecular Psychiatry
year: 2023
pmcid: PMC10005934
doi: 10.1038/s41380-022-01936-6
license: CC BY 4.0
---
# Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder
## Abstract
Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain’s large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control *Theory is* emerging as a powerful tool to quantify network controllability—i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, $$n = 692$$) and healthy controls ($$n = 820$$). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.
## Introduction
Complex network theory conceptualizes the brain as a dynamical system which depends on the interactions between distributed brain regions [1]. Accordingly, the brain can be viewed as an intricate network of brain regions that synchronize their activity via anatomical and functional connections. Based on this, mathematical graph theory is utilized to gain insights into the underlying organizational principles of the brain [2, 3] and its topological organization in health and disease [4, 5]. For example, reduced global fractional anisotropy (FA) has been associated with remission status of depressive patients, while FA in connections between frontal, temporal, insular, and parietal regions was found to be negatively associated with symptom severity [6, 7]. Cross-disorder connectome analyses have further revealed disruptions in connections central to global network communication and integration, emphasizing the involvement of the connectome in a wide range of mental health and neurological conditions [8]. In addition, machine learning on graphs—for example graph convolutional networks or reinforcement learning-based graph dismantling [9]—is emerging as a fruitful extension of classical graph analysis.
While classic connectome analysis has yielded tremendous insights into the topological organization of the brain in health and disease, it does not advance our ability to actively manipulate and control the brain. It is, however, this very ability to control the large-scale dynamics of the brain which facilitates virtually all therapeutic interventions in psychiatry [10, 11]. In short, any intervention—from medication to psychotherapy—can be conceptualized as an attempt to control the large-scale, dynamic network state transitions in the brain [1, 12, 13]. Control Theory as the study and practice of controlling dynamical systems is ubiquitous in medicine and biology [14], framing any intervention—from the optimization of cancer chemotherapy [15, 16] and the design of artificial organs [17] to real-time drug administration and non-pharmaceutical pandemic defense strategies [18]—as a control problem.
Integrating Control Theory and network neuroscience, recent progress in Network Control Theory has enabled the quantification of the influence a brain region has on the dynamic transitions between brain states [1, 12]. This so-called controllability of a brain region is linked to its structural connectivity properties which constrain or support transitions between different brain states [19, 20] and has been strongly related to a multitude of cognitive domains [20]. Controllability of a brain region is commonly captured by two key metrics: On the one hand, average controllability measures the ability of a system to spread and amplify the control inputs and is thus indicative of the node’s ability to support low-energy state transitions. On the other hand, modal controllability represents the ability to control especially fast decaying neural dynamics [21]. For formal definitions of average and modal controllability measures see Methods section.
Elucidating the variation and effect of controllability in mental disorders is of particular interest as controlling large-scale state transitions in the brain that underly cognition and behavior is at the heart of all therapeutic interventions in psychiatry [1, 22]. Fueled by evidence that the human brain is in principle controllable [19] and the recently discovered associations with cognition [20, 23], studies with small to moderate patient sample sizes have begun to investigate network controllability in mental disorders. First, Jeganathan et al. [ 24] showed altered controllability in young people with bipolar disorder ($$n = 38$$) and those at high genetic risk ($$n = 84$$) compared to healthy controls ($$n = 96$$). Likewise, Braun et al. [ 25] showed altered network control properties in schizophrenia patients ($$n = 24$$) as compared to ($$n = 178$$) healthy controls. Of note, Parkes et al. [ 26] investigated the association between average controllability and negative and positive psychosis spectrum symptoms in a large sample of youths between 8 and 22 years of age. Related to mental disorders, Kenett et al. [ 27] showed regional associations between controllability and subclinical depressive symptoms as measured using the Beck Depression Inventory [28] in healthy controls.
Building on these advances, we provide a comprehensive characterization of individual variation in average and modal controllability regarding demographic, disease-related, genetic, personal, and familial risk in Major Depressive Disorder (MDD). First, we assess the effect of age and gender on average and modal controllability. Then, we compare average and modal controllability between healthy controls and MDD patients and test whether these measures vary with age, gender, current symptom severity, or remission status. Second, we assess whether average and modal controllability in MDD patients are associated with polygenic scores for MDD [29], Bipolar Disorder [30], and psychiatric cross-disorder [31] risk as well as with familial risk of MDD and bipolar disorder. Finally, we quantify the effects of body mass index as a personal risk factors previously reported to be associated with brain-structural deviations in MDD on average and modal controllability [32, 33].
## Sample
Participants were part of the Marburg-Münster Affective Disorders Cohort Study (MACS) [34] and were recruited at two different sites (Marburg & Münster, Germany). See [35] for a detailed description of the study protocol. Participants ranging in age from 18 to 65 years were recruited through newspaper advertisements and local psychiatric hospitals. All experiments were performed in accordance with the ethical guidelines and regulations and all participants gave written informed consent prior to examination. To confirm the psychiatric diagnosis or a lack thereof, the Structural Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV Text Revision (DSM-IV-TR) (SCID-I [36];) was used. MDD subjects were included with current acute depressive episodes and partial or full remission from depression. Patients could be undergoing in-patient, out-patient, or no current treatment at all. Exclusion criteria comprised the presence of any neurological abnormalities, history of seizures, head trauma or unconsciousness, severe physical impairment (e.g. cancer, unstable diabetes, epilepsy etc.), pregnancy, hypothyroidism without adequate medication, claustrophobia, color blindness, and general MRI contraindications (e.g. metallic objects in the body). Only Caucasian subjects were included in the analyses. Further, lifetime diagnoses of schizophrenia, schizoaffective disorder, bipolar disorder, or substance dependence posed reason for exclusion. After excluding subjects according to the aforementioned exclusion criteria, DTI data for 1567 subjects were available. 55 subjects were excluded due to poor DTI quality (see below for a detailed description of the quality assurance procedure). Final samples of $$n = 692$$ MDD patients and $$n = 820$$ healthy controls were used for the controllability analyses. See Table 1 for a sample description of sociodemographic and clinical data. Table 1Sample summary. CharacteristicMDDaHCap($$n = 692$$)($$n = 820$$)Sociodemographic Gender451 female, 241 male529 female, 291 male<0.001b Age, years36.41 ± 13.1333.96 ± 12.75<0.001cQuestionnaires BDI17.67 ± 11.064.02 ± 4.18<0.001cClinical Depressive episodes3.86 ± 6.28–– Duration of illness, years10.27 ± 9.75––Medication Medication load1.32 ± 1.47–– CPZ24.77 ± 89.21––HC healthy control group, MDD patient group with major depression disorder, BDI sum score based on 21 items, CPZ chlorpromazine-equivalent doses.aNumbers present either absolute numbers or mean plus standard deviation.bχ2-test (two-tailed).ct-test (two-tailed).
## Imaging data acquisition
In the MACS Study, two MR scanners were used for data acquisition located at the Departments of Psychiatry at the University of Marburg and the University of Münster with different hardware and software configurations. Both T1 and DTI data were acquired using a 3 T whole body MRI scanner (Marburg: Tim Trio, 12-channel head matrix Rx-coil, Siemens, Erlangen, Germany; Münster: Prisma, 20-channel head matrix Rx-coil, Siemens, Erlangen, Germany). A GRAPPA acceleration factor of two was employed. For DTI imaging, fifty-six axial slices, 2.5 mm thick with no gap, were measured with an isotropic voxel size of 2.5 × 2.5 × 2.5 mm³ (TE = 90 ms, TR = 7300 ms). Five non-DW images (b0 = 0) and 2 × 30 DW images with a b-value of 1000 sec/mm² were acquired. Imaging pulse sequence parameters were standardized across both sites to the extent permitted by each platform. For a description of MRI quality control procedures see [35]. The body coil at the Marburg scanner was replaced during the study. Therefore, a variable modeling three scanner sites (Marburg old body coil, Marburg new body coil and Münster) was used as covariate for all statistical analyses.
## Imaging data preprocessing
Connectomes were reconstructed involving the following steps [37]. For a more detailed description of the preprocessing see [6]. In accordance with [6], we decided on using a basic DTI reconstruction rather than more advanced diffusion direction reconstruction methods to provide a reasonable balance between false negative and false positive fiber reconstructions [38]. For each subject an anatomical brain network was reconstructed, consisting of 114 areas of a subdivision of the FreeSurfer’s Desikan–Killiany atlas [39, 40], and the reconstructed streamlines between these areas. White matter connections were reconstructed using deterministic streamline tractography, based on the Fiber Assignment by Continuous Tracking (FACT) algorithm [41]. Network connections were included when two nodes (i.e., brain regions) were connected by at least three tractography streamlines [42]. For each participant, the network information was stored in a structural connectivity matrix, with rows and columns reflecting cortical brain regions, and matrix entries representing graph edges. Edges were only described by their presence or absence to create unweighted graphs.
## DTI quality control
In accordance with [6], measures for outlier detection included 1. average number of streamlines, 2. average fractional anisotropy, 3. average prevalence of each subject’s connections (low value, if the subject has “odd” connections), and 4. average prevalence of each subjects connected brain regions (high value, if the subject misses commonly found connections). For each metric the quartiles (Q1, Q2, Q3) and the interquartile range (IQR = Q3–Q1) was computed across the group and a datapoint was declared as an outlier if its value was below Q1 − 1.5*IQR or above Q3 + 1.5*IQR on any of the four metrics.
## Genotyping and calculation of polygenic scores
Genotyping was conducted using the PsychArray BeadChip (Illumina, San Diego, CA, USA), followed by quality control and imputation, as described previously [43, 44]. In brief, quality control and population substructure analyses were performed in PLINK v1.90 [45], as described in the Supplementary Methods. The data were imputed to the 1000 Genomes phase 3 reference panel using SHAPEIT and IMPUTE2.
For the calculation of polygenic risk scores (PRS; [46]), single-nucleotide polymorphism (SNP) weights were estimated using the PRS-CS method [47] with default parameters. This method employs Bayesian regression to infer PRS weights while modeling the local linkage disequilibrium patterns of all SNPs using the EUR super-population of the 1000 Genomes reference panel. The global shrinkage parameter φ was determined automatically (PRS-CS-auto; CD: φ = 1.80 × 10−4, MDD: φ = 1.11 × 10−4). The PRS were calculated, using these weights, in PLINK v1.90 on imputed dosage data based on summary statistics of genome-wide association studies (GWAS) by the Psychiatric Genomics Consortium (PGC) containing 162,151 cases and 276,846 controls for a cross-disorder phenotype [31] and 59,851 cases and 113,154 controls for MDD [29]. PRS were available for 637 of the 692 MDD patients.
## Network controllability analysis
To assess the ability of a certain brain region to influence other regions in different ways, we adopt the control theoretic notion of controllability. Controllability of a dynamical system refers to the possibility of driving the state of a dynamical system to a specific target state by means of an external control input [48]. A state is defined as the vector of neurophysiological activity magnitudes across brain regions at a single time point. In this paper, following the established model of structural brain controllability [19], we assume the system to follow a descrete noise-free linear time-invariant model as in Eq. 1.1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x\left({k + 1} \right) = Ax\left(k \right) + Bu\left(k \right)$$\end{document}xk+1=Axk+Bukwhere x represents the temporal activity of the 114 brain regions, A is the adjacency matrix whose elements quantify the structural connectivity between every two brain regions, B is the input matrix and u shows the control strategy. Classic results in control theory ensure that the system in Eq. 1 is from the set of nodes K controllable, when the controllability Gramian matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$W_K = \mathop {\sum}\nolimits_{$i = 0$}^\infty {A^iB_KB_K^T(A^T)^i} $$\end{document}WK=∑$i = 0$∞AiBKBKT(AT)i is invertible (T denotes matrix transpose). A rigorous mathematical formulation of network controllability in brain networks can be found in [19]. From the Gramian matrix, different controllability measures can be computed for each node (brain region) in the network. Here, based on previous research of network controllability in brain networks, we compute for each participant and each brain region their average controllability and modal controllability as defined in [19].
Average controllability, estimated as the trace of the controllability Gramian matrix i.e. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Tr(W_j)$$\end{document}Tr(Wj) where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$B = e_j$$\end{document}B=ej is the jth canonical vector (all elements except for the jth item are zero)., is a measure of the ability of brain regions to spread the control inputs. Thus, regions with high average controllability can be used to drive the brain towards a greater number of reachable and nearby states. Previous work has identified brain regions that demonstrate high average controllability, such as the precuneus, posterior cingulate, superior frontal, paracentral, precentral, and subcortical structures [19].
Modal controllability (MC) is a measure of the ability of brain regions to control the fast decaying modes of brain activity and thus those states that are intuitively more difficult to reach. Mathematically, MC is estimated as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi _j = \mathop {\sum}\nolimits_n^N {\left[{1 - \xi _n^2\left(A \right)} \right]v_{nj}^2} $$\end{document}ϕj=∑nN1−ξn2Avnj2 where ξj and vnj represent respectively the eigenvalues and elements of the eigenvector matrix of A \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({$$n = 114$$} \right)$$\end{document}$$n = 114$.$ Previous work has identified brain regions that demonstrate high modal controllability, such as the postcentral, supramarginal, inferior parietal, pars orbitalis, medial orbitofrontal, and rostral middle frontal cortices [19].
Building on these definitions, we estimate single node controllability measures (average and modal controllability) and the whole-brain controllability is then defined as the average of single node controllability metrics over all nodes.
## Statistical analyses
Our analysis process is as follows (Fig. 1): Based on DTI data (Fig. 1a), we defined anatomical brain networks by subdividing the entire brain into 114 anatomically distinct brain regions (network nodes) in a commonly used anatomical atlas [39, 40]. Following prior work (see “*Imaging data* preprocessing”), we connected nodes (brain regions) by the number of white matter streamlines which results in sparse, undirected structural brain networks for each participant (Fig. 1b). Next, a simplified model of brain dynamics was applied to simulate network control and quantify average and modal controllability for each brain region for each participant, as described in [19, 48]. Figure 1c illustrates the dynamic state transitions of the brain over time. Note that in our analyses, a brain state is characterized not by three, but 114 values per time point, corresponding to the 114 regions contained in the atlas. Points in this space (colored points in Fig. 1c) thus correspond to brain states at different time points. Controllability parameters are, in turn, related to the ease with which a given brain region can induce dynamic state transitions in this space (see “Network controllability analysis”).Fig. 1Analysis Overview. From Diffusion Tensor *Imaging data* (a) we derived the structural connectivity matrix for each participant (b) and quantified modal and average controllability—i.e., the influence a brain region has on the dynamic transitions between brain states underlying cognition and behavior (c). We then investigated their association with genetic, familial, and personal risk (d).
We then analyzed mean whole-brain controllability as well as regional (i.e., per-node) controllability (dependent variable) using an ANCOVA approach with age, gender, MRI scanner site, and the number of present edges as covariates. For all analyses involving PRS, we also controlled for ancestry (first three MDS components). Also, we removed outliers defined as values located more than three standard deviations from the mean. In all analyses involving MDD patients only, we additionally controlled for medication load in accordance with previous publications [6, 49]. We report partial η2 as effect size for all whole-brain analyses and provide the $95\%$ confidence interval based on 1000 draws with replacement. Note that, in the regional analyses, we controlled for multiple comparisons by calculating the false discovery rate [50] with a false-positive rate of 0.05. All other constructs (age, gender, diagnosis, symptom severity, familial risk of MDD and BD as well as polygenic scores for MDD, BD, and cross-disorder risk) were tested independently and not corrected further for multiple testing.
## Demographic effects
First, we examined whether chronological age and gender are associated with controllability as has been shown before [23, 51]. Indeed, we find that the whole-brain average controllability was negatively correlated with age for both healthy controls (F[1,811] = 24.47, $p \leq 0.001$, ηp2 = 0.029292 [0.012970, 0.051426]) and MDD patients (F[1,686] = 15.08, $p \leq 0.001$, ηp2 = 0.021505 [0.007166, 0.043462]). Likewise, the regional average controllability significantly varied with age in 30 and 35 different regions for healthy controls and MDD patients, respectively (all $p \leq 0.05$, FDR-corrected; for a full list of regions for all analyses yielding significant regional associations, see Supplementary Results Tables S1 to S16). Whole-brain modal controllability was positively correlated with age for both healthy controls (F[1,811] = 3.93, $$p \leq 0.048$$, ηp2 = 0.004821 [0.000101, 0.016141]) and showed a similar trend in MDD patients (F[1,685] = 2.91, $$p \leq 0.089$$, ηp2 = 0.004229 [0.000124, 0.015783]). Regional modal controllability significantly varied with age in 33 regions for both healthy controls and MDD patients (all $p \leq 0.05$, FDR-corrected). Gender was not significantly associated with whole-brain average controllability for healthy controls (F[1,814] = 0.04, $$p \leq 0.839$$, ηp2 = 0.000051 [0.000004, 0.004761]) or MDD patients (F[1,687] = 0.08, $$p \leq 0.773$$, ηp2 = 0.000121 [0.000003, 0.006214]). In contrast, regional average controllability significantly varied with gender in 12 and 13 regions for healthy controls and MDD patients, respectively (all $p \leq 0.05$, FDR corrected). Whole-brain modal controllability was higher in males than in females for healthy controls (F[1,814] = 7.58, $$p \leq 0.006$$, ηp2 = 0.009231 [0.001672, 0.022200]) and showed a similar trend in MDD patients (F[1,687] = 3.73, $$p \leq 0.054$$, ηp2 = 0.005397 [0.000222, 0.019111]). Regional modal controllability significantly varied with age in 16 and 18 regions for healthy controls and MDD patients, respectively (all $p \leq 0.05$, FDR corrected).
## Disease-related variation
Focusing specifically on controllability in MDD, we show that patients displayed lower whole-brain modal controllability (F[1,1505] = 7.96, $$p \leq 0.005$$, ηp2 = 0.005261 [0.001227, 0.012531]) than healthy controls. Correspondingly, we observed a non-significant trend towards higher whole-brain average controllability values in MDD patients (F[1,1505] = 3.08, $$p \leq 0.080$$, ηp2 = 0.002041 [0.000056, 0.007247]).
In contrast to previous findings in sub-clinically depressed controls [27], our results do not support an effect of current symptom severity, as measured by the Beck Depression Inventory, on the whole-brain average (F[1,671] = 0.19, $$p \leq 0.665$$, ηp2 = 0.000279 [0.000005, 0.006632]) or modal controllability (F[1,671] = 1.49, $$p \leq 0.222$$, ηp2 = 0.002222 [0.000038, 0.011509]) in MDD patients. In line with this observation, the remission status was neither associated with average (F[2,683] = 0.43, $$p \leq 0.649$$, ηp2 = 0.001264 [0.000188, 0.013090]) nor modal controllability (F[2,683] = 0.07, $$p \leq 0.935$$, ηp2 = 0.000196 [0.000229, 0.009890]) on the whole-brain or regional level in MDD patients. For direct comparison with the previous publication, we also analyzed the healthy controls only: Again, we did not find a significant association between current symptom severity and whole-brain average (F[1,793] = 0.86, $$p \leq 0.355$$, ηp2 = 0.001080 [0.000014, 0.008154]) or modal controllability (F[1,793] = 2.32, $$p \leq 0.128$$, ηp2 = 0.002923 [0.000053, 0.012674]).
## Genetic and familial risk factors
Next, we examined whether controllability in MDD patients is associated with familial risk of MDD and Bipolar Disorder. We show that average controllability was significantly higher in patients carrying self-reported familial risk of MDD (F[1,685] = 4.87, $$p \leq 0.028$$, ηp2 = 0.007064 [0.000485, 0.022823]), mirroring the trend-wise increased average controllability of MDD patients compared to healthy controls. This was not the case for modal controllability (F[1,685] = 2.40, $$p \leq 0.122$$, ηp2 = 0.003492 [0.000070, 0.015817]).
Average controllability was also higher in patients carrying a familial risk of Bipolar Disorder (F[1,685] = 10.30, $$p \leq 0.001$$, ηp2 = 0.014809 [0.002123, 0.038855]) with regional effects in the right supramarginal gyrus, right inferior parietal gyrus, and precuneus. Likewise, whole-brain modal controllability was lower in patients carrying a familial risk of Bipolar Disorder (F[1,685] = 9.69, $$p \leq 0.002$$, ηp2 = 0.013951 [0.002281, 0.033644]) with regional effects in the right supramarginal gyrus (for detailed regional analyses, see the Supplementary Tables S15 and S16).
Building on this evidence, we extended the analysis to polygenic risk scores and show that polygenic risk scores for MDD [26] were negatively associated with modal controllability (F[1,624] = 4.88, $$p \leq 0.028$$, ηp2 = 0.007757 [0.000446, 0.024640]). Likewise, polygenic risk scores for cross-disorder risk [28] were also negatively associated with whole-brain modal controllability (F[1,623] = 4.17, $$p \leq 0.042$$, ηp2 = 0.006650 [0.000322, 0.021361]). In addition, we show that polygenic risk scores for MDD [26] were positively correlated with average controllability (F[1,623] = 3.86, $$p \leq 0.050$$, ηp2 = 0.006164 [0.000287, 0.021261]). Polygenic risk scores for cross-disorder risk [28] were not significantly associated with whole-brain average controllability (F[1,622] = 0.99, $$p \leq 0.320$$, ηp2 = 0.001590 [0.000012, 0.011372]).
In contrast to the observed effect for familial risk of Bipolar Disorder, we neither found a significant association of average (F[1,624] = 0.21, $$p \leq 0.644$$, ηp2 = 0.000342 [0.000006, 0.007621]) nor modal controllability (F[1,623] = 0.00, $$p \leq 0.990$$, ηp2 = 0.000000 [0.000011, 0.007404]) with polygenic risk score for Bipolar Disorder [30].
## Body mass index
With mounting evidence pointing towards brain-structural deviations relating body mass index and MDD [32, 33], we examined the effects of body mass index on controllability. For average controllability, we found associations in 9 regions ($p \leq .05$, FDR corrected) including negative correlations in the left superior frontal and posterior cingulate gyrus as well as positive correlations in the superior temporal and left lingual gyrus (see Supplementary Tables S13 und S14). With positive and negative regional associations, a whole-brain effect was absent (F[1,643] = 0.31, $$p \leq 0.579$$, ηp2 = 0.000478 [0.000008, 0.007147]). Analyses of modal controllability revealed the involvement of 6 regions ($p \leq 0.05$, FDR-corrected) showing a similar set of regions including the left superior frontal, posterior cingulate, and superior temporal gyrus with—as expected—a switched direction of correlations and, again, no consistent whole-brain effect (F[1,684] = 1.10, $$p \leq 0.294$$, ηp2 = 0.001611 [0.000014, 0.011596]).
To assess the specificity of our results, we additionally tested for the association with height and participant order of inclusion in the study. Neither height (modal controllability: F[1,1410] = 0.88, $$p \leq 0.349$$, ηp2 = 0.000622 [0.000008, 0.004492]) nor order of inclusion (F[1,1410] = 0.41, $$p \leq 0.522$$, ηp2 = 0.000290 [0.000004, 0.003831]) showed any significant effects on the whole-brain or on the regional level.
## Discussion
Building on Network Control Theory, we investigated the association of average and modal network controllability with genetic, familial, and individual risk in MDD patients ($$n = 692$$) and healthy controls ($$n = 820$$). First, we established that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we showed that modal and average controllability in MDD patients could be predicted based on polygenic scores for MDD and psychiatric cross-disorder risk as well as associations with familial risk of MDD and bipolar disorder. Finally, we provide evidence that controllability varies with body mass index. This evidence suggests that individual differences in these variables either impact the brain’s control architecture (e.g., in the case of genetic effects) or are driven by it—as may be the case for e.g. body mass index.
Against this background, our results indicate that individual differences in demographic, disease-related, genetic, individual, and familial risk factors are associated with controllability. We replicated previous findings showing that age and gender affected controllability measures [23, 51] also for MDD patients. Given that women are disproportionally affected by MDD, future studies might investigate gender differences in more detail.
Interestingly, associations were mainly found with whole-brain controllability—modal and average alike—suggesting subtle changes in how effectively not only single regions, but a larger set of regions in the brain can drive state transitions. This is of particular interest as previous studies have focused on the set of 30 regions with the highest controllability defined a priori, thereby potentially obscuring such whole-brain effects. This suggests that extending current controllability analyses towards the investigation of sets of regions controlling the brain (as has been done by, e.g., [52]) might be fruitful also for MDD. Moreover, all results were corrected for the number of present edges, which suggests a specific control effect that goes beyond basic graph properties.
More fundamentally, the question regarding the biological underpinnings of the control theoretic concepts has to be addressed: *To this* end, He et al. showed that control theoretic constructs are directly linked to gray matter integrity, metabolism, and energetic generation in the brain [53]. Specifically, they showed in temporal lobe epilepsy patients that higher control energy is required to activate the limbic network compared to healthy volunteers. The energetic imbalance between ipsilateral and contralateral temporolimbic regions was tracked by asymmetric patterns of glucose metabolism measured using positron emission tomography, which, in turn, was selectively explained by asymmetric gray matter loss. This work provides the first theoretical framework unifying gray matter integrity, metabolism, and energetic generation in a control theoretic framework. In addition, controllability has been associated with cognition [20, 23] and numerous studies have empirically investigated the two metrics in mental disorders other than MDD [24–26].
From the more general perspective of control, answering what changes in the brain after a specified stimulation event and which regions are most effective or efficient to stimulate is crucial for all therapeutic interventions. First attempts to achieve these goals in the context of electrical brain stimulation have recently shown promising results [54, 55]. In this context, our results imply that individual characteristics may be relevant when designing future interventions based on Network Control Theory. In turn, our results suggest that variation in response to treatment—e.g., with transcranial magnetic stimulation or electroconvulsive therapy—might be explained by controllability differences arising from demographic, disease-related, genetic, personal, and familial risk. Future studies may therefore investigate whether interventions guided by Network Control Theory are more effective or efficient than current approaches.
Several limitations should be noted. First, calculation of average and modal controllability relies on the simplified noise-free linear discrete-time and time-invariant network model employed in virtually all work on brain Network Control Theory [19, 22, 56]. Given the brain’s clearly non-linear dynamics, this is justified as 1) nonlinear behavior may be accurately approximated by linear behavior [57] and 2) the controllability of linear and nonlinear systems is related such that a controllable linearized system is locally controllable in the nonlinear case (see also [19] for details).
Second, our estimation of controllability is based upon Diffusion Tensor Imaging (DTI) tractography which in itself is limited in its ability to accurately quantify the structural connectome (for an introduction, see [58]). Currently, several novel approaches to controllability quantification are being explored including estimation from gray matter [59] and resting-state functional dynamics [56]. Empirically comparing and theoretically reconciling results from these methods will be crucial for robust parameter estimation in Network Control Theory studies of the brain. In addition, longitudinal data from DTI, gray matter, and resting-state functional dynamics available from, e.g., the Marburg-Münster Affective Disorders Cohort Study (MACS [35];) will enable us to assess the (differential) reliability of these approaches. In combination with functional Magnetic Resonance Imaging, this approach also provides an opportunity to further characterize the relationship between network control and individual task-related activation [60].
Third, it should be noted that most effect sizes observed in this study were small. Methodologically, however, it has been shown that small samples systematically inflate the apparent effect size, whereas large samples such as this one provide a much more accurate estimate of the true effect size [61]. Most importantly, our characterization of individual differences in controllability in MDD does not consider isolated effects but is supported by a broad range of analyses.
In summary, we build on a growing body of literature studying cognition and psychopathology within the framework of Network Control Theory to show effects of demographic, disease-related, genetic, personal, and familial risk on modal and average controllability in MDD patients. Thereby, we hope to aid future studies employing Network Control Theory to predict treatment response, guide therapeutic planning, and design novel interventions for MDD.
## Supplementary information
Supplementary Material The online version contains supplementary material available at 10.1038/s41380-022-01936-6.
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---
title: 'Brain ageing in schizophrenia: evidence from 26 international cohorts via
the ENIGMA Schizophrenia consortium'
authors:
- Constantinos Constantinides
- Laura K. M. Han
- Clara Alloza
- Linda Antonella Antonucci
- Celso Arango
- Rosa Ayesa-Arriola
- Nerisa Banaj
- Alessandro Bertolino
- Stefan Borgwardt
- Jason Bruggemann
- Juan Bustillo
- Oleg Bykhovski
- Vince Calhoun
- Vaughan Carr
- Stanley Catts
- Young-Chul Chung
- Benedicto Crespo-Facorro
- Covadonga M. Díaz-Caneja
- Gary Donohoe
- Stefan Du Plessis
- Jesse Edmond
- Stefan Ehrlich
- Robin Emsley
- Lisa T. Eyler
- Paola Fuentes-Claramonte
- Foivos Georgiadis
- Melissa Green
- Amalia Guerrero-Pedraza
- Minji Ha
- Tim Hahn
- Frans A. Henskens
- Laurena Holleran
- Stephanie Homan
- Philipp Homan
- Neda Jahanshad
- Joost Janssen
- Ellen Ji
- Stefan Kaiser
- Vasily Kaleda
- Minah Kim
- Woo-Sung Kim
- Matthias Kirschner
- Peter Kochunov
- Yoo Bin Kwak
- Jun Soo Kwon
- Irina Lebedeva
- Jingyu Liu
- Patricia Mitchie
- Stijn Michielse
- David Mothersill
- Bryan Mowry
- Víctor Ortiz-García de la Foz
- Christos Pantelis
- Giulio Pergola
- Fabrizio Piras
- Edith Pomarol-Clotet
- Adrian Preda
- Yann Quidé
- Paul E. Rasser
- Kelly Rootes-Murdy
- Raymond Salvador
- Marina Sangiuliano
- Salvador Sarró
- Ulrich Schall
- André Schmidt
- Rodney J. Scott
- Pierluigi Selvaggi
- Kang Sim
- Antonin Skoch
- Gianfranco Spalletta
- Filip Spaniel
- Sophia I. Thomopoulos
- David Tomecek
- Alexander S. Tomyshev
- Diana Tordesillas-Gutiérrez
- Therese van Amelsvoort
- Javier Vázquez-Bourgon
- Daniela Vecchio
- Aristotle Voineskos
- Cynthia S. Weickert
- Thomas Weickert
- Paul M. Thompson
- Lianne Schmaal
- Theo G. M. van Erp
- Jessica Turner
- James H. Cole
- Rosa Ayesa-Arriola
- Rosa Ayesa-Arriola
- Stefan Du Plessis
- Yoo Bin Kwak
- Víctor Ortiz-García de la Foz
- Therese van Amelsvoort
- Theo G. M. van Erp
- Danai Dima
- Esther Walton
journal: Molecular Psychiatry
year: 2022
pmcid: PMC10005935
doi: 10.1038/s41380-022-01897-w
license: CC BY 4.0
---
# Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium
## Abstract
Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18–72 years; $67\%$ male) and 2598 healthy controls (mean age 33.8 years, range 18–73 years, $55\%$ male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years ($95\%$ CI: 2.91, 4.19; I2 = $57.53\%$) compared to controls, after adjusting for age, sex and site (Cohen’s $d = 0.48$). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.
## Introduction
Schizophrenia (SZ) is associated with an increased risk of premature mortality, with an average decrease in life expectancy of ~15 years [1–3]. This is partially accounted for by suicidal behaviour or accidental deaths, as well as poor somatic health, including cardiovascular and metabolic disease [4–6]. The high prevalence of physical morbidity, long-term cognitive decline, and excess mortality seen in SZ may partly be the result of “accelerated” ageing (i.e., a biological age which “outpaces” chronological age) [7–9]. An increasing number of studies report systemic, age-related biological changes in SZ patients, including elevated levels of oxidative stress, inflammation, and cytotoxicity [10, 11]. There is also evidence for progressive brain changes in gray and white matter structures that may begin around or after illness onset [12–18], which may, in part, reflect deviations from normal brain ageing trajectories.
Although chronological age can be predicted accurately with neuroimaging data using machine learning, discrepancies can occur between brain-predicted age (also known as “brain age”) and chronological age [19]. This can be referred to as brain-predicted age difference (brain-PAD). A brain-PAD larger than zero indicates a brain that appears “older” than the person’s chronological age, whereas a brain-PAD lower than zero reflects a “younger” brain than expected at a given chronological age. Higher brain-PAD scores have been associated with a wide range of health-related lifestyle factors and outcomes, including smoking, higher alcohol intake, obesity (or higher BMI), cognitive impairments, major depression, type 2 diabetes, and early mortality [20–25].
To our knowledge, only a few studies have investigated brain age in adults with SZ using various machine learning algorithms or imaging (gray and/or white matter) measures. A higher brain-PAD was consistently shown in SZ patients relative to healthy individuals, with reported scores varying from +2.6 to 7.8 years across studies [26–31]. Furthermore, a greater brain-PAD was observed in first-episode SZ patients [26], and longitudinal data suggests that this gap widens predominantly during the first years after illness onset [29]. As these prior studies were performed with relatively small to moderate sample sizes (range: 43–341 patients), it is important to examine whether brain age findings in SZ can be generalised through large-scale studies consisting of many independent samples worldwide. Two recent mega-analyses with up to 1110 SZ patients across multiple cohorts found a moderate increase in brain-PAD derived from structural T1-weighted MRI (Cohen’s $d = 0.51$) [32] and diffusion tensor imaging (Cohen’s $d = 0.29$) [33], respectively. Validation of those findings, as well as identifying which clinical characteristics or other factors may underlie advanced brain ageing in SZ, could have diagnostic and prognostic implications for patients.
Here, we set out to investigate brain age in over 5000 individuals from the Schizophrenia Working Group within the Enhancing Neuro-Imaging Genetics through Meta-analysis (ENIGMA) consortium (26 cohorts, 15 countries), covering almost the entire adult lifespan (18–73 years). We employed a recently developed multisite brain ageing algorithm based on FreeSurfer-derived gray matter regions of interest (ROIs) [24] to examine brain-PAD differences between SZ patients and healthy controls in a prospective meta-analysis. We hypothesised significantly higher brain-PAD in SZ patients, compared to controls. In addition, we assessed whether a higher brain-PAD in SZ patients was associated with clinical characteristics, such as age of onset, length of illness, symptom severity, and antipsychotic treatment.
## Study samples
Twenty-six cohorts from the ENIGMA SZ working group with cross-sectional data from SZ patients ($$n = 2803$$) and healthy controls ($$n = 2598$$) were included in this study (18–73 years of age). Details of demographics, location, clinical characteristics (including methods for data harmonization), and inclusion/exclusion criteria for each cohort may be found in Supplementary Information (Supplementary Tables S1–3, Supplementary Fig. S1, and Supplementary Material). All sites obtained approval from the appropriate local institutional review boards and ethics committees, and all study participants provided written informed consent.
## Image acquisition and pre-processing
Structural T1-weighted brain MRI scans of each participant were acquired at each site. We used standardized protocols for image analysis and feature extraction (Nfeatures = 153) across multiple cohorts (http://enigma.ini.usc.edu/protocols/imaging-protocols/). FreeSurfer [34] was used to segment and extract volumes bilaterally for 14 subcortical gray matter regions (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus), 2 lateral ventricles, along with 68 regional cortical thickness and 68 regional cortical surface area measures, and total intracranial volume (ICV). Cortical parcellations were based on the Desikan/Kiliani atlas [35]. Segmentations were visually inspected and statistically examined for outliers. Further details of image acquisition parameters, software descriptions, and quality control may be found in Supplementary Table S4 and Supplementary Material.
## Brain age prediction
We used the publicly available ENIGMA brain age model (https://photon-ai.com/enigma_brainage). As described and discussed in Han et al. [ 24], brain age models were developed separately for males and females. The training samples were based on structural brain measures from 952 males and 1236 female healthy individuals (18–75 years of age) from the ENIGMA Major Depressive Disorder (MDD) group. There is no known participant overlap between the training samples and the participant data used in this work. Briefly, FreeSurfer measures from the left and right hemispheres were combined by calculating the mean ((left + right)/2)) of volumes for subcortical regions and lateral ventricles, and thickness and surface area for cortical regions, resulting in 77 features. The 77 average structural brain measures were used as predictors in a multivariable ridge regression to model chronological age in the healthy training samples (separately for males and females), using the Python-based sklearn package [36]. Model performance was originally validated in training samples (through 10-fold cross-validation) and out-of-sample controls. Here, the parameters from the previously trained model(s) were applied to our test samples of healthy controls and SZ patients (and separately for males and females) to obtain brain-based age estimates for each cohort. To assess the model’s generalization performance in the test control samples, we calculated the [1] mean absolute error (MAE) between predicted brain age and chronological age, the [2] Pearson correlation coefficients between predicted brain age and chronological age (r), and [3] the proportion of chronological age variance explained by the model (R2). For more detailed information on the training samples, model development/validation, and generalisation performance in the current samples, see Supplementary Material and Han et al. [ 24].
## Statistical analyses
Brain-PAD (predicted brain-based age minus chronological age) was calculated for each participant and used as the outcome variable. While different prediction models were built for males and females, the generated brain-PAD values were pooled across sex for subsequent statistical analyses within each cohort. Each dependent measure of the ith individual was modelled as follows:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathrm{brain}}}}}}}} - {{{{{{{\mathrm{PAD}}}}}}}}_{{{{{{{\mathrm{i}}}}}}}} = \, {{{{{{{\mathrm{intercept}}}}}}}} + \beta 1\left({{{{{{{{\mathrm{Dx}}}}}}}}_{{{{{{{\mathrm{i}}}}}}}}} \right) + \beta 2({{{{{{{\mathrm{sex}}}}}}}}_{{{{{{{\mathrm{i}}}}}}}}) + \beta 3\left({{{{{{{{\mathrm{age}}}}}}}}_{{{{{{{\mathrm{i}}}}}}}}} \right)\\ + \beta 4\left({{{{{{{{\mathrm{age}}}}}}}}_i^2} \right) + \beta 5\left({{{{{{{{\mathrm{site}}}}}}}}_{{{{{{{\mathrm{i}}}}}}}}} \right) + \varepsilon _{{{{{{{\mathrm{i}}}}}}}}$$\end{document}brain−PADi=intercept+β1Dxi+β2(sexi)+β3agei+β4agei2+β5sitei+εiwhere Dx represents diagnostic status for SZ. We corrected for the well-documented systematic age bias in brain age prediction (see Supplementary Material for brief explanation of this issue) [37, 38], as well as for potential confounding effects of age and sex in our test samples, by adding age, quadratic age (age2), and sex as covariates to our statistical models. We included both linear and quadratic age covariates in the same model as this provided a significantly better model fit to previous data compared with models including a linear age covariate only [24]. In addition, and where applicable, multiple scanning sites/scanners were added as (n-1) dummy variables.
Within SZ patients, we also used linear models to examine associations between brain-PAD and clinical characteristics (CC):2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathrm{brain}}}}}}}} - {{{{{{{\mathrm{PAD}}}}}}}}_{{{{{{{\mathrm{i}}}}}}}} = {{{{{{{\mathrm{intercept}}}}}}}} + \beta 1({{{{{{{\mathrm{CC}}}}}}}}_{{{{{{{\mathrm{i}}}}}}}}) + \beta 2\left({{{{{{{{\mathrm{age}}}}}}}}_{{{{{{{\mathrm{i}}}}}}}}} \right) + \beta 2\left({{{{{{{{\mathrm{age}}}}}}}}_i^2} \right) + \varepsilon _{{{{{{{\mathrm{i}}}}}}}}$$\end{document}brain−PADi=intercept+β1(CCi)+β2agei+β2agei2+εiwhere “CC” represents either age of onset, illness duration (time from age-of-onset to time of scanning), SZ symptomatology at study inclusion (including Scale for the Assessment of Negative Symptoms—SANS Global, Scale for the Assessment of Positive Symptoms—SAPS Global, and Positive and Negative Syndrome Scale – PANSS Total), antipsychotic (AP) medication use at time of scanning (typical/atypical/both/none) or chlorpromazine (CPZ) dose equivalents (mg per day). Analyses were also repeated while additionally covarying for handedness (right/left/ambidextrous) or parental socioeconomic status (see Supplementary Material). Cohorts with less than 10 healthy controls and less than 5 participants in a particular predictor or covariate subgroup (e.g., sex, clinical characteristics) were excluded from the analyses (see Supplementary Material for more details).
Cohort-specific results were then meta-analysed using the rma function in the metafor package [39]. Random (or mixed) effects models were fitted using restricted maximum likelihood estimation and inverse-variance weighting. Statistical tests were two-sided, and results for the effects of nine clinical characteristics among SZ patients were false discovery rate (FDR) corrected (using the Benjamini-Hochberg procedure) and considered statistically significant at α < 0.05. In addition, as cohorts differed in age or sex distribution, or, had multiple scanning sites (ASRB, FBIRN, Huilong, MCIC, MPRC, PAFIP) or different MRI scanners, post-hoc meta-regressions were performed to explore between-study heterogeneity in effect size with respect to the number of scanning sites (i.e., single vs. multi-site status), scanner field strength (i.e., 1.5 T vs. 3 T MRI), mean sample age or percentage of females (across cases and controls).
Finally, to better understand the contribution or importance of individual structural brain measures for making brain age predictions, we calculated Pearson’s correlation coefficients between brain-predicted age and each of the 77 FreeSurfer features in each cohort. A weighted average by sample size across cohorts was then calculated for each correlation coefficient and plotted on cortical maps for illustrative purposes only. Correlation analyses were also conducted separately for SZ patients and healthy controls.
## Sample characteristics
Demographics and clinical characteristics across cohorts can be found in Table 1. Mean age weighted by sample size (range) across SZ patient and healthy control cohorts was 34.22 (18.36–43.66) and 33.82 (22.58–41.41) years, respectively. Patient and control cohorts were on average $67.32\%$ (43.75–100) males and $54.89\%$ (38.46–100) males, respectively. Weighted mean age of onset and duration of illness across patient cohorts were 24.75 (17.55–29.99) and 10.83 (0.62–18.87) years. Mean symptom severity (PANSS total) was 62.41 (33.38–93.12). For cohorts where current antipsychotic medication type information was available, the weighted mean percentage of patients on first-generation (typical), second-generation antipsychotics (atypical), both typical and atypical, or no antipsychotic medication was $10.05\%$, $67.65\%$, $14.73\%$ and $7.57\%$, respectively. Table 1Participant characteristics for patients and controls across cohorts. CharacteristicWeighted mean (range)aKSZHCMean % males$67.32\%$ (43.75–100)$54.89\%$ (38.46–100)$\frac{26}{25}$Mean age in (years)34.22 (18.36–43.66)33.82 (22.58–41.41)$\frac{26}{25}$Mean age of onset (in years)24.75 (17.55–29.99)-21/-Mean duration of illness (in years)10.83 (0.62–18.87)-21/-Mean symptom severity (PANSS total)62.41 (33.38–93.12)-20/-Mean SANS global7.94 (3.64–14.06)-22/-Mean SAPS global6.72 (1.41–12.53)-21/-Antipsychotic medicationb21/- Mean % Atypical$67.65\%$ (0.00–93.00)- Mean % Typical$10.05\%$ (0.00–90.24)- Mean % Both atypical & typical$14.73\%$ (0.00–100)- Mean % None$7.57\%$ (0.00–53.62)-Mean CPZ-equivalent dose414.30 (167.88–1367.94)-19Handedness$\frac{20}{19}$ Mean % Right$91.15\%$ (81.16–100)$91.05\%$ (81.82–100) Mean % Left$6.00\%$ (0.00–14.49)$6.45\%$ (0.00–18.18) Mean % Ambidextrous$2.85\%$ (0.00–11.1)$2.49\%$ (0.00–11.67)SZ patients, HC healthy controls, K data available for K number of cohorts, SANS Scale for the Assessment of Negative Symptoms, SAPS Scale for the Assessment of Positive Symptoms, PANSS Positive and Negative Syndrome Scale, CPZ chlorpromazine.aUnless otherwise specified, means are weighted by the number of participants per group (SZ or HC)/cohort. For continuous variables, range indicates the smallest and largest mean value across cohorts. For categorical variables (percentages), range indicates the smallest and largest proportion of participants in each category across cohorts.bMean percentages are weighted based on the number of cases with recorded antipsychotic type at each cohort.
## Brain age prediction performance
In controls, the weighted average MAE across cohorts was 7.60 (SE = ± 0.40) and 8.45 (SE = ± 0.46) years for males and females, respectively (Supplementary Fig. S2a, b). Within the SZ group, the MAE was 10.14 (SE = ± 0.52) and 9.61 (SE = ± 0.54) years for males and females, respectively (Supplementary Fig. S2c, d). Correlations between chronological age and predicted brain age were moderate to large in controls (males $r = 0.64$, and females $r = 0.63$; both R2 = 0.41), and in SZ patients (males $r = 0.58$, and females $r = 0.62$; both R2 = 0.33) (Supplementary Fig. S3a–d).
## Brain age differences between SZ and controls
Weighted mean brain-PAD scores were +4.39 years (SE = ± 0.84) in the control group and +7.74 years (SE = ± 0.94) in the SZ group. On average, brain-PAD was higher by +3.55 years ($95\%$ CI 2.91, 4.19; $p \leq 0.0001$) in individuals with SZ compared to controls (Cohen’s $d = 0.48$; $95\%$ CI 0.33, 0.63; $p \leq 0.0001$) adjusted for age, age2, sex and scanning site (Fig. 1). Post-hoc sensitivity analysis excluding cohorts in which the model generalised less well (based on MAE > 10.00 or R2 < 0.1 in healthy controls) returned similar results (see Supplementary Fig. S4). Effect sizes were heterogeneous across individual cohorts (Q [24] = 55.15, $p \leq 0.0003$; I2 = $57.53\%$). A significant effect was seen in 22 out of 25 cohorts, with a positive direction of mean effect size observed in all but one cohort. Across cohorts, mean brain-PAD did not differ between single versus multi-site cohorts (QM[1]=0.033, $$p \leq 0.857$$), nor between 1.5 T versus 3 T scanners (QM[1] = 0.084; $$p \leq 0.772$$) or with respect to mean age (QM[1] = 0.33, $$p \leq 0.566$$). There was some evidence for a moderating effect of sex at the cohort level with an attenuated association between SZ and brain-PAD in cohorts with a higher proportion of females (b = −0.069, SE = 0.028, QM [1] = 6.271; $$p \leq 0.012$$), accounting for some of the residual heterogeneity in the estimated brain-PAD difference between SZ and HC across the 25 cohorts (R2 = $35.83\%$; I2 = $46.68\%$). We also found a weak linear, yet not significant effect for age on brain-PAD (bage = −0.23, $95\%$ CI −0.47, 0.01, $$p \leq 0.061$$; bage2 = −0.00, $95\%$CI −0.05, 0.05, $$p \leq 0.998$$). Additional adjustment for handedness in a smaller pool of 16 cohorts did not meaningfully change our main finding for the effect of SZ (+3.62 years; $95\%$ CI 2.82, 4.42; $p \leq 0.0001$).Fig. 1Case-control differences in brain-PAD.Forest plot of differences in mean brain-PAD scores (predicted brain age - chronological age) between patients with schizophrenia (SZ) and controls across (26 −1) 25 cohorts (a total of 2792 cases and 2598 controls; excluding 1 cohort that contributed data for patients only), controlling for sex, age and age2 and scanning site. Regression coefficients (in years) are denoted by black boxes. Black lines indicate $95\%$ confidence intervals. The size of the box indicates the weight the cohort received (based on inverse variance weighting). The pooled estimate for all cohorts is represented by a black diamond, with the outer edges of the diamond indicating the confidence interval limits.
## Brain age and clinical characteristics in SZ
Among SZ patients, we found no statistically significant effects on brain-PAD of clinical characteristics, including age-of-onset, length of illness, symptom severity (PANSS total, SAPS global), antipsychotic use, and CPZ-equivalent dose after adjusting for age and age2 (Table 2 and Supplementary Fig. S5a–i). A weak, positive effect for negative symptom severity (SANS global) on Brain-PAD was observed, although it did not reach significance ($b = 0.18$, $95\%$ CI −0.01, 0.38, PFDR = 0.62). In addition, no significant effects were found for typical versus atypical and both atypical and typical versus atypical medication groups (Supplementary Table S6). Further adjustment for handedness returned similar results (Supplementary Table S7).Table 2Clinical characteristics and brain-PAD in individuals with SZ.Clinical parameterNKbetaSE$95\%$ CIPFDR valueAge of onset (years)205321−0.060.09−0.22, 0.110.84Length of illness (years)2056210.050.09−0.12, 0.220.84PANSS total1437200.050.06−0.06, 0.170.77SANS global1911220.180.10−0.01, 0.380.62SAPS global1892210.140.12−0.09, 0.380.70AP use—atypical vs. unmed642 ($\frac{486}{156}$)71.711.27−0.77, 4.190.70AP use—typical vs. unmed117 ($\frac{42}{72}$)3−0.131.00−2.10, 1.840.90AP use—both vs. unmed266 ($\frac{184}{82}$)4−0.331.08−2.43, 1.770.90CPZ-equivalent dose1698190.000.01−0.02, 0.020.90K number of cohorts, N total number of participants included in each meta-analysis (where applicable, total group size for AP type use/unmedicated is given in brackets), SE standard error, CI confidence intervals. P values are false discovery rate (FDR) adjusted. SANS Scale for the Assessment of Negative Symptoms, SAPS Scale for the Assessment of Positive Symptoms, PANSS Positive and Negative Syndrome Scale, AP Antipsychotics, CPZ chlorpromazine. Associations between clinical characteristics and brain-PAD (predicted brain age—chronological age) in SZ. For continuous variables (age of onset, length of illness, PANSS total, SANS/SAPS global and CPZ), the regression coefficient beta indicates a mean change in brain-PAD per unit increase in each clinical variable across cohorts. For categorical variables (AP use—typical/atypical/both atypical and typical), beta indicates the mean brain-PAD difference between each treatment group and unmedicated (unmed) individuals. Effects were adjusted for age and age2.
## Correlations between brain imaging features and brain age
All imaging features, except mean lateral ventricle volume, were negatively correlated with predicted brain age (Fig. 2); thickness features correlated more strongly with brain age (mean Pearson r [SD]: − 0.46 [0.13]), especially in medial frontal and temporo-parietal regions, than subcortical volumes (−0.32 [0.30]) or surface area features (−0.22 [0.06]). We also visualized these associations separately for controls and SZ patients with similar results, suggesting comparable structure coefficients in both groups (for more details see Supplementary Material).Fig. 2Correlation coefficients of predicted brain age and FreeSurfer features across control and schizophrenia (SZ) groups. Bivariate correlations are shown to provide an indication of the relative contribution of features in brain age prediction. The figure shows Pearson correlations between predicted brain age and cortical thickness features (top row), cortical surface areas (middle row) and subcortical volumes (bottom row), from both the lateral (left) and medial (right) view. Features were averaged across the left and right hemispheres. The negative correlation with ICV was excluded from this figure for display purposes.
## Discussion
We assessed brain ageing in 2803 individuals with SZ and 2598 healthy controls using a novel brain age algorithm based on FreeSurfer ROIs. Results indicate that, at a group level, patients with SZ show a greater discrepancy between their brain-predicted age and chronological age compared to healthy individuals (+3.55 years), with a moderate increase in brain-PAD (Cohen’$s = 0.48$). The greater brain-PAD in the SZ group was not driven by any of the specific clinical characteristics assessed here (age of onset, length of illness, symptom severity, and antipsychotic use and dose). This study has two major strengths. Firstly, through a prospective meta-analytic approach within the ENIGMA consortium, we were able to assess brain age differences between SZ patients and healthy controls using standardised analysis methods across multiple independent cohorts worldwide, providing a generalised mean effect size. Second, the overall large sample size and harmonisation of data across cohorts allowed for a more reliable assessment of the relationship between clinical variables and brain-PAD among SZ patients.
The mean brain-PAD difference between patients and controls was +3.55 years (Cohen’s $d = 0.48$) in our study. Overall, this finding is aligned with previously reported brain-PAD scores in SZ patients vs. healthy controls (range: +2.6–7.8 years) [26–33]. Schnack et al. [ 29] and a recent mega-analysis by Kaufmann et al. [ 32] found similar effect sizes (+3.4 years and Cohen’s $d = 0.51$, respectively) in largely non-overlapping/independent samples from this current study. On the other hand, our brain-PAD difference is smaller relative to that reported in earlier work by Koutsouleris et al. [ 27] and Shahab et al. [ 30] showing respectively +5.5 to +7.8 years of brain age in smaller samples of SZ patients. Several methodological differences may explain the variability in magnitude of brain age effects in SZ across studies, including the type of neuroimaging features (e.g., voxel-wise vs. ROI-based morphometric data; and/or single vs. multiple imaging modalities) [40], the machine learning algorithm used for brain age estimation [41], the size of training and test data samples, and differences in patient characteristics.
Relative to healthy controls, brain-PAD scores in SZ suggest more advanced brain ageing than in MDD (+1.12 years) [42] and bipolar disorder (BD; +1.93 years) [42], that may reflect more pronounced structural brain abnormalities in SZ [24]. This aligns with previous reports from the ENIGMA consortium, showing largest effect sizes of cortical and subcortical gray matter alterations in SZ (highest Cohen’s d effect size = 0.53) [16, 17], followed by BD (highest Cohen’s $d = 0.32$) [43, 44] and MDD (highest Cohen’s $d = 0.14$) [45, 46]. Hence, sensitivity of brain-PAD to SZ at the group level appears to be quantitively similar to that of leading cortical thickness and subcortical volume measures. A further key advantage of the “brain age” paradigm is that it captures multivariate age-related structural brain patterns into one (or more) composite measure(s), thereby simplifying analyses and aids interpretation with respect to normative patterns of brain ageing.
Consistent with previous reports [27, 31], we did not observe significant associations between brain-PAD and age of onset, length of illness, and antipsychotic treatment or dose among SZ patients. This suggests that a greater brain-PAD in SZ may not be primarily driven by disease progression or treatment-related effects on brain structure that have been reported elsewhere [12, 14, 18, 47, 48]. This is in keeping with previous studies showing a greater brain-PAD already present in first-episode SZ and first-episode psychosis patients [26, 49]. Using a longitudinal design, Schnack et al. investigated brain age acceleration (i.e., annual rate of change in brain-PAD) over the duration of illness in SZ ($$n = 341$$; mean follow up period: 3.48 years). Brain-PAD started increasing by about 2.5 years (per year) just after illness onset, though this acceleration rate slowed down to a normal rate over the first 5 years of illness [29]. Lastly, in contrast to previous findings in SZ [27] and first-episode psychosis [49] we did not find strong evidence for a positive association between negative symptom severity and brain-PAD. An explanation for this could be that negative symptoms are more specifically linked to brain age differences at the regional level (i.e., temporal or parietal brain-PAD) than at the global level (i.e., “whole-brain” brain-PAD), as reported previously [32].
The biological mechanisms underlying advanced brain ageing in SZ remain elusive. These may involve interrelated biochemical abnormalities that accompany both schizophrenia and brain ageing, including increased inflammation and oxidative stress [10, 50]. Elevated levels of inflammatory markers (e.g., pro-inflammatory cytokines in blood and central nervous system) have been observed by multiple studies in individuals with schizophrenia [11, 51]. Moreover, there has been evidence for peripheral inflammation markers being associated with structural brain abnormalities observed in schizophrenia and related outcomes (e.g., first episode psychosis), including but not limited to abnormal cortical thickness of the bilateral Broca’s area and temporal gyrus [52, 53], as well as with greater brain-PAD scores [54]. Abnormal levels of multiple oxidative stress markers have also been observed in SZ, both peripherally and in brain tissue [11, 55]. Oxidative stress and inflammation may reciprocally induce one another via a positive feedback loop in SZ, resulting in cellular damage [56].
Several methodological issues require further consideration. First, while a brain-PAD score (that is not equal to zero) is conceptually a prediction error that could reflect physiological deviations from normal ageing trajectories, it could be partly attributed to lack of model accuracy due to noise or unwanted variation [32, 57, 58]. Potential sources of unwanted variation include the use of multiple scanners and/or image acquisition protocols across (or within) participating cohorts that may affect the overall generalization performance of the brain age model applied here. To overcome this, in the primary analysis we included cohorts that had data on both cases and healthy controls collected in a similar, if not identical, manner (i.e., same site/scanner and/or image acquisition protocol) and have adjusted for multiple scanners where applicable. Nevertheless, while our model fit is lower than some previous studies, this would only increase noise, not a bias towards finding an effect of SZ on brain-PAD. Second, although our meta-analytic approach allowed us to combine information across multiple cohorts, the summary-level data reported here does not adequately capture the considerable inter-individual variability in brain-PAD among SZ patients, as has been documented elsewhere [32]. As some individuals with SZ are not characterised by a greater brain-PAD, it would be important to further investigate both clinical as well as biological, lifestyle and technical confounding factors that are linked to SZ and/or brain-PAD (e.g., inflammation, smoking, body mass index, imaging parameters) potentially accounting for inter-individual variability. Given that greater brain-PAD has been associated with poorer health outcomes, such as an increased mortality risk [23], understanding the extent to which various factors may contribute to brain ageing in SZ could help prioritize targets for interventions aiming to halt (or reverse) advanced brain ageing. Additionally, future studies should direct their efforts towards better characterization of region-specific brain patterns that could explain individual variation as well as differences in (global) brain-PAD within and between groups [59, 60]. Third, although the sample size of our main analysis (SZ versus controls) was very large for a neuroimaging study, the size of patient groups categorised by status of antipsychotic use was relatively small (particularly that of unmedicated individuals with SZ) and cohort differences include the use of different assessments or processes to ascertain medication use and dose. This may have precluded detection of some associations. Lastly, given the cross-sectional design of the current study, we were not able to assess brain age acceleration more directly and how that may be related to clinical characteristics. Longitudinal large-scale studies are better suited for examining brain ageing per se [61] and for evaluating the clinical relevance of brain-PAD in SZ.
In conclusion, we found evidence of advanced brain ageing in SZ patients compared to healthy controls, which does not seem to be driven by the effects of medication or other clinical characteristics. Deviations from normative brain ageing trajectories in SZ may at least in part reflect increased risk of premature mortality and age-related chronic diseases commonly seen in SZ. Future longitudinal studies with more in-depth clinical characterization—including information on mental and somatic health outcomes—will be needed to elucidate whether a brain age predictor such as brain-PAD can provide a clinically useful biomarker to inform early prevention or intervention strategies in SZ.
## Supplementary information
Supplementary Materials Supplementary Tables and Figures The online version contains supplementary material available at 10.1038/s41380-022-01897-w.
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|
---
title: Golgi apparatus, endoplasmic reticulum and mitochondrial function implicated
in Alzheimer’s disease through polygenic risk and RNA sequencing
authors:
- Karen Crawford
- Ganna Leonenko
- Emily Baker
- Detelina Grozeva
- Benoit Lan-Leung
- Peter Holmans
- Julie Williams
- Michael C. O’Donovan
- Valentina Escott-Price
- Dobril K. Ivanov
journal: Molecular Psychiatry
year: 2022
pmcid: PMC10005937
doi: 10.1038/s41380-022-01926-8
license: CC BY 4.0
---
# Golgi apparatus, endoplasmic reticulum and mitochondrial function implicated in Alzheimer’s disease through polygenic risk and RNA sequencing
## Abstract
Polygenic risk scores (PRS) have been widely adopted as a tool for measuring common variant liability and they have been shown to predict lifetime risk of Alzheimer’s disease (AD) development. However, the relationship between PRS and AD pathogenesis is largely unknown. To this end, we performed a differential gene-expression and associated disrupted biological pathway analyses of AD PRS vs. case/controls in human brain-derived cohort sample (cerebellum/temporal cortex; MayoRNAseq). The results highlighted already implicated mechanisms: immune and stress response, lipids, fatty acids and cholesterol metabolisms, endosome and cellular/neuronal death, being disrupted biological pathways in both case/controls and PRS, as well as previously less well characterised processes such as cellular structures, mitochondrial respiration and secretion. Despite heterogeneity in terms of differentially expressed genes in case/controls vs. PRS, there was a consensus of commonly disrupted biological mechanisms. Glia and microglia-related terms were also significantly disrupted, albeit not being the top disrupted Gene Ontology terms. GWAS implicated genes were significantly and in their majority, up-regulated in response to different PRS among the temporal cortex samples, suggesting potential common regulatory mechanisms. Tissue specificity in terms of disrupted biological pathways in temporal cortex vs. cerebellum was observed in relation to PRS, but limited tissue specificity when the datasets were analysed as case/controls. The largely common biological mechanisms between a case/control classification and in association with PRS suggests that PRS stratification can be used for studies where suitable case/control samples are not available or the selection of individuals with high and low PRS in clinical trials.
## Introduction
Alzheimer’s disease (AD) is a neurodegenerative disorder characterised by progressive cognitive decline, molecular changes including, but not limited to the accumulation of beta-amyloids (extracellular Aß plaques) and tau tangles in the human brain [1]. The molecular changes are detectable much earlier than the clinical phenotype, occurring ~10–20 years before cognitive deterioration [2]. Currently, there are no approved pharmacological or other treatments that have been shown to reverse or stop the symptoms and/or the associated molecular changes. An accurate diagnostic test in early (preclinical) and late stages of the disease is a prerequisite not only for the successful application of future treatments, but also for the correct stratification of individuals for clinical trials.
Polygenic Risk Scores (PRS) are a mathematical aggregate (i.e. a single value) indexing an individual’s relative genetic liability to a trait conferred by hundreds or indeed thousands of risk alleles [3]. The scores are the output of statistical models developed using data from large genome-wide association studies (GWAS). PRS analysis has been widely adopted as a tool for measuring common variant liability in cardiometabolic disease, schizophrenia, AD, diabetes and cancer [4–9]. Furthermore, there have been efforts to develop the use of PRS as a diagnostic tool (i.e. as a biomarker) for early identification of people at an increased risk for manifestation of clinical disease [8].
In AD, PRS have been used to predict lifetime risk of AD development [4, 10, 11], yielding Area Under the Curve estimates in identifying individuals with pathologically confirmed AD vs. controls of ~82–$84\%$ [11], including the APOE locus. In addition, the sensitivity (true positives) increases to ~$90\%$ for PRS extremes [12]. Thus far, efforts to exploit GWAS associations to identify pathological mechanisms underpinning AD have met with varying success [13], but immune response, lipid metabolism, regulation of Aß formation and cholesterol metabolism, have been identified as likely to be key disrupted biological mechanisms [14] and macrophages and microglia as likely key drivers of pathology [15]. As for AD PRS, based on many variants in a cumulative fashion, understanding the underlying molecular or biological mechanisms that comprise the polygenic component in AD through gene-expression data, have not been explored before.
To address the paucity of knowledge with respect to the downstream molecular consequences of genetic liability to AD and to understand the biological mechanisms that are likely to be impacted upon by increased liability, we analysed the differential gene-expression from bulk RNA sequencing in the MayoRNAseq publicly available dataset with respect to PRS. We also compare the findings to a case/controls differential gene-expression analysis (Fig. 1). This offers a potential to extend the clinical utility of PRS beyond diagnosing individuals at high risk of AD by pointing to putative causal processes at the molecular level. Fig. 1Experimental flowchart. A MayoRNAseq comprise individuals with matched genetic (WGS) and gene-expression data (bulk brain RNA-seq) from two brain regions: cerebellum and temporal cortex. B Differentially expressed genes were derived separately for case/controls and PRS. C Gene-ontology enrichment analysis was performed separately for both case/control and PRS outcomes and compared pairwise across all analyses.
## Sample data
The MayoRNAseq [16–18] study (part of Accelerated-Medicine Partnership (AMP-AD)) is a post-mortem brain cohort of individuals with a neuropathological diagnosis of AD, progressive supranuclear palsy, pathological ageing and elderly controls with samples from both temporal cortex and cerebellum tissues. Sample descriptors can be found in Table S1. We retained only data from samples with a label of AD or control.
## RNA-seq QC and differential gene-expression
The original rna-seq bam files (https://www.synapse.org/#!Synapse:syn9702085) were re-aligned to GRCh38.98 followed by a quality control using RNA-SeQC 2.3.5 [19] (Table S2). Read counts were derived using htseq-count and samples were removed from further analysis with 0 read counts across all genes. Genes were removed from further analysis if they had 0 read counts across all samples and if the trimmed mean of M-values were <0.5 in $50\%$ of the samples (edgeR [20]). Further details are provided in supplementary methods. Differentially expressed genes were derived using DESeq2 [21] with raw counts adjusting for age at death, sex and APOE (Table S3) status (DESeq2 model matrix: design = ~age_at_death + sex + APOE_status + diagnosis for case/control analysis and design = ~age_at_death + sex + APOE_status + PRS for PRS; log fold changes and p-values are returned for the last variable in the design matrix). FDR was used for multiple hypotheses testing correction. RNA-seq were matched to VCF samples with verifyBamID [22] (IBD ≥ 0.8).
## VCF QC and PRS calculation
WGS recalibrated vcf files (https://www.synapse.org/#!Synapse:syn22264775) were converted to a PLINK [23] format and variants converted to GRCh38 (http://genome.ucsc.edu/cgi-bin/hgLiftOver). For ethnicity estimates we used the phase3 1000 Genomes Project reference data [24] (https://www.cog-genomics.org/plink/2.0/resources#1kg_phase3). Variants with HWE p ≤ 1.0 × 10−06, missingness ≥ 0.05 and MAF ≤ 0.01 were excluded from further analysis. Ancestry was estimated using Principal Component Analysis in PLINK2 by plotting the first two eigenvectors and samples were excluded from further analysis if a sample deviated from the 1000 Genomes EUR cluster (Fig. S1). Individuals with inbreeding coefficient F ≤ 0.2 were deemed females and F ≥ 0.8 males. All pair of samples that had PI-HAT ≥ 0.22 were excluded from further analysis.
For PRS calculation we used the summary statistics from a clinically assessed case/control study on AD [14], excluding the AMP-AD samples. PRS were calculated using PLINK for pT ≤ 0.1 on LD-clumped SNPs by retaining the SNP with the smallest p value excluding SNPs with r2 > 0.1 in a 1000 kb window. PRS were adjusted for five consecutive PCs then standardised within the MayoRNAseq samples.
## Gene ontology
The Wilcoxon rank-sum test (Catmap [25]), was used to test for enrichment of Gene Ontology (GO) categories (supplementary methods). Ranks of genes were based on the p value from the significance of the differential gene-expression. For all tests, three lists were derived comprising [1] differentially expressed genes based on p value only (termed no-direction), [2] the most differentially up-regulated (p value and log-fold > 0) genes at the top of the list and most differentially down-regulated genes (log-fold < 0) at the bottom of the list (termed up-regulated) and [3] the most differentially down-regulated genes at the top of the list and most differentially up-regulated genes at the bottom of the list (termed down-regulated). We used random gene null hypothesis as it was deemed computationally unfeasible to perform sample-label permutations [25]. For comparison we also performed a GO enrichment analysis using a separate method (topGO [26]). FDR was used to account for multiple hypotheses testing. Semantic similarity (GOSemSim [27]) was used to cluster statistically significant GO terms (Rel and classical multidimensional scaling (CMD)). The most representative (manually curated) GO term was chosen as the name for describing CMD clusters.
## Sample numbers after QC
After our quality control (genotypes and RNA-seq), there were 288 samples with matched genetic and RNA-seq samples in the MayoRNAseq dataset (170 genetically unique individuals; Table S1).
## AD case/control differential gene-expression and GO enrichment
Differentially expressed genes were derived using DESeq2 separately for the two tissue samples in the MayoRNAseq (temporal cortex and cerebellum), including covariates for age at death, sex and APOE status. There were >5000 differentially expressed genes after correction for multiple hypothesis testing in both cerebellum (~8000) and temporal cortex (~5000; Data S1, S2) with a statistically significant overlap of differentially expressed genes between the two tissues (Fig. S2). There was no statistically significant enrichment of AD-associated GWAS risk genes in any of the three gene lists ($$p \leq 0.97$$ and $$p \leq 0.31$$ for cerebellum and temporal cortex respectively for genes based only on p value (no-direction); $$p \leq 0.42$$ and 0.06 for up-regulated (order by p value and logfc); $$p \leq 0.58$$ and $$p \leq 0.94$$ for down-regulated; Data S10; list of AD GWAS risk genes given in Data S3 and description in supplementary methods).
We performed GO enrichment analysis (biological process (BP), cellular component (CC) and molecular function (MF)) using the three sets of differential expression gene lists, that is no-direction (based on p value only), up-regulated and down-regulated (log-fold change and p value). There was a statistically significant overlap of significantly enriched GO terms (separately for all three gene lists) between the two tissues (Fig. S3a–e) in addition to a significant GO rank profile similarity (Fig. S3f–h). This suggests that both tissues share an overall statistically significant similarity in terms of disrupted biological pathways with respect to a case/control analysis. It is of note that overlap of GO and testing for profile similarity achieved much stronger statistical significance in the up and down-regulated significant GO terms as compared to the no-direction results (gene order based on p value only).
The statistically significant GO terms from both tissues (no-direction gene-list; p value only) were combined and clusters were derived using semantic similarity (BP and CC). This was done to reduce the complexity and functional redundancy of GO terms. Significantly disrupted biological processes included response to stimulus, regulation of signal transduction, cell motility and metabolism, aerobic respiration, differentiation, organelles (Golgi apparatus, endoplasmic reticulum (ER), mitochondria), oxidoreductase complex, cell cycle, regulation of cell death (Fig. S4).
We also performed the same semantic similarity clustering separately for the up-regulated and down-regulated GO terms. Significantly disrupted up-regulated biological processes included regulation of metabolism (including lipid and cholesterol), stress and immune response, signalling, DNA repair, differentiation/morphogenesis/development, organelles (Golgi apparatus, ER, mitochondria), senescence, neuronal cells (Fig. S5). Significantly disrupted down-regulated biological processes mainly included cellular respiration such as mitochondrial electron transport, respiratory chain complexes, mitochondrial membrane, etc. ( Fig. S6). GO-terms in all semantic similarity clusters (both up and down-regulated analysis) were from both tissue samples, replicating the statistically significant GO profile similarity and overlap of GO terms, suggesting limited overall brain region specificity.
GO enrichment analysis showed that several biological pathways previously implicated from GWAS [14] in AD were significantly enriched in the AD case/control differential gene-expression analysis (Data S4, S5, Figs. 2a, 5a), although we have not formally tested if this is statistically significant, thus it could be a chance finding. Significantly up-regulated GO terms included immune system processes (GO:0002376, $$p \leq 1.13$$ × 10−06 and $$p \leq 4.09$$ × 10−43 for cerebellum and temporal cortex respectively), response to lipids (GO:0033993, $$p \leq 7.41$$ × 10−03 and $$p \leq 1.05$$ × 10−27), inflammatory response (GO:0006954, $$p \leq 4.87$$ × 10−02 and $$p \leq 5.83$$ × 10−15), endosome (GO:0005768, $$p \leq 6.27$$ × 10−07 and $$p \leq 2.30$$ × 10−11), regulation of cell death (GO:0010941, $$p \leq 1.78$$ × 10−08 and $$p \leq 1.26$$ × 10−32), regulation of neuron death (GO:1901214, $$p \leq 6.46$$ × 10−03 and $$p \leq 1.32$$ × 10−04). There was also evidence for the involvement of glial cells in the temporal cortex (up-regulated GOs, glial cell projection GO:0097386 $$p \leq 1.62$$ × 10−04, astrocyte projection GO:0097449 $$p \leq 6.55$$ × 10−04, regulation of microglial cell activation GO:1903978 $$p \leq 1.24$$ × 10−02), but not in cerebellum. In addition, we were only able to confirm such previous AD GWAS-derived disrupted biological pathways by sorting (log-fold change and p value) or in other words using the direction of effect of the genes (mostly up-regulated and mostly down-regulated at the top of the gene lists), but not based on p value only (no-direction). For example, immune system process (GO:0002376) was not significantly enriched GO-term in the no-direction (genes sorted by p value only; FDR $$p \leq 1$$ and $$p \leq 8.07$$ × 10−01 in cerebellum and temporal cortex respectively), but it was significantly enriched in both cerebellum and temporal cortex in the up-regulated GO-terms analysis. Fig. 2Semantic similarity clustering of up-regulated statistically significant GO terms in both cerebellum and temporal cortex (case/control analysis).A Semantic similarity clustering (BP only up-regulated GO). X-and Y-axes represent classical multidimensional scaling (CMD) dimension 1 and 2. All GO terms p ≤ 0.05 FDR. Green dots represent significant GO terms from the case/control analysis of cerebellum, blue dots represent significant GO terms from the case/control analysis of temporal cortex, red dots represent significant GO terms overlapping in case/control analysis of cerebellum and temporal cortex. Cluster labels were manually curated based on the most common GO term in the cluster. B Overlap of GO terms (BP, CC and MF up-regulated GOs). Proportional Venn diagram. Numbers represent significant GO terms (FDR) in the two lists with the middle number representing the number of GOs that overlap. Red colour represents cerebellum, blue- temporal cortex. Hypergeometric test $$p \leq 4.11$$ × 10−287.
We also performed the GO-terms enrichment analysis using a separate enrichment method (topGO [26]) with the same three gene lists (no-direction, mostly up-regulated and mostly down-regulated at the top). The results from both Catmap and topGO (paired GO ranks) display extremely similar rank profile of GO-terms with r2 ranging from 0.65 to 0.8 (Fig. S7).
## PRS differential gene-expression and GO enrichment
Similarly, to the case/control differential gene-expression analysis, for each gene we derived differentially expressed genes associated with PRS using DESeq2 separately for the two tissue samples in the MayoRNAseq, including a covariate for age at death, sex and APOE status.
There were three and 351 genes differentially expressed genes in the cerebellum and the temporal cortex respectively following an FDR correction for multiple hypothesis testing (Data S6). There were fewer differentially expressed genes in cerebellum as compared to temporal cortex associated with PRS, in contrast to the fewer differentially expressed genes in the temporal cortex as compared to cerebellum in the case/control analysis. Due to few genes being differentially expressed in cerebellum, we performed an overlap of the top 300 genes in both tissue samples. There was a statistically significant overlap of genes in both datasets in the same direction (including a significant rank correlation of all genes; Fig. S8).
There was also a statistically significant enrichment of previous AD-associated GWAS risk genes (Wilcox rank-sum test $$p \leq 2.99$$ × 10−02; not corrected for multiple hypothesis testing) in the temporal cortex no-direction gene list (ordered by p value only), but not in cerebellum ($$p \leq 2.44$$ × 10−01). The top ranked 15 genes in the temporal cortex also found in AD GWAS hits were HAVCR2, MS4A6A, INPP5D, ECHDC3, SPI1, ADAMTS4, ADAMTS1, CR1, IL34, PICALM, HLA-DRB1, CD33, APH1B, FERMT2, and PLCG2, although only HAVCR2 and MS4A6A ($$p \leq 1.61$$ × 10−02, beta = 0.21 and $$p \leq 3.72$$ × 10−02; beta = 0.29), passed FDR correction. In addition, there was a statistically significant enrichment of AD-associated GWAS genes in the up-regulated gene list in temporal cortex ($$p \leq 1.22$$ × 10−05 and $$p \leq 0.49$$ for temporal cortex and cerebellum respectively), but not in the down-regulated gene list for both temporal cortex and cerebellum ($$p \leq 0.5$$ and $$p \leq 0.99$$). This suggests that overall GWAS-hits are on average ranked significantly higher in the temporal cortex gene expression list in the PRS analysis than expected by chance alone and these are more likely to be up-regulated than down-regulated (only IL34 was down-regulated among the top 15 GWAS hits). Furthermore, in temporal cortex, higher AD PRS was associated with increased gene-expression of 52 out of 75 AD GWAS associated genes [14, 28–32] (Fisher’s exact test $$p \leq 2.79$$ × 10−04; 10319 up and 11071 down-regulated among all genes).
There was a statistically significant overlap of significantly enriched GO terms (separately for all three gene lists) between the two tissues (Fig. S9a–e) in addition to a significant GO rank profile similarity (Figs. S9f–h). This suggests that both tissues share an overall similarity in terms of disrupted biological pathways with respect to PRS.
The statistically significant GO terms from both tissues (no-direction gene-list; p value only) were combined and clusters were derived using semantic similarity. Significantly disrupted biological processes (no-direction gene list) included immune response, stress response, regulation of metabolism, transport and signalling, aerobic respiration, organelles (Golgi apparatus, ER, mitochondria), oxidoreductase complex, cell cycle, regulation of cell death (Fig. S10). Nevertheless, GOs in immune-related clusters (i.e. immune response, regulation of T/B cells and interferon/interleukin) were statistically significant only in temporal cortex (Fig. S10a), but not in cerebellum.
The semantic similarity clustering was also performed separately for the up-regulated and down-regulated GO terms. Significantly disrupted up-regulated biological processes included regulation of metabolism (including fatty acids and cholesterol), stress and immune response (adaptive and innate), signalling, DNA repair, differentiation/morphogenesis/development, organelles (Golgi apparatus, ER, mitochondria), senescence and neuronal cell death, neuronal cells (Fig. S11 and Fig. 3a). Significantly disrupted down-regulated biological processes mainly included cellular respiration such as mitochondrial electron transport, respiratory chain complexes, mitochondrial membrane, mitochondrial ATP synthesis, metabolism, neuronal processes such as neurotransmitter secretion/transport, neuron projection, postsynaptic membrane (Fig. S12). The semantic similarity clusters comprised up-regulated GO terms from both tissues (semantic similarity Fig. S11), but there were notable differences in the down-regulated GOs, suggesting tissue specificity. All synaptic-associated GO-terms were found to be significantly down-regulated in temporal cortex, but up-regulated cerebellum. These include synaptic/neuronal processes such as synaptic signalling, synaptic and pre/postsynaptic membranes, regulation of synaptic plasticity, synaptic vesicle, neurotransmitter secretion, glutamatergic synapse, etc. ( Figs. 3c, 5a).Fig. 3Semantic similarity clustering of up-regulated statistically significant GO terms in both cerebellum and temporal cortex (PRS analysis).A Semantic similarity clustering (BP only up-regulated GO). B Overlap of GO terms (BP, CC and MF up-regulated GOs). C Semantic similarity clustering (CC only down-regulated GO). D Overlap of GO terms (BP, CC and MF down-regulated GOs). A, C X-and Y-axes represent classical multidimensional scaling (CMD) dimension 1 and 2. All GO terms p ≤ 0.05 FDR. Green dots represent significant GO terms from the PRS analysis of cerebellum, blue dots represent significant GO terms from the PRS analysis of temporal cortex, red dots represent significant GO terms overlapping in PRS analysis of cerebellum and temporal cortex. Cluster labels were manually curated based on the most common GO term in the cluster. B, D Proportional Venn diagram. Numbers represent the significant GO terms (FDR) in the two lists with the middle number representing the number of GOs that overlap. Red colour represents cerebellum the blue temporal cortex. B Hypergeometric test $p \leq 1.0$ × 10−300 (D) Hypergeometric test $$p \leq 6.85$$ × 10−65.
Similarly, to the case/control analysis GO enrichment analysis showed that a wide range of previously implicated (from GWAS) biological pathways in AD were also found to be significantly enriched (Data S9 and Figs. 3a, b, 5a), including immune system processes (GO:0002376, $$p \leq 2.72$$ × 10−05 and $$p \leq 2.67$$ × 10−98 for cerebellum and temporal cortex respectively), response to lipids (GO:0033993, $$p \leq 1.44$$ × 10−07 and $$p \leq 1.17$$ × 10−21), inflammatory response (GO:0006954, $$p \leq 7.98$$ × 10−04 and $$p \leq 3.24$$ × 10−29), endosome (GO:0005768, $$p \leq 2.44$$ × 10−03 and $$p \leq 4.48$$ × 10−19), regulation of cell death (GO:0010941, $$p \leq 2.26$$ × 10−07 and $$p \leq 1.12$$ × 10−27), regulation of neuron death (GO:1901214, $$p \leq 2.55$$ × 10−03 and $$p \leq 2.96$$ × 10−02). There was also some evidence for the involvement of glial cells in both tissues (up-regulated GOs, glial cell projection GO:0097386 $$p \leq 1.95$$ × 10−03 and $$p \leq 3.48$$ × 10−02 for cerebellum and temporal cortex respectively, astrocyte activation GO:0048143 $$p \leq 3.66$$ × 10−02 and $$p \leq 1.26$$ × 10−02, microglial cell activation GO:0001774 $$p \leq 4.09$$ × 10−02 and $$p \leq 3.09$$ × 10−07).
Similarly, to the case/control GO analysis, the results from both Catmap and topGO (paired GO ranks) displayed extremely similar rank profile of GO-terms with r2 ranging from 0.65 to 0.81 (Fig. S13).
## Molecular mechanisms shared/different between cases/controls and PRS with respect to differential gene expression
We compared the differential expression results in terms of genes from the case/control and PRS analyses for cerebellum and temporal cortex respectively. There was no statistically significant overlap of differentially expressed genes in cerebellum (Figs. S14, S15), but there was a statistically significant overlap of differentially up and down-regulated genes in the temporal cortex (Fig. S16).
Contrary to the results with respect to overlap of differentially expressed genes, the overlap of GO terms for both cerebellum and temporal cortex showed remarkable similarity in terms of both overlap of significantly disrupted GOs and rank profiles in all three gene lists (no-direction, most up-regulated at the top and most-downregulated at the top; Figs. S17, S18), although fewer GOs overlapped if no-direction of gene effect was used.
The statistically significant GO terms from both tissues (no-direction gene-list; p value only) from the case/control and PRS analyses were combined and clusters were derived using semantic similarity, separately for cerebellum and temporal cortex. There were fewer significantly disrupted GO terms in the case/control analysis as compared to PRS (57 vs. 389 in cerebellum and 264 and 695 for temporal cortex for the case/control and PRS respectively; Data S4a, S5a, S7a, S8a). The only processes that were in common in cerebellum were GOs related to organelles and metabolic processes (Fig. S19). Similarly, the commonly disrupted biological processes in temporal cortex were extracellular space/structure, organelles, response to stimulus/lipids, signal transduction (Fig. S22). Most of the semantically similar clusters of up/down-regulated GOs in cerebellum with respect to case/controls and PRS comprised GOs from both analyses (case/controls and in response to PRS), suggesting similarly disrupted biological processes with very few differences (Figs. S20, S21). Differences included significantly down-regulated biological processes found only in response to PRS such as, WNT/NF-kappaB signalling, rRNA processing, protein import in mitochondria (Fig. S21) and significantly up-regulated processes only found in case/control analysis such as, histone acetyltransferase complexes (Fig. S20).
Similarly to cerebellum, most of the up-regulated semantically similar clusters in temporal cortex (case/control vs. PRS) have GO terms from both case/control and PRS analysis, suggesting little differences in terms of significantly disrupted up-regulated biological processes (Figs. S23 and Fig. 4a). This was in contrast to down-regulated terms that showed differences. These included mainly neuronal/synaptic down-regulated processes only found in response to PRS as compared to case/control analysis such as, neuronal plasticity, synaptic signalling/transmission, neurotransmitter levels and secretion, post/pre-synaptic membrane, glutamatergic and GABA-ergic synapse (Fig. S24 and Fig. 4b).Fig. 4Semantic similarity clustering of up and down-regulated statistically significant GO terms in temporal cortex (case/control vs. PRS analysis).A Semantic similarity clustering (BP only up-regulated GO). B Overlap of GO terms (BP, CC and MF up-regulated GOs). C Semantic similarity clustering (BP only down-regulated GO). D Overlap of GO terms (BP, CC and MF down-regulated GOs). A, C X-and Y-axes represent classical multidimensional scaling (CMD) dimension 1 and 2. All GO terms p ≤ 0.05 FDR. Green dots represent significant GO terms from the case/control & PRS analysis of temporal cortex, blue dots represent significant GO terms from the case/control & PRS analysis of temporal cortex, red dots represent significant GO terms overlapping in case/control analysis of cerebellum and temporal cortex. Cluster labels were manually curated based on the most common GO term in the cluster. B, D Proportional Venn diagram. Numbers represent the significant GO terms (FDR) in the two lists with the middle number representing the number of GOs that overlap. Red colour represents case/control the blue PRS analyses. hypergeometric test for (B) $p \leq 1.0$ × 10−300 hypergeometric test for (D) $$p \leq 3.04$$ × 10−93.
We parsed all the GO-terms from all the analyses (case/control and PRS in cerebellum and temporal cortex) using search terms from previously reported molecular mechanisms disrupted in AD [14, 33]. The search terms were grouped in eight categories, ageing/senescence, death/apoptosis, neuron/synapse, glial cell populations, amyloid, immune response, stress response, lipid/cholesterol/fatty acid metabolism. GO-terms matching any of the search terms and are statistically significant in at least one analysis were retained and sorted by the mean -log10(p) FDR across all the analyses. The most statistically significant categories were immune and stress response, asserting an important role of the immune system in the development of AD [14] (Fig. 5a). The least significant were glial cell populations and amyloid. This analysis does not take into account the overlap of genes within different GOs and the overall redundancy of GO terms. It is of note that in all the differential gene-expression analyses (case/control and PRS) we included age of death as a fixed covariate and despite this, ageing GO term is still a significant molecular mechanism associated with the development of AD.Fig. 5AD GWAS and novel mechanisms statistically significant in MayoRNAseq temporal cortex and cerebellum (case/control & PRS).A AD GWAS mechanisms (details are provided in the supplementary methods). B Novel AD disrupted mechanisms. Heatmap of GO terms that are statistically significant in at least one dataset (cas/con & PRS MayoRNAseq temporal cortex and cerebellum). cascon Case/control analysis; CER cerebellum; TEMP temporal cortex. Heatmap p-values are capped at 1.0 × 10−30. blue colour represents down-regulated GOs and red-colours represent up-regulated GOs. All full GO term names from the up and down-regulated GO term results were searched using stress, immun, neuro/synap, death/apoptosis, lipid/cholesterol/fatty, aging/senescence, glia/astrocyte, abeta, endosome, golgi, reticulum and mitochond/respir and GO terms selected if FDR p value was ≤0.05. GO terms within each category were ordered by mean −log10 p and the top 8 selected for visualisation (3 for lipid and cholesterol and 2 for fatty acid metabolism).
Even though, the most significantly disrupted AD GWAS-associated molecular mechanisms were immune/stress response and death/apoptosis, there were other statistically significant GO-terms that have not been reported associated with AD previously and were shared between the case/control and PRS analyses in cerebellum and temporal cortex. These included variety of respiration-related processes (e.g. respiratory electron transport chain, mitochondrial inner membrane), *Golgi apparatus* and ER (Fig. 5b).
## Discussion
We performed an integrative transcriptomics analysis using case/control and genetic liability paradigms. The main aim of the study was to try to understand the biological correlates of elevated common variant liability to AD, and their relationship with these associated with AD per se.
Our overall findings suggest that disrupted biological pathways associated with affected status and increased PRS show remarkable profile similarities with respect to biological pathways derived from gene-expression (bulk brain-derived RNA-seq). In temporal cortex, we found evidence for a modest degree of similarity with respect to genes that are differentially expressed in AD cases compared to controls, and those are associated with increased PRS values. However, the degree of similarity between case status and elevated PRS was much stronger at the level of the GO-term enrichments for differentially expressed genes. This suggests a disease heterogeneity in terms of changes in the gene-expression of individual genes [34], but a convergence in terms of disrupted disease biological mechanisms underlying AD. Crucially, this also suggests that both a case/control and PRS classifications elucidate similar molecular mechanisms. There was some evidence for tissue specificity for the associations with PRS, higher PRS being associated with down-regulation of neuronal process genes in temporal cortex, but up-regulation of the same categories in cerebellum. In contrast, there was limited tissue specificity when the dataset was analysed as a case/control sample.
*Our* gene ontology analysis of differential gene-expression in cases vs. controls shows a degree of convergence with analogous analyses of GWAS studies [14, 35, 36], highlighting immune (both adaptive and innate) and stress response, lipid, fatty acids and cholesterol metabolisms, endosome and cellular/neuronal death. Our results also suggest a significant involvement of previously less well characterised processes in AD. These include the involvement of cellular structures (ER, ER stress, Golgi apparatus, actin cytoskeleton, lamellipodium) and cellular mitochondrial respiration and secretion (exocytosis and endocytosis). Most of the AD GWAS implicated loci are non-coding [14, 29] and choosing the closest gene to an index variant could miss genes that are further away or miss other regulatory mechanisms. Therefore we did not expect to find enrichment of GWAS hits (closest genes) among the differentially expressed genes, although some SNPs have been shown to be directly related to AD [37]. Nevertheless, there was a significant enrichment of differentially expressed genes in the temporal cortex associated with PRS. Thus, some of the putative GWAS implicated genes, as defined as those closest to the associated index SNP at the locus are also likely to show a differential gene-expression in relationship with PRS in temporal cortex. Tissue specificity is also likely to account for some of the differences. The top ranked genes among the differential expression gene list include HAVCR2, MS4A6A, INPP5D, ECHDC3, SPI1, ADAMTS4, ADAMTS1, CR1, IL34, PICALM, HLA-DRB1, CD33, APH1B, FERMT2, and PLCG2, although only HAVCR2 and MS4A6A passed FDR correction. Strikingly, $69\%$ ($\frac{52}{75}$; $$p \leq 2.79$$ × 10−04) of all GWAS implicated genes were up-regulated in response to different PRS among the temporal cortex samples. While it is beyond the scope of this work, this result suggests a potential common regulatory mechanism or mechanisms. MS4A6A, INPP5D and SPI1 have been previously shown to be dysregulated specifically in microglial cells [33, 38, 39]. Furthermore, the GO term microglial cell activation involved in immune response (GO:0002282) was significantly disrupted in temporal cortex with respect to PRS and it comprises TYROBP, TREM2, GRN and IL33. TYROBP was significantly up-regulated in response to higher PRS in temporal cortex and has been shown as a strategic and causal regulator in several microglial activation signalling cascades and the complement pathway in late onset AD [40]. Even though the gene-expression data we used are brain-derived (cerebellum and temporal cortex) bulk RNA-seq, we found several disrupted GO terms specifically related to glial cells (Fig. 5a). Glial and microglia-related GO terms were not the top ranked GO terms, but it is remarkable that this signal is detectable in bulk brain-derived RNA-seq.
The strongest GO-terms enriched in all datasets (both case/control and PRS) were the ER, Golgi apparatus, mitochondria and associated mitochondrial respiratory chain complexes. These cellular structures have received relatively little attention in AD, although both ER and mitochondrial function have been shown to be altered in AD [41–44]. The ER-mitochondria interaction is tightly linked to changes in lipid and cholesterol metabolism pathways [44], both of which have been found to be significantly disrupted mechanisms in all datasets used in this work. Furthermore, Aß interacts with ER, *Golgi apparatus* and mitochondria to disrupt their normal function [45].
Although age is one of the main risk factors for the development of AD, there is little understanding of the molecular mechanisms involved in this relationship. Most of the AD genetic and genomic statistical analysis use age at death or age of onset to account for the differences in chronological age of research participants and ageing is interchangeably used with age. In this study, despite adjusting our differential gene-expression analysis for age at death, we still found the GO term ageing to be enriched for genes that are up-regulated in a case/control and in response to higher PRS. This suggests that on average the gene-expression of ageing-related genes is markedly changed in individuals with AD as compared to controls and with respect to PRS. This indicates that the use of chronological age in the statistical modelling of genetic/genomic data in AD-research could be flawed. Following Horvath’s seminal paper on estimating biological age using an epigenetic clock [46], AD individuals have indeed been shown to exhibit an accelerated epigenetic clock and the rate might be also different in different brain regions [47]. Thus, constructing such epigenetic clocks in AD individuals could help delineate the difference between ageing and chronological age and provide further understanding of AD development.
Our study is an integrative computational approach of publicly available data to try to highlight the biological processes associated with PRS in comparison to case/control classification in AD. Our results point to a considerable heterogeneity in terms of changes in gene-expression with respect to case/control design and genes associated with PRS, but a convergence in terms of disrupted biological pathways, including novel and previous GWAS implicated biological processes and cellular structures.
## Supplementary information
Supplementary materials Supplementary Data The online version contains supplementary material available at 10.1038/s41380-022-01926-8.
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|
---
title: Hydroxychloroquine lowers Alzheimer’s disease and related dementias risk and
rescues molecular phenotypes related to Alzheimer’s disease
authors:
- Vijay R. Varma
- Rishi J. Desai
- Sheeja Navakkode
- Lik-Wei Wong
- Carlos Anerillas
- Tina Loeffler
- Irene Schilcher
- Mufaddal Mahesri
- Kristyn Chin
- Daniel B. Horton
- Seoyoung C. Kim
- Tobias Gerhard
- Jodi B. Segal
- Sebastian Schneeweiss
- Myriam Gorospe
- Sreedharan Sajikumar
- Madhav Thambisetty
journal: Molecular Psychiatry
year: 2022
pmcid: PMC10005941
doi: 10.1038/s41380-022-01912-0
license: CC BY 4.0
---
# Hydroxychloroquine lowers Alzheimer’s disease and related dementias risk and rescues molecular phenotypes related to Alzheimer’s disease
## Abstract
We recently nominated cytokine signaling through the Janus-kinase–signal transducer and activator of transcription (JAK/STAT) pathway as a potential AD drug target. As hydroxychloroquine (HCQ) has recently been shown to inactivate STAT3, we hypothesized that it may impact AD pathogenesis and risk. Among 109,124 rheumatoid arthritis patients from routine clinical care, HCQ initiation was associated with a lower risk of incident AD compared to methotrexate initiation across 4 alternative analyses schemes addressing specific types of biases including informative censoring, reverse causality, and outcome misclassification (hazard ratio [$95\%$ confidence interval] of 0.92 [0.83–1.00], 0.87 [0.81–0.93], 0.84 [0.76–0.93], and 0.87 [0.75–1.01]). We additionally show that HCQ exerts dose-dependent effects on late long-term potentiation (LTP) and rescues impaired hippocampal synaptic plasticity prior to significant accumulation of amyloid plaques and neurodegeneration in APP/PS1 mice. Additionally, HCQ treatment enhances microglial clearance of Aβ1-42, lowers neuroinflammation, and reduces tau phosphorylation in cell culture-based phenotypic assays. Finally, we show that HCQ inactivates STAT3 in microglia, neurons, and astrocytes suggesting a plausible mechanism associated with its observed effects on AD pathogenesis. HCQ, a relatively safe and inexpensive drug in current use may be a promising disease-modifying AD treatment. This hypothesis merits testing through adequately powered clinical trials in at-risk individuals during preclinical stages of disease progression.
## Introduction
Advances in understanding the basic biology of Alzheimer’s Disease (AD) have not translated into effective treatments [1]. Traditional drug discovery approaches have focused on modifying either amyloid plaque or neurofibrillary tangle pathologies, which may be downstream events in a cascade that is initiated years before these pathologies appear. Therefore, identifying the earliest molecular abnormalities in disease progression may be key to developing effective treatments for AD. Equally importantly, there is growing consensus that pharmacological modulation of multiple key pathogenic pathways simultaneously may be preferable to agents against single targets [2].
We recently defined a hypothetical network of interacting and intersecting metabolic pathways in Alzheimer’s disease, linked to dysregulation in brain glycolysis- the Alzheimer’s Disease Aberrant Metabolism (ADAM) network [3]. We nominated genetic regulators of metabolic and signaling reactions in the ADAM network as plausible AD drug targets. Cytokine signaling through the Janus-kinase–signal transducer and activator of transcription (JAK/STAT) pathway was nominated as one such drug target for pharmacoepidemiologic analyses in the Drug Repurposing for Effective Alzheimer’s Medicines (DREAM) study [3]. Prior studies have suggested that dysregulation in this pathway may be associated with neurodegenerative diseases [4, 5] and may therefore may be a plausible therapeutic target [6–8]. We recently showed that disease modifying antirheumatic drugs (DMARDs) including tofacitinib (a JAK inhibitor), and 2) tocilizumab (an interleukin [IL]-6 inhibitor) were not associated with risk of AD and related dementias (ADRD) while TNF inhibitors may reduce risk of ADRD among patients with cardiovascular disease [9]. We additionally showed that C188-9, an experimental STAT3 inactivator currently in human clinical trials of cancer, rescued several molecular phenotypes relevant to AD in cell culture-based phenotypic assays [10].
Among existing FDA approved treatments, hydroxychloroquine (HCQ), a brain-penetrant DMARD, was recently shown to impact the JAK/STAT pathway through the inactivation STAT3 in lung adenocarcinoma cells suggesting a novel pharmacological approach in cancer chemotherapy [11]. We therefore hypothesized that HCQ may also impact AD pathogenesis and risk through the inactivation of STAT3.
In this study (Fig. 1) we first used a large, real-world clinical dataset to assess whether exposure to HCQ in older individuals lowers risk of incident Alzheimer’s disease and related dementias (ADRD). We then tested whether HCQ can restore impaired hippocampal synaptic plasticity in the APP/PS1 transgenic mouse model of AD and whether HCQ rescues molecular abnormalities associated with AD pathogenesis in cell culture-based phenotypic assays. Finally, we tested whether HCQ inactivates STAT3 in microglia, astrocytes, and neurons and whether restoration of impaired hippocampal synaptic plasticity is associated with STAT3 inactivation. Together, our results suggest that HCQ targets multiple molecular abnormalities in AD and may be a novel disease-modifying treatment in individuals in preclinical stages of AD progression. These findings merit confirmation in adequately powered human clinical trials. Fig. 1Study design and key findings. A We first demonstrated that in a large, real-world clinical dataset using Medicare claims data, exposure to HCQ reduces risk of incident ADRD in RA patients relative to the active comparator, methotrexate (MTX). B We next showed that HCQ rescues impaired hippocampal synaptic plasticity assessed by late long-term potentiation (LTP) in the APP/PS1 transgenic mouse model of AD. C We demonstrated that HCQ rescues molecular abnormalities associated with AD including reduction in LPS-induced neuroinflammation, increase in Aβ1-42 phagocytosis by microglia; and lowering of tau phosphorylation. D We finally demonstrated that HCQ inactivates STAT3 in microglia, astrocytes, and neurons. HCQ hydroxychloroquine, AD Alzheimer’s disease, LPS bacterial lipopolysaccharide, IL-1β interleukin 1 beta, TNF-α tumor necrosis factor alpha, Aβ1-42 amyloid-beta 1-42, APP/PS1 double transgenic mice expressing mutant human amyloid precursor protein and mutant human presenilin 1, SC Schaffer collateral pathway, CA1 cornu ammonis 1, CA3 cornu ammonis 3, MF mossy fiber, Rec recording electrode, S1 apical dendritic input, S2 basal dendritic input, MPP medial perforant path, fEPSP field excitatory postsynaptic potentials, STET strong tetanization, ADRD Alzheimer’s disease and related dementias, MTX methotrexate, RA rheumatoid arthritis.
## HCQ and clinically diagnosed incident ADRD in medicare claims data analyses
The full study protocol for patient-level analysis in Medicare claims was pre-registered on clinicaltrials.gov prior to data analysis (NCT04691505) and contains detailed information on implementation including all codes that were used to identify study variables to allow future replication. These analyses were performed within the ongoing DREAM study [3]. The following sections summarize key methodologic details.
## Data source
We used Medicare Fee-For-Service claims data from 2007 through 2017. Medicare Part A (hospitalizations), B (medical services), and D (prescription medications) claims are available for research purposes through the Centers for Medicare and Medicaid Services (CMS). A signed data use agreement with the CMS was available and the Brigham and Women’s Hospital’s Institutional Review Board approved this study. All the analyses were conducted using anonymized patient data, therefore the institutional review board waived the requirement for informed consent.
## Study cohort
We employed a new user, active comparator, observational cohort study design comparing HCQ with methotrexate (MTX). We selected MTX as a comparator to HCQ because both treatments are used first-line for RA. The patients were required to have continuous enrollment in Medicare parts A, B, and D during the baseline period of 365 days before initiation date of MTX or HCQ, which was defined as the cohort entry date. Patients were required to have ≥1 diagnosis codes indicating rheumatoid arthritis during the baseline period but no prior use of any disease modifying antirheumatic treatments. We excluded patients with existing diagnoses of ADRD any time prior to and including cohort entry date to focus on incident events. We further excluded patients with nursing home admission in the 365 days prior to and including cohort entry date as medication records for short nursing home stays are unavailable in Medicare claims.
## Outcome measurement
We identified the endpoint of ADRD based on diagnosis codes, recorded on 1 inpatient claim or 2 outpatient claims indicating Alzheimer’s disease, vascular dementia, senile, presenile, or unspecified dementia, or dementia in other diseases classified elsewhere (see Supplementary Table 1). When validated against a structured in-home dementia assessment, Medicare claims-based dementia identification is reported to have a positive predictive value (PPV) in the range of $65\%$ to $78\%$ [12].
## Alternative analytic approaches
To accommodate various uncertainties involved in pharmacoepidemiologic investigations focused on ADRD risk, we employed the following alternative analyses (Supplementary Fig. 1) recommended as good practice [13] and detailed earlier [3].
## Analysis 1- ‘As-treated’ follow-up approach
In this approach, the follow-up started on the day following the cohort entry date and continued until treatment discontinuation or switch (to comparator treatment), insurance disenrollment, death, or administrative endpoint (December 2017). A 90-day ‘grace period’ after the end of the expected days-supply of the most recently filled prescription was considered to define the treatment discontinuation date to accommodate for suboptimal adherence during treatment periods.
## Analysis 2- ‘As-started’ follow-up approach incorporating a 6-month ‘induction’ period
In this approach, we incorporated a 6-month induction period after the cohort entry date before beginning the follow-up for ADRD and followed patients for a maximum of 3 years regardless of subsequent treatment changes or discontinuation, similar to an intent-to-treat approach in randomized controlled trials. This follow-up approach addresses concerns related to informative censoring if patients discontinue or if physicians de-prescribe the treatments under consideration because of memory problems associated with ADRD, but the diagnosis is not recorded in the electronic healthcare records until after the drug is discontinued.
## Analysis 3- Incorporating a 6-month ‘symptoms to diagnosis’ period
In this approach, we assigned an outcome date that is 6 months before the first recorded ADRD date and excluded last 6 months of follow-up for those who are censored without an event to account for the possibility that ADRD symptoms likely appear some time before a formal diagnosis is recorded in insurance records, which leads to misclassification of ADRD onset.
## Analysis 4- Alternate outcome definition
In this approach, the outcome was defined using a combination of a diagnosis code and ≥1 prescription claim for a symptomatic treatment [donepezil, galantamine, rivastigmine, and memantine] occurring within 6 months of each other with outcome date assigned to second event in the sequence. Use of medication records to identify dementia has been reported to result in >$95\%$ PPV in a previous validation study [14].
## Pre-treatment patient characteristics
We identified 73 patient characteristics, measured during the 365-days before the cohort entry date. The following set of variables were included: [1] demographic factors such as age, gender, race, socioeconomic status proxies, [2] risk factors for ADRD identified in previous studies such as diabetes, stroke, and depression [15–17], [3] lifestyle factors such as smoking as well as use of preventive services, including screening mammography and vaccinations, to account for healthy-user effects [18]; measures for use of various healthcare services before cohort entry including number of distinct prescriptions filled, number of emergency department visits, hospitalizations, and number of physician office visits to account for patients’ general health and contact with the healthcare system to minimize the possibility of differential surveillance [19]; frailty indicators based on composite scoring scheme [20] to address potential confounding by frailty, and [4] comorbid conditions and co-medications including prior use of steroids and opioids. Please refer to Supplementary Table 2 for a full list of covariates included.
## Statistical analyses
We used a propensity-score (PS) [21]-based approach to minimize confounding in this study. The PS was calculated as the predicted probability of initiating the exposure of interest (HCQ) versus the reference drug (MTX) conditional on baseline covariates using multivariable logistic regression. On average, patients with similar PSs have similar distribution of potential confounders used to estimate the PS. Therefore, analyses conditioned on the PS provide effect estimates that account for confounding by these measured characteristics. For all our analyses, initiators of the exposure of interest (HCQ) were matched with initiators of the reference exposure (MTX) based on their PS [22]. Pair matching was conducted using a nearest-neighbor algorithm, which seeks to minimize the distance between propensity scores in each pair of treated and reference patients [23], and a caliper of 0.025 on the natural scale of the PS was used to ensure similarity between the matched patients [24]. Multiple diagnostics for PS analysis were evaluated including PS distributional overlap before and after matching to ensure comparability of these groups [25] and balance in each individual covariate between two treatment groups using standardized differences [26].
In the PS-matched population, incidence rates along with $95\%$ confidence intervals for ADRD were estimated for the HCQ and MTX groups. The competing risk of death could have been of concern for the current set of analyses if mortality was frequent among patients included in the cohort and if differences in the risk of death between treatment and reference groups were substantial. Therefore, we calculated cumulative incidence of ADRD using cumulative incidence functions that account for competing risk by death and provided cause-specific hazard ratios from Cox proportional hazards regression model [27]. We used cumulative incidence functions that estimate the probability of experiencing ADRD at each time point considering death as a competing event and separately estimating survival probability. Further, Cox proportional hazards regression model was used to estimate cause-specific hazard ratios for ADRD accounting for competing risk of death by censoring on all-cause mortality. The cumulative incidence plots were inspected visually for evidence of violation of the non-proportionality assumption; gross violations were not identified. Pre-specified subgroup analyses were conducted based on age, sex, and baseline cardiovascular disease.
We conducted two secondary analyses: first we restricted the outcome definition to only include codes for AD. Second, we switched the reference drug from MTX to leflunomide, the third most commonly used non biologic DMARD in Medicare after MTX and HCQ. These secondary analyses were used to evaluate the consistency of any effects in the primary analyses.
Assuming an incidence rate of incident AD dementia of 1.23 per 100 person-years as reported previously based on the Baltimore Longitudinal Study of Aging [28], we estimated that a total of 9625 patients treated with candidate treatment and 9625 matched patients treated with reference medication will be needed to detect $20\%$ reduction in the incidence rate of dementia over 3 years with $80\%$ power. Our study sample exceeded this estimated sample size requirement.
Analyses of the Medicare claims data were performed using the Aetion Evidence Platform v4.32 (incl. R v3.4.2), which has been scientifically validated by accurately repeating a range of previously-published studies [29] and by replicating [30] or predicting clinical trial findings [31].
Statistical analysis was performed in GraphPad Prism 9.1.2. Group differences were evaluated for each test item separately by one-way ANOVA followed by Dunnett’s multiple comparison test versus VC or lesion control.
Digitized images were obtained, processed, and quantified with ImageLab version 6.1 (BioRad Laboratories) and densitometry data was analyzed with ImageJ. We tested β-Actin (loading control)-normalized total STAT3, p-STAT3 and the ratio of p-STAT3/total STAT3 in HCQ-treated and MTX-treated cells compared to untreated control cells using the one-way ANOVA test (parametric). We additionally used the Wilcoxon rank-sum test (non-parametric) to confirm that results were robust to distributional assumptions. Significant differences were indicated as $p \leq 0.05.$
Digitized images were obtained, processed, and quantified using ImageJ (NIH software). We examined tubulin (loading control)-normalized total STAT3, p-STAT3 and the ratio of p-STAT3/total STAT3 in APP/PS1 and WT, HCQ treated and untreated hippocampal slices. All values were calculated in relation to the control group (i.e., WT). Similar to western blot analytic methods described above, group differences comparing HCQ-treated cells to untreated control cells were evaluated using the one-way ANOVA test (parametric). We additionally used the Wilcoxon rank-sum test (non-parametric) to confirm that results were robust to distributional assumptions. Significant differences were indicated as $p \leq 0.05.$
## Animals
All animal experiments and procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of National University of Singapore. We used a transgenic mouse model of AD, which expresses a mutated chimeric mouse/human APP and the exon-9-deleted variant of human PS1, both linked to familial AD, under the control of a prion promoter element (APPSwe/PS1dE9), which we denote as APP/PS1 [32, 33]. A total of 35 hippocampal slices (21 APP/PS1 and 14 wild type (WT) slices) were prepared from 9 APP/PS1 mice and 7 WT mice which were 4–5 months old. Animals were housed under 12 h light/12 h dark conditions with food and water available ad libitum. Power calculations were not carried out for APP/PS1 hippocampal synaptic plasticity experiments. We followed standard procedures for our vitro slice physiology for long-term functional plasticity studies. This includes a sample size of $\frac{7}{9}$ slices as well as nonparametric testing of differences. These are similar sample sizes used in prior LTP experiments from Sajikumar et al. Animals were not randomized to experimental groups and investigators were not blinded to allocation.
## Hippocampal slice preparation
Animals were anaesthetized briefly using CO2 and were decapitated. Brains were quickly removed in 4 °C artificial cerebrospinal fluid (aCSF), a modified Krebs-Ringer solution containing the following (in mM): 124 NaCl, 3.7 KCl, 1.2 KH2PO4, 1 MgSO4·7H2O, 2.5 CaCl2·2H2O, 24.6 NaHCO3, and 10 d-glucose. The pH of aCSF was between 7.3 and 7.4 when bubbled with $95\%$ oxygen and $5\%$ carbon dioxide (carbogen). Both right and left hippocampi were dissected out in cold (2–4 °C) aCSF, which was continuously bubbled with carbogen [34–36]. Transverse hippocampal slices of 400 μm thickness were prepared from the right and left hippocampus using a manual tissue chopper (Stoelting, Wood Dale, Illinois), transferred onto a nylon net placed in an interface chamber (Scientific Systems Design, Ontario, Canada) and incubated at 32 °C at an aCSF flow rate of 1 ml/min and carbogen consumption of 16 l/h. The entire process of animal dissection, hippocampal slice preparation and placement of slices on the chamber was done within approximately five minutes to ensure that hippocampal slices were in good condition for electrophysiology studies. The slices were incubated for at least 3 h before starting the experiments. Detailed description of these methods has been reported previously [35, 36].
## Field potential recordings
In all the electrophysiology recordings, two-pathway experiments were performed. Two monopolar lacquer-coated stainless-steel electrodes (5MΩ; AM Systems, Sequim) were positioned at an adequate distance within the stratum radiatum of the CA1 region for stimulating two independent synaptic inputs S1 and S2 of one neuronal population, thus evoking field excitatory postsynaptic potentials (fEPSP) from Schaffer collateral/commissural‐CA1 synapses. One electrode (5MΩ; AM Systems) represented as ‘rec’ was placed in the CA1 apical dendritic layer for recording fEPSP. After the pre‐incubation period, a synaptic input-output curve (afferent stimulation vs. fEPSP slope) was generated. Test stimulation intensity was adjusted to elicit fEPSP slope of $40\%$ of the maximal slope response for both synaptic inputs S1 and S2. The signals were amplified by a differential amplifier, digitized using a CED 1401 analog-to-digital converter (Cambridge Electronic Design, Cambridge, UK) and monitored online with custom-made software. To induce late long-term potentiation (LTP), a “strong” tetanization (STET) protocol consisting of three trains of 100 pulses at 100 Hz (single burst, stimulus duration of 0.2 ms per polarity), with an inter‐train interval of 10 min, was used. In all experiments, a stable baseline was recorded for at least 30 min. Four 0.2-Hz biphasic, constant current pulses (spaced at 5 s) given every five min were used for baseline and post-induction recordings also and the average slope values from the four sweeps was considered as one repeat while used for plotting. Initial slopes of fEPSPs were expressed as percentages of baseline averages. A series of pulses ranging from 0, 10, 20, 30, 40, 50, 70, 100 microamperes were applied to generate an input-output curve. Graphs are plotted as stimulus intensity versus fEPSP slope. Paired pulse ratio (PPR) was evoked using an interstimulus interval of 50 ms at 40 % of maximum stimulus intensities. PPR was expressed as the ratio of the fEPSP slope of second stimulus to the first stimulus [37].
## Pharmacology
HCQ Sulphate (Selleckchem, catalog, No-S4430) was stored at −20 °C as 50 mM stock in deionized water. Before application, the stock solution was diluted to a final concentration of 25 µM or 50 µM in aCSF and bath-applied for a total of 60 min, 30 min before and 30 min after the STET or unless otherwise specified.
MTX was stored similarly and diluted to a final concentration of 50 nM in aCSF and bath-applied for a total of 60 min, 30 min before and 30 min after the STET or unless otherwise specified.
## Statistical analysis
All data are represented as mean ± standard error of the mean (SEM). The fEPSP slope value expressed as percentages of average baseline values per time point was subjected to statistical analysis using Graph Pad Prism (Graph Pad, San Diego, CA, USA). Nonparametric tests were used considering lack of normality due to small sample size. Wilcoxon signed rank test (Wilcox test) was used to compare fEPSP values within one group and Mann-Whitney U test (U-test) was used when data were compared between groups. Statistical comparisons for input-output (I/O) curve and paired pulse facilitation (PPF) experiments were performed using two-way ANOVA test. $p \leq 0.05$ was considered as the cutoff for statistically significant differences.
## HCQ and AD-related phenotypes in cell culture
We tested whether HCQ could rescue molecular phenotypes relevant to AD including Aβ1-42 clearance, Aβ secretion, Aβ toxicity, tau phosphorylation, lipopolysaccharide (LPS)-induced neuroinflammation, cell death due to trophic factor withdrawal and neurite outgrowth. LPS-induced neuroinflammation assays were performed on BV-2 cells (immortalized murine microglial cells) and adult 5xFAD microglia. Aβ1-42 clearance studies were performed on BV-2 cells, adult 5xFAD microglia, and iPSC derived microglia from an adult human AD patient. Aβ secretion assay was performed on human APP overexpressing H4-hAPP cells, Aβ toxicity studies were performed on primary hippocampal neurons, tau phosphorylation in tau441 overexpressing SH-SY5Y cells, and cell death due to trophic factor withdrawal and neurite outgrowth on mouse primary cortical neurons. Supplementary Table 3 includes descriptions of the phenotypic assays and HCQ concentrations tested. Detailed descriptions of phenotypic assays are included in Supplementary Text. Cell lines used for phenotypic assays are regularly tested for mycoplasma contamination by PCR of cell culture supernatants. Only mycoplasma free cells were used for experiments. All experiments were conducted on cells that have been passaged no more than 5 times upon thawing. Transgenic cell lines are regularly assessed for transgene expression on protein level.
## HCQ and STAT3 inactivation in microglia, astrocytes, and neurons
Immortalized human microglia HMC3 cells (ATCC CRL − 3304) and astrocytoma 1321N1 cells (Sigma-Aldrich; derived from human brain astrocytoma; [38]) were cultured in Eagle’s minimum essential medium (EMEM) supplemented with $10\%$ fetal bovine serum (FBS, Gibco) and $1\%$ antibiotics and antimycotics (Gibco). Neuroblastoma SK-N-BE[2]-M17 (M17) (ATCC CRL-2267) cells were cultured in a 1:1 mixture of EMEM and F12 media, supplemented with $10\%$ FBS plus $1\%$ antibiotics and antimycotics.
For primary cultures of embryonic cortical neurons, timed-pregnant mice were obtained from the Jackson Laboratory. Cultures were prepared from embryonic day E18.5 cerebral tissues as described previously [39]. Pregnant mice were killed by fast cervical dislocation, embryos and embryo brains were removed, and the cerebral hemisphere was extracted in sterile Hank’s balanced saline solution (HBSS). Brain tissues were incubated in $0.25\%$ trypsin-EDTA for 30 min at 37 °C and then transferred to Dulbecco’s Modified Eagle Medium (DMEM) containing $10\%$ fetal bovine serum (DMEM+). The tissues were transferred to neurobasal (NB) medium containing B27 supplements, 2 mM L-glutamine, antibiotics and antimycotics (Gibco), and 1 mM HEPES and dissociated by trituration using a fire-polished Pasteur pipet. The dissociated cells were seeded into polyethyleneimine-coated plastic culture dishes at a density of 60,000 cells/cm2 and cultured in the same B27-containing NB medium. Experiments were performed 5 days later.
Treatment with HCQ Sulfate (Selleckchem, Catalog No. S4430) for 48 h was performed at a concentration of 50 µM. Treatment with Methotrexate (Selleckchem, Catalog No. S5097) for 48 h was performed at a concentration of 50 nM. Both treatment doses were previously assessed to confirm that they did not produce adverse effects on cell viability assessed by direct cell counting.
## Western blot analyses
Protein extracts were obtained by lysing cells with a denaturing buffer containing $2\%$ sodium dodecyl sulfate (SDS) (Sigma-Aldrich) in 50 mM HEPES. After boiling and sonication, whole-cell protein extracts were size-fractionated through polyacrylamide gels and transferred to nitrocellulose membranes (Bio-Rad). Membranes were blocked with $5\%$ non-fat dry milk and immunoblotted. Primary antibodies were employed that recognized total STAT3 (124H6) (Cell Signaling, 9139 T) and phosphorylated STAT3 (p-STAT3) (Tyr705) (D3A7) XP® (Cell Signaling, 9145 S), the primary phosphorylated STAT3 residue.
For Western blot analyses, we studied four groups (3 mice in each group, 5 months old) in two experiments (HCQ at 25 or 50 µM): (i) WT, (ii) APP/PS1, (iii) WT with HCQ 25 or 50 µM and (iv) APP/PS1 with HCQ 25 or 50 µM. Hippocampal slices were treated with drug 30 min before and after STET. In each group, the slices were collected one hour after STET. Tissues around the recording electrodes in CA1 region were cut carefully and snap frozen in liquid nitrogen and stored at −80 °C. Protein extraction was performed using Tissue Protein Extraction reagent (T-PER; Thermo Scientific, USA) supplemented with protease and phosphatase inhibitor (Thermo Scientific, USA) according to the manufacturer’s protocol, followed by centrifugation for 5 min at 10,000 rpm at 4 °C. Protein concentration was determined using the Bradford assay (Bio-Rad). Appropriate protein concentration was added to the sample buffer and heated at 95 °C for 10 min before separation on SDS-polyacrylamide gels. Gels were transferred to PVDF membranes (Bio-Rad) in a wet transfer cell (Bio-Rad) for 1.5 h at 100 V. The membranes were blocked with $5\%$ w/v non-fat dry milk in 1X TBST and immunoblotted with primary antibodies. The primary antibodies with their concentrations used were as follows: rabbit anti-phospho STAT3 (Tyr705) (1:1000, Cell Signaling), mouse anti-STAT3 (1:2000, Cell Signaling) and mouse anti-tubulin (1:20000; Sigma-Aldrich). The membranes were incubated with secondary peroxidase-conjugated antibodies. Signals were generated by using the SuperSignal® West Pico Chemiluminescent Substrate (Thermo Scientific, USA).
## HCQ lowers risk of clinically diagnosed incident ADRD compared to MTX in medicare claims data
To test whether exposure to HCQ lowers ADRD risk in older individuals, we used longitudinal insurance claims data from Medicare beneficiaries (NCT04691505). We assembled a cohort of patients with rheumatoid arthritis (RA), an indication for which HCQ is routinely used, to identify initiators of HCQ or an active comparator, methotrexate (MTX). After controlling for 73 confounding variables through PS matching, we estimated treatment effects in four alternative analyses designed to address various uncertainties associated with claims-based analyses of dementia risk including exposed person-time misclassification, reverse causation, informative censoring, and misclassification of outcome onset as described previously (Supplementary Fig. 1; DREAM study) [3].
Of the 881,432 patients filling at least one prescription for the drugs of interest (HCQ or MTX) during our study period, we included 109,124 patients with RA who met all inclusion criteria (54,562 HCQ initiators 1:1 PS matched to 54,562 MTX initiators). Supplementary Table 2 provides patient characteristics before and after PS matching. As HCQ and MTX are both used as first-line DMARDs for RA, we noted that most characteristics were balanced even before PS matching indicating sufficient clinical equipoise and comparability of the two exposure groups. Average age of included patients was 74 years, $76\%$ were females, and $84\%$ were White. PS-matching minimized all residual imbalances in measured characteristics.
Supplementary Table 4 summarizes the incidence rates of clinically diagnosed incident ADRD per 1000 person years ($95\%$ confidence intervals [CI]) in the two exposure groups across the four analyses showing lower incidence rates for ADRD among individuals exposed to HCQ compared to MTX. Figure 2 summarizes cumulative incidence of ADRD among HCQ initiators compared to MTX initiators after accounting for competing risk by mortality; results from all four analyses indicate that after approximately 2 years of treatment, individuals on HCQ had lower cumulative incidence of ADRD compared to MTX. Figure 3 summarizes the crude and PS-matched comparative risk of ADRD in HCQ compared to MTX groups; results indicated that risk of ADRD was consistently lower among HCQ initiators. In Analysis 1 where patient follow-up time was censored at discontinuation of the initial treatment (“as-treated” approach), HCQ initiators had an $8\%$ lower rate of ADRD compared to MTX initiators (HR 0.92 [$95\%$ CI 0.83–1.00]). In Analysis 2 where we incorporated a 6-month induction period to eliminate potential reverse causality and continued follow-up for 3 years regardless of treatment change or discontinuation to minimize the impact of informative censoring in an ‘as started’ follow-up scheme, HCQ was significantly associated with a $13\%$ lower rate (HR 0.87 [$95\%$ CI 0.81–0.93]). In Analysis 3 where we accommodated a 6-month ‘symptom to claims’ period to address misclassification of outcome onset, HCQ was significantly associated with a $16\%$ lower rate (HR 0.84 [$95\%$ CI 0.76–0.93]). Finally, in Analysis 4, which required symptomatic treatment with cholinesterase inhibitors or memantine along with diagnosis codes to overcome outcome misclassification due to lower specificity of the outcome measurement relying only on diagnosis codes, effect estimates were largely consistent with other approaches (HR 0.87 [$95\%$ CI 0.75–1.01]). We observed no conclusive evidence of heterogeneity in treatment effects across subgroups of age, gender, and baseline cardiovascular function (Supplementary Fig. 2).Fig. 2Cumulative incidence of clinically diagnosed Alzheimer’s and related dementia (ADRD) in rheumatoid arthritis patients treated with methotrexate or hydroxychloroquine, medicare data 2007–2017.A new-user active comparator design with propensity score (PS)-based adjustment for confounding, was used to estimate treatment effects in four alternative analyses. Analyses indicate that cumulative incidence of ADRD among HCQ initiators compared to MTX initiators diverged after approximately 2 years of treatment wherein individuals on HCQ had lower cumulative incidence of ADRD compared to MTX users. The four analyses were designed to address various uncertainties associated with claims-based analyses of ADRD risk: Analysis 1: ‘As-treated’ follow-up approach (MTX: N patients = 54,562, N outcomes = 1096, N person years = 71,029; HCQ: N patients = 54,562, N outcomes = 774, N person years = 56,891); Analysis 2: ‘As-started’ follow-up approach incorporating a 6-month induction period (MTX: N patients = 30,615, N outcomes = 1391, N person years = 68,504; HCQ: N patients = 30,615, N outcomes = 1206, N person years = 68,329); Analysis 3: *Incorporating a* 6-month ‘symptom to diagnosis’ period’ period (MTX: N patients = 25,072, N outcomes = 798, N person years = 43,254; HCQ: N patients = 25,072, N outcomes = 609, N person years = 39,808); and Analysis 4: Alternate outcome definition (MTX: N patients = 54,562, N outcomes = 416, N person years = 71,669; HCQ: N patients = 54,562, N outcomes = 275, N person years = 57,349). See Methods for additional description of analytic approach. HCQ hydroxychloroquine, MTX methotrexate. Fig. 3Comparative risk of clinically diagnosed Alzheimer’s and related dementia (ADRD) in rheumatoid arthritis patients treated with hydroxychloroquine versus methotrexate, medicare data 2007–2017.A new-user active comparator design with PS-based adjustment for confounding, was used to estimate treatment effects in four alternative analyses. Analyses indicate that the that risk of ADRD was consistently lower among HCQ initiators. The four analyses were designed to address various uncertainties associated with claims-based analyses of ADRD risk: Analysis 1: ‘As-treated’ follow-up approach; Analysis 2: ‘As-started’ follow-up approach incorporating a 6-month induction period; Analysis 3: *Incorporating a* 6-month ‘symptom to diagnosis’ period’ and Analysis 4: Alternate outcome definition (See Methods for additional description of analytic approach). HCQ hydroxychloroquine, MTX methotrexate, PS propensity score.
In secondary analyses, restricting the outcome to AD only, we observed similar results comparing HCQ to MTX (Analysis 1: as treated: HR 0.78 [$95\%$ CI 0.65–0.93]). Using leflunomide as the active comparator in place of MTX, we also observed similar results (Analysis 1: HCQ vs leflunomide: as treated: HR 0.76 [$95\%$ CI 0.65–0.88]).
## HCQ rescues impaired hippocampal synaptic plasticity in APP/PS1 mice
To test whether HCQ may rescue impaired hippocampal synaptic plasticity in AD, we studied its effects on late long-term potentiation (LTP), a form of activity-induced synaptic plasticity that has been shown to be impaired prior to significant accumulation of amyloid plaques and neurodegeneration in the APP/PS1 transgenic AD mouse model [33].
Figure 4A illustrates the schematic diagram of the location of electrodes in a hippocampal slice from wild type (WT) and APP/PS1 transgenic mice (ages 4–5 months). Figures 4B, C illustrate late LTP recordings from the hippocampus of non-disease/WT controls and disease (APP/PS1) mice respectively. In WT hippocampal slices, applying strong tetanization (STET; S1 input) resulted in a long-lasting and stable late LTP throughout the recording time period of 180 min (Fig. 4B: filled blue circles). The control input (S2) remained stable throughout the recording time period (Fig. 4B: open blue circles). We observed statistically significant differences in field excitatory postsynaptic potentials (fEPSP) from 1 min until 180 min when compared with its own baseline and with control input S2 (1 min Wilcox, $$p \leq 0.01$$, U-test, $$p \leq 0.0006$$; 60 min Wilcox, $$p \leq 0.01$$, U-test, $$p \leq 0.0006$$; 120 min Wilcox, $$p \leq 0.01$$, U-test, $$p \leq 0.0006$$; 180 min Wilcox, $$p \leq 0.01$$, U-test, $$p \leq 0.0006$$ respectively).Fig. 4Treatment with HCQ rescues late LTP in hippocampal CA1 synapses of APP/PS1 mice. A Schematic representation of a hippocampal slice with electrodes located in the CA1 region. ‘ Rec’ represents the recording electrode positioned in the CA1 region flanked by two stimulating electrodes represented as S1 and S2 in the stratum radiatum to stimulate two independent pathways to a single neuronal population in the Schaffer collateral pathway (sc). B Induction of late LTP by STET in synaptic input S1 in WT mice resulted in a potentiation that remained stable for 180 min (filled blue circles, $$n = 7$$). C Induction of late LTP by STET in synaptic input S1 in APP/PS1 mice resulted only in early LTP in S1 (filled blue circles, $$n = 6$$). D Treatment of hippocampal slices with 25 µM HCQ resulted in late LTP in S1 in APP/PS1 mice (filled blue circles, $$n = 8$$) that was however significantly lower in magnitude than WT late LTP (D vs B). E Treatment of hippocampal slices with 50 µM HCQ resulted in late LTP in S1 in APP/PS1 mice (filled blue circles, $$n = 7$$) similar to WT late LTP (E vs B). F Treatment of hippocampal slices with 50 µM HCQ in WT mice resulted in late LTP in S1 that was similar to untreated WT late LTP (F vs B). In Figs. ( B–F), control input S2 remained stable throughout the recording (open blue circles). G Comparison of input-output curves showed no significant change between WT and APP/PS1 before and after HCQ application. H Comparison of PPR also revealed no significant change in PPF ratio between WT and APP/PS1 mice before and after HCQ application ($$n = 12$$). Error bars in all the graphs indicate ±SEM. Analog traces represent typical fEPSPs of inputs S1 and S2, recorded 15 min before (dotted line), 30 min after (dashed line), and 180 min (solid line) after tetanization in S1 and the corresponding time points in S2. The three solid arrows represent the time of induction of late LTP by STET. Blue rectangular bar represents the time of application of HCQ. Scale bars: vertical, 2 mV; horizontal, 3 ms. HCQ hydroxychloroquine, PPR paired pulse ratio, APP/PS1 double transgenic mice expressing AD pathology (human amyloid precursor protein mutant human presenilin 1), WT wild type mice, LTP long-term potentiation, STET strong tetanization, PPF paired pulse facilitation, SEM standard error of the mean, fEPSP field excitatory postsynaptic potential.
In APP/PS1 hippocampal slices, induction of late LTP by applying STET to S1 resulted in only short-lasting form of LTP (early-LTP) (Fig. 4C: filled blue circles) while the control input S2 remained stable (Fig. 4C: open blue circles). Significant difference was observed in fEPSP only until 170 min, when compared to its own baseline and until 115 min when compared to S2 (170 min Wilcox, $$p \leq 0.03$$, 115 min U-test, $$p \leq 0.04$$ respectively).
We then tested whether HCQ (25 µM) could rescue impaired late LTP in hippocampal slices from APP/PS1 mice. As shown in Fig. 4D, bath-application of HCQ 30 min before and 30 min after the STET induced late LTP in the APP/PS1 hippocampus (Fig. 4D: filled blue circles) which was significantly different from 1 min up to 180 min when compared to its own baseline and S2 (Fig. 4D: open blue circles) (1 min Wilcox, $$p \leq 0.007$$, U-test, $$p \leq 0.0002$$; 60 min Wilcox, $$p \leq 0.007$$, U-test, $$p \leq 0.0002$$; 120 min Wilcox, $$p \leq 0.007$$, U-test, $$p \leq 0.0003$$; 180 min Wilcox, $$p \leq 0.01$$, U-test, $$p \leq 0.01$$ respectively). When compared to WT (Fig. 4D vs 4B), late LTP in HCQ (25 µM) -treated APP/PS1 group was similar in magnitude up to 60 min (U-test, $p \leq 0.05$). From 70 min through 180 min, late LTP in APP/PS1 hippocampus after HCQ application (25 µM) showed that potentiation remained significantly lower than in WT (70 min, U-test, $$p \leq 0.009$$; 180 min, U-test, $$p \leq 0.01$$) indicating a partial rescue of impaired late LTP at this dose (Fig. 4D vs 4B).
To assess whether HCQ exerted dose-dependent effects on late LTP in APP/PS1 mice, we next tested a higher concentration of HCQ (50 µM) in these experiments. As shown in Fig. 4E, HCQ (50 µM) induced late LTP (S1) in the APP/PS1 hippocampus (Fig. 4E: filled blue circles). We observed statistically significant differences in fEPSP compared to its own baseline and S2 (Fig. 4E: open blue circles) up to 180 min (1 min Wilcox, $$p \leq 0.01$$, U-test, $$p \leq 0.0006$$; 60 min Wilcox, $$p \leq 0.01$$, U-test, $$p \leq 0.0006$$; 120 min Wilcox, $$p \leq 0.01$$, U-test, $$p \leq 0.0006$$; 180 min Wilcox, $$p \leq 0.01$$, U-test, $$p \leq 0.004$$ respectively). Late LTP in the HCQ (50 µM) treated APP/PS1 group was similar in magnitude to WT (U-test, $p \leq 0.05$ at 1, 60, 120, 180 min) (Fig. 4E vs 4B), throughout the recording period, indicating a complete rescue of late LTP at the higher dose of HCQ.
We also tested whether HCQ exerted any detrimental effects on hippocampal synaptic plasticity in WT mice. As shown in Fig. 4F, late LTP in HCQ-treated WT hippocampal slices (Fig. 4F: filled blue circles) was similar in magnitude compared to untreated WT (Fig. 4F vs 4B, U-test, $p \leq 0.05$). We observed statistically significant differences in fEPSP from 1 min until 180 min when compared with its own baseline (Wilcox, $$p \leq 0.01$$ at 1, 60, 120, 180 min) as well as with S2 (Fig. 4F: open blue circles) (U-test, $$p \leq 0.001$$ at 1, 60, 120, 180 min) throughout the recording period. In all experiments, control input S2 remained stable throughout the recording time period.
Comparison of input-output (I/O) curves between WT and APP/PS1 before and after HCQ application did not show any significant differences ($$p \leq 0.99$$) (Fig. 4G). Comparison of paired pulse ratio (PPR) in WT and APP/PS1 before and after HCQ did not show a significant difference ($$p \leq 0.09$$), suggesting that HCQ may not affect basal synaptic transmission in either WT or APP/PS1 mice (Fig. 4H).
Using the same experimental design, we additionally tested whether MTX (50 nM) affects impaired hippocampal synaptic plasticity in the APP/PS1 transgenic AD mouse model. Supplementary Fig. 3A indicates that MTX did not exert any detrimental effects on hippocampal synaptic plasticity in WT mice. Supplementary Fig. 3B indicates that MTX did not rescue impaired late LTP in hippocampal slices from APP/PS1 mice; there was no significant difference comparing MTX treated and untreated APP/PS1 hippocampal slices. See Supplementary Text for additional details.
## HCQ rescues AD phenotypes in cell culture
To test whether HCQ rescues molecular outcomes relevant to AD in cell culture-based phenotypic assays, we examined its effects on lipopolysaccharide (LPS)-induced neuroinflammation, Aβ1-42 clearance, Aβ1-42 toxicity, and Aβ secretion, tau phosphorylation, cell death due to trophic factor withdrawal, and neurite outgrowth and neurogenesis.
Treatment of BV-2 microglia with HCQ (2.5 µM, 25 µM) reduced the levels of secreted pro-inflammatory cytokines from microglia in the LPS-induced neuroinflammation assay. A reduction in TNF-α secretion was observed at the highest HCQ concentration (25 µM) and a dose-dependent reduction in IL-6, IL-1β, IL-12p70 and IL-10 was observed at 2.5 µM and 25 µM without any adverse effects on cell viability (Fig. 5A: 1–5).Fig. 5Hydroxychloroquine rescues molecular phenotypes relevant to AD.A Levels of inflammatory cytokines [1] TNFα, [2] IL-6, [3] IL-1β, [4] IL-12p70, and [5] IL-10 in the supernatant of BV-2 microglial cells and [6] IL-6, [7] IL-1β, [8] IL-12p70 in adult 5xFAD microglial cells after 24 h LPS stimulation and HCQ treatment. HCQ significantly reduced secretion of inflammatory cytokines in a dose-dependent manner. B Levels of Aβ1-42 in the [1] supernatant [2] supernatant:lysate Aβ1-42 in BV-2 microglial cells after 24 h treatment with HCQ and [3] supernatant and [4] uptake of pHrodo red positive cells into acidic cell organelles in iPSC derived adult human AD microglial cells after 4h treatment with HCQ. HCQ significantly increased clearance of Aβ1-42 as shown by reduced levels of Aβ1-42 in the supernatant and a lowering of the Aβ1-42 supernatant:lysate ratio and HCQ significantly increased microglial uptake of Aβ1-42. C Levels of [1] total tau and [2] phosphorylated tau (pT231), in lysates from SH-SY5Y cells over-expressing mutant human tau441 (SH-SY5Y-TMHT441) after 24 h treatment with HCQ. HCQ (25 µM) significantly reduced levels of total tau and phosphorylated tau (pT231). Error bars in all bar graphs indicate group mean + standard deviation (SD). Individual values are shown as dots ($$n = 6$$ per group). Group differences comparing HCQ-treated cells to the VC (A & B) or LPS control (C) were evaluated using the one-way ANOVA test followed by Dunnett’s multiple comparison test. Asterisks indicate significant differences between groups: *$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001.$ HCQ hydroxychloroquine, AU arbitrary units, VC vehicle control ($0.1\%$ DMSO), LPS lipopolysaccharide, iPSC induced pluripotent stem cells.
Treatment of MACS isolated adult 5xFAD mouse microglia with HCQ reduced levels of IL-6, IL-12p70, and IL-10 at the highest HCQ concentration (25 µM) and reduced levels of IL-1β at all concentrations (0.25 µM, 2.5 µM, 25 µM) without any adverse effects on cell viability (Fig. 5A: 6–8) Treatment of BV-2 microglia with HCQ (25 µM) increased microglial clearance of exogenous Aβ1-42 as shown by reduced levels of Aβ1-42 in the supernatant and a lowering of the Aβ1-42 supernatant:lysate ratio (i.e. phagocytized Aβ1-42) without adverse effects on cell viability (Fig. 5B: 1, 2). Treatment with HCQ (25 µM) significantly increased microglial uptake of pH-sensitive Protonex-labelled Aβ1-42 into acidic organelles compared to control cells treated with Aβ1-42 alone (Fig. 6).Fig. 6Hydroxychloroquine increases exogenous Aβ1-42 clearance through microglial uptake into acidic organelles. A HCQ (0.25, 2.5, and 25 µM) was applied to BV-2 microglial cells treated with Protonex Green 500-labelled Aβ1-42 (3 h). Labelled Aβ1-42 exhibits green fluorescence when internalized by acidic cell organelles (e.g., lysosomes) and is non-fluorescent at physiologic pH. BV-2 microglial cells treated with HCQ 25 µM showed significantly increased microglial uptake of Aβ1-42 into acidic cell organelles compared to control cells treated with Aβ1-42 alone. B Images of Protonex Green 500-labelled Aβ1-42 comparing the VC to HCQ 25 µM. Error bars in the bar graph indicate group mean + standard deviation (SD). Group differences comparing HCQ-treated cells to the VC were evaluated using the one-way ANOVA test. Asterisks indicate significant differences between groups: **$p \leq 0.01$ HCQ hydroxychloroquine, RFU relative fluorescence unit, VC vehicular control.
Treatment of iPSC derived adult human AD microglia with HCQ (0.25 µM, 2.5 µM) increased microglial clearance of exogenous Aβ1-42 as shown by reduced levels of Aβ1-42 in the supernatant (Fig. 5B: 3). Treatment with HCQ (0.25 µM) significantly increased microglia uptake of pHrodo red positive cells into acidic cell organelles compared to control cells treated with Aβ1-42 alone (Fig. 5B: 4) Treatment of MACS isolated adult 5xFAD mouse microglia with HCQ did not have an effect on microglial clearance of exogenous Aβ1-42 at any concentration.
Treatment with HCQ (25 µM) reduced levels of total tau and phosphorylated tau in SH-SY5Y cells overexpressing human mutant tau (pT231) (Fig. 5C: 1,2).
A summary of results across all AD-related phenotypic assays is included in Supplementary Table 5.
## HCQ inactivates STAT3 in microglia, astrocytes and neurons
We tested whether HCQ treatment (48 h; 50 µM) compared to the vehicular control (VC) alters levels of total STAT3, phosphorylated STAT3 (p-STAT3; Tyr705, the primary phosphorylated epitope) and the ratio of p-STAT3/ total STAT3 in microglia, astrocytes, neuroblasts and neurons. HCQ significantly reduced p-STAT3 levels in astrocytes, microglia, and mouse primary cortical neurons; HCQ did not have an impact on total STAT3 levels other than a significant increase in levels in microglia; HCQ significantly reduced the ratio of p-STAT3/total STAT3 in microglia and mouse primary cortical neurons. Results were robust to distributional assumptions. Representative western blot images and bar plots visualizing significant results are included in Fig. 7A, B. Full western blot results are included in Supplementary Fig. 4 and a summary of all statistical results are included in Supplementary Table 6.Fig. 7Hydroxychloroquine inactivates STAT3 in microglia, astrocytes and neurons. Human microglia cells, human astrocytoma cells, neuroblastoma cells, and mouse primary cortical neurons were either left untreated (VC) or treated with HCQ (50 µM, 48 h) or MTX (50 nM, 48 h), and the levels of p-STAT3 (Tyr705; primary epitope) and total STAT3 were assessed by western blot analysis, quantified by densitometry and normalized to levels of the loading control protein, ACTB. A Representative western blot images comparing treatment of HCQ or MTX (+) to VC (−). B After HCQ treatment, levels of p-STAT3 (Tyr705) were significantly reduced compared to the VC in astrocytes, microglia, and mouse primary cortical neurons; the ratio of p-STAT3/total STAT3 was significantly reduced in in microglia and mouse primary cortical neurons; HCQ did not have an impact on total STAT3 levels other than a significant increase in levels in microglia. C After MTX treatment, levels of p-STAT3 (Tyr705) and the ratio of p-STAT3/total STAT3 were significantly reduced compared to the VC. Error bars in all bar graphs indicate group mean + standard deviation (SD). Group differences comparing HCQ or MTX -treated cells to untreated control cells were evaluated using the one-way ANOVA test. Asterisks indicate significant differences between groups: *$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001.$ p-STAT3 phosphorylated STAT3, HCQ hydroxychloroquine, MTX methotrexate, Tyr705 tyrosine 705, ACTB β-Actin, VC vehicular control.
We additionally tested the effect of MTX treatment (48 h.; 50 nM). MTX significantly reduced p-STAT3 levels compared to the VC only in mouse primary cortical neurons; MTX did not have a significant effect on total STAT3 levels; MTX significantly reduced the ratio of p-STAT3/total STAT3 in mouse primary cortical neurons (Fig. 7C; Supplementary Fig. 4; Supplementary Table 6).
## HCQ-induced rescue of impaired hippocampal synaptic plasticity is associated with inactivation of STAT3
To test whether the HCQ-induced rescue of impaired hippocampal synaptic plasticity (described previously) may be associated with inactivation of STAT3, we tested levels of total STAT3 and phosphorylated STAT3 (p-STAT3; Tyr705 the primary phosphorylated STAT3 epitope) after treatment with HCQ (25 and 50 µM) in WT and APP/PS1 mouse hippocampal slices. Total STAT3 and p-STAT3 levels were significantly higher in untreated APP/PS1 hippocampi relative to WT; there was no change in the p-STAT3/total STAT3 ratio. After treatment of APP/PS1 hippocampi with 25 µM HCQ, we did not observe an inactivation of STAT3. Total STAT3 and p-STAT3 levels of treated and untreated APP/PS1 hippocampi were significantly higher compared to WT and there was no significant difference in total STAT3 and p-STAT3 levels comparing treated and untreated APP/PS1 hippocampi. There was also no change in p-STAT3/total STAT3 ratio after treatment of APP/PS1 hippocampi with 25 µM HCQ.
After treatment of APP/PS1 hippocampi with 50 µM HCQ, we observed inactivation of STAT3. While total STAT3 and p-STAT3 levels were still significantly higher compared to WT, p-STAT3 levels were significantly lower in treated APP/PS1 hippocampi compared to untreated while total STAT3 levels remained unchanged (Fig. 8). There was no change in the p-STAT3/total STAT3 ratio. Complete results of the western blot analyses including all pair-wise comparisons are included in Supplementary Table 7.Fig. 8Hydroxychloroquine rescue of impaired late long-term potentiation is associated with inactivation of STAT3.WT and APP/PS1 mouse hippocampal slices were either left untreated or treated with HCQ (50 µM, 48 h), and the levels of p-STAT3 (Tyr705; primary epitope) and total STAT3 were assessed by western blot analysis, quantified by densitometry and normalized to levels of the loading control protein, tubulin and reported in comparison with WT. A Representative western blot images comparing total STAT3 and p-STAT3 levels across WT, APP/PS1, WT + 50 µM HCQ and APP/PS1 + 50 µM HCQ ($$n = 3$$ mouse hippocampi/group). B Levels of both total STAT3 and p-STAT3 were significantly higher in untreated and treated (50 µM HCQ) APP/PS1 hippocampi compared to WT. Levels of p-STAT3 were significantly lower in APP/PS1 + 50 µM HCQ-treated hippocampi compared to untreated APP/PS1 mice, while no differences in total STAT3 levels were observed. Error bars in all bar graphs indicate group mean + standard deviation (SD). Group differences were evaluated using the one-way ANOVA test. Asterisks indicates significant differences between groups: *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ and ****$p \leq 0.0001$). p-STAT3 phosphorylated STAT3, HCQ hydroxychloroquine, Tyr705 tyrosine 705, APP/PS1 double transgenic mice expressing AD pathology (human amyloid precursor protein mutant human presenilin 1), WT wild type mice.
## Discussion
Extending our recent work examining candidate AD treatments targeting the JAK/STAT cytokine signaling pathway [3, 9], we now demonstrate that HCQ lowers the incidence of ADRD compared to MTX in older individuals, rescues impaired hippocampal synaptic plasticity in APP/PS1 mice and corrects multiple molecular abnormalities underlying AD. Together, these findings suggest that HCQ may be a promising disease-modifying AD treatment in at-risk individuals.
We first tested whether exposure to HCQ lowers ADRD risk in humans in a large real-world clinical dataset. We implemented a rigorous study design that addresses several common pitfalls in pharmacoepidemiologic studies of ADRD including the lack of an active comparator group for the same indication, misclassification of ADRD onset, as well as lack of outcome specificity [3]. Across all prespecified analyses, we found that exposure to HCQ in older individuals prior to ADRD diagnosis was associated with 8–$16\%$ lowering of incident ADRD relative to the active comparator, MTX. These results were consistent when restricting the outcome to only AD as well as when using an alternate active comparator drug (i.e., leflunomide).
We next proceeded to test the effects of HCQ across molecular abnormalities underlying AD including impaired hippocampal synaptic plasticity which is believed to mediate cognitive impairment in AD. We show that HCQ restores late LTP [40, 41] in the hippocampus of APP/PS1 mice prior to significant accumulation of amyloid plaques and neurodegeneration. This restoration is dose-dependent, including a partial rescue at 25 µM HCQ and a complete rescue of impaired hippocampal synaptic plasticity at 50 µM HCQ. The comparator drug, MTX, did not show a similar rescue of late LTP. These findings are the first to suggest that HCQ may ameliorate dysfunction in the neural basis of learning and memory processes [42, 43] that may underlie neurocognitive impairment in AD [44–46].
We conducted additional exploratory analyses to test the effects of HCQ in phenotypic assays reflecting molecular features of AD pathogenesis to further assess its potential as a candidate AD treatment. Our results suggest that HCQ impacts key cellular functions relevant to AD pathophysiology [47, 48]. These include countering neuroinflammation by lowering release of pro-inflammatory cytokines in both BV-2 and adult 5xFAD microglial cells (Fig. 5A), enhancing the microglial clearance of extracellular Aβ1-42 through phagocytosis into acidic cellular compartments in both BV-2 and iPSC derived adult human AD microglial cells (Figs. 5B, 6) and reducing tau phosphorylation in neuroblastoma cells overexpressing human mutant tau (Fig. 5C).
Our findings suggest that HCQ lowers risk of incident ADRD compared to MTX and rescues abnormalities associated with AD including impaired hippocampal synaptic plasticity as well as the three principal pathogenic mechanisms in AD: neuroinflammation, Aβ clearance and tau phosphorylation. One potential mechanism explaining these findings may be through the inactivation of the cytokine transducer protein, STAT3. Recent findings suggest that HCQ inactivates STAT3 [11] while prior work in transgenic AD mouse models has implicated enhanced STAT3 signaling in Aβ-induced neuronal death, reactive astrogliosis, impaired microglial clearance of Aβ as well as in cognitive impairment [49, 50], suggesting that inhibition of STAT3 signaling may target multiple molecular abnormalities and hence present a novel therapeutic approach in AD. To assess whether STAT3 inactivation may be associated with disease-modifying effects of HCQ, we first showed that HCQ inactivates STAT3 in astrocytes, microglia, and mouse primary cortical neurons. We additionally showed that treatment of APP/PS1 hippocampi with HCQ significantly reduces p-STAT3 levels suggesting that the rescue of impaired hippocampal synaptic plasticity by may be associated with the inactivation of STAT3. These mechanistic studies, while preliminary suggest that HCQ effects on ADRD may be mediated through STAT3 inactivation.
A previous small (HCQ group: $$n = 77$$, placebo group: $$n = 78$$) clinical trial of HCQ in patients with established AD (i.e., symptomatic individuals) [51] in 2001 did not show a significant effect in slowing cognitive decline. Several methodologic issues in this previous clinical trial merit consideration. First, this trial was likely under-powered to show differences on cognitive endpoints between drug and placebo. In comparison, recent phase $\frac{2}{3}$ clinical trials of amyloid-lowering drugs have recruited more than 1000 patients randomized to drug or placebo groups [52]. Additionally, prior bioavailability studies have shown that steady state levels of HCQ are only achieved after approximately six months of dosing [53, 54]. In a recent phase-2 clinical trial testing HCQ in patients with primary progressive multiple sclerosis, a run-in period of six months from the initiating treatment was incorporated and primary outcomes measured between 6 and 18 months [55]. The absence of a similar run-in period to achieve steady state HCQ levels in the prior 2001 clinical trial in AD may have further reduced the likelihood of detecting changes in clinical outcomes. Finally, the previous clinical trial of HCQ was performed well before the recognition of preclinical AD as a diagnostic entity and before the advent of biomarkers to accurately diagnose AD for patient recruitment into clinical trials. Interestingly, a recent case report showed that HCQ treatment in a patient diagnosed with sarcoidosis and mild cognitive impairment due to AD was associated with significant improvement in cognitive performance and accompanying correction of abnormal CSF Aβ1-42 levels [56].
Previous epidemiologic studies have examined the impact of HCQ on AD risk. Two such studies utilizing data from primary care patients in the UK and Taiwan showed no reduction in AD risk with HCQ treatment [57, 58]. Notably, our study differs substantially from these prior investigations as we explicitly compared the risk of ADRD in RA patients treated with HCQ or an equivalent alternative treatment (MTX) to minimize confounding by indication after accounting for a large number of potential confounding factors. Further, our study used a large cohort of HCQ treated patients and may therefore have had greater statistical power to detect smaller effect sizes.
Several features make HCQ an attractive candidate for repurposing in AD including its permeability across the blood brain barrier and effective partitioning into the brain. Doses of HCQ of 6.0–6.5 mg/kg/day typically used in RA patients, yield serum concentrations of 1.4 to 1.5 micromolar [59]. Brain concentrations are several fold higher than plasma and accumulation is likely even higher in acidic compartments including lysosomes [60, 61]. The doses tested in our in vitro experiments (i.e., 25 µM and 50 µM) may be consistent with brain concentrations achievable with conventional dosing of HCQ in RA patients. Furthermore, HCQ has a well-established safety profile with serious side effects being relatively rare [62], although additional screening for cardiac arrhythmias in some patients may be necessary [63, 64].
This study has limitations. First our phenotypic studies used a variety of cells lines to assess the effects of HCQ on several distinct AD-related phenotypes. These cell culture based phenotypic assays only reflect discrete aspects of AD pathogenesis are not capable of recapitulating complex gene-environment interactions that underlie the disease in older individuals. Second, we do not include cognitive data in our transgenic AD mouse models. While our pharmacoepidemiologic evidence suggests clinical benefits in humans, additional behavioral data from animal models testing the effects of HCQ would provide important experimental evidence for any potential benefit. This would be an important next step in follow-up studies. Third, our experiments to test whether STAT3 inactivation may be associated with disease-modifying effects HCQ are preliminary; the associations reported only suggest that HCQ may impact AD pathogenesis through this mechanism and merit confirmation in future studies.
In summary, we have established that the commonly used RA drug, HCQ lowers AD risk in older individuals, and targets multiple pathogenic mechanisms in AD including synaptic dysfunction, neuroinflammation, Aβ clearance, and tau phosphorylation. Our results provide compelling evidence that this safe and inexpensive drug may be a promising disease-modifying treatment for AD. Confirmation of our findings in adequately powered clinical trials in at-risk individuals during preclinical stages of disease progression should be initiated in a timely manner.
## Supplementary information
Supplementary Text Supplementary Figures and Tables The online version contains supplementary material available at 10.1038/s41380-022-01912-0.
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|
---
title: 'Predictors of longitudinal cognitive ageing from age 70 to 82 including APOE
e4 status, early-life and lifestyle factors: the Lothian Birth Cohort 1936'
authors:
- Janie Corley
- Federica Conte
- Sarah E. Harris
- Adele M. Taylor
- Paul Redmond
- Tom C. Russ
- Ian J. Deary
- Simon R. Cox
journal: Molecular Psychiatry
year: 2022
pmcid: PMC10005946
doi: 10.1038/s41380-022-01900-4
license: CC BY 4.0
---
# Predictors of longitudinal cognitive ageing from age 70 to 82 including APOE e4 status, early-life and lifestyle factors: the Lothian Birth Cohort 1936
## Abstract
Discovering why some people’s cognitive abilities decline more than others is a key challenge for cognitive ageing research. The most effective strategy may be to address multiple risk factors from across the life-course simultaneously in relation to robust longitudinal cognitive data. We conducted a 12-year follow-up of 1091 (at age 70) men and women from the longitudinal Lothian Birth Cohort 1936 study. Comprehensive repeated cognitive measures of visuospatial ability, processing speed, memory, verbal ability, and a general cognitive factor were collected over five assessments (age 70, 73, 76, 79, and 82 years) and analysed using multivariate latent growth curve modelling. Fifteen life-course variables were used to predict variation in cognitive ability levels at age 70 and cognitive slopes from age 70 to 82. Only APOE e4 carrier status was found to be reliably informative of general- and domain-specific cognitive decline, despite there being many life-course correlates of cognitive level at age 70. APOE e4 carriers had significantly steeper slopes across all three fluid cognitive domains compared with non-carriers, especially for memory (β = −0.234, $p \leq 0.001$) and general cognitive function (β = −0.246, $p \leq 0.001$), denoting a widening gap in cognitive functioning with increasing age. Our findings suggest that when many other candidate predictors of cognitive ageing slope are entered en masse, their unique contributions account for relatively small proportions of variance, beyond variation in APOE e4 status. We conclude that APOE e4 status is important for identifying those at greater risk for accelerated cognitive ageing, even among ostensibly healthy individuals.
## Introduction
With advancing age, a pattern of decline is observed across a multitude of cognitive domains, though the magnitude differs across domains, and there are marked individual differences in rates of cognitive change in the population [1, 2]. Some cognitive abilities, such as vocabulary, remain relatively intact into later life. Other, complex cognitive processes such as processing speed, reasoning, and memory—which require the manipulating of mental data—begin to decline from early adulthood [3–5], and some of these changes are underpinned by a general factor of cognitive ageing [6–8]. Deterioration in cognitive abilities is linked to impairments in older adults’ everyday functions [9], quality of life [10], and health [11]. Better understanding of long-term cognitive trajectories and their determinants could inform public policy regarding targeted interventions for those adults at greatest risk of rapid decline, and of progression to Alzheimer’s Disease (AD) and other dementias [12], as well as protective factors for staying sharp in later life.
The determinants of individual differences in age-related cognitive decline are likely to include genetic and early-life factors, adult socio-economic status (SES), and health [13–15], though estimates differ with respect to their individual contributions. Risk of accelerated cognitive decline increases with age, cerebrovascular disease, cardiovascular risk factors (e.g. diabetes, obesity) and heart disease [16], but these factors only partially account for cognitive decline risk among the general population [14]. The APOE (apolipoprotein) e4 allele is a well-established genetic risk factor for AD [17, 18], however, the reported effects of APOE e4 across the full spectrum of cognitive functioning are highly inconsistent and there is disagreement about whether or not APOE e4 influences the rate of cognitive decline in healthy adults [19–25]. Despite a broad corpus of research literature on the role of behavioural risk factors in mitigating age-related cognitive decline, such as smoking, physical activity, alcohol, and diet [3, 26, 27], the evidence is patchy and often classed as low to moderate quality [10]. Importantly, many of the effect sizes are small, and findings are often partly, or wholly, attributed to reverse causation, where prior cognitive ability causes variation in the supposed cause of cognitive ability in later adult life [13].
Cognitive decline trajectories are likely to be the result of an accumulation of small effects from numerous individual genetic and environmental risk factors across the life-course [28]. Even smoking, for which there is consistent and demonstrable evidence of an adverse effect on cognitive and brain ageing [29–31], generally accounts for around only $1\%$ of the variance in cognitive decline, similar in magnitude to the estimated effect size of APOE e4 on cognitive change from childhood to adulthood [32]. Given that many risk factors for cognitive decline are correlated [33], modelling these potential predictors together, i.e. simultaneously, may be a more valuable approach than focussing on single-candidate determinants (such as one individual lifestyle or health factor). Unlike univariate accounts of cognitive ageing, multivariate modelling acknowledges the multicollinearity among risk factors and provides more insight into their relative contributions to cognitive change. The very few studies to have tested multiple risk factor models of longitudinal (multi-domain) cognitive decline report few consistent correlates of cognitive change across abilities [34, 35]. In the same sample as in the current study—the Lothian Birth Cohort 1936—an earlier multivariate analysis by Ritchie et al. showed that faster rates of decline from age 70 to 76 years were observed in APOE e4 carriers, men, and those with poorer physical fitness for some, but not all, cognitive domains [36].
A further challenge in understanding the predictors of cognitive ageing trajectories is the difficulty in disentangling actual cognitive change from lifelong levels of performance (which are conflated in cross-sectional data) and partitioning the variance appropriately [8]. Longitudinal studies with repeated cognitive measures across an extended period in later life, paired with appropriate methodologies for modelling change, are crucial for characterising the progression of cognitive change and robustly identifying its correlates [15]. Ideally, studies should establish the extent to which potential determinants of differences in cognitive ageing are independent of prior cognitive ability differences.
In the current study, we address these issues using data from the Lothian Birth Cohort 1936, an extensively-phenotyped, community-dwelling sample of older adults in Scotland, for whom there are comprehensive cognitive data collected at five time-points across later life (age 70–82), cognitive ability scores from early-life, and data on a wide range of potential covariates (see Box 1 for a summary of study characteristics). Trajectories of cognitive function were evaluated using latent growth curve (LGC) modelling for four major domains of cognitive ability—visuospatial ability, processing speed, and memory (characterising fluid intelligence), and verbal ability (characterising crystallised intelligence). A main aim was to examine which putative cognitive ageing predictors from across the life-course survive simultaneous entry in multivariate cognitive models, using fifteen of the most commonly-used candidate risk factors in the field of cognitive ageing, covering: early-life (education, childhood IQ); demographic (age, sex, living alone, SES); lifestyle (smoking, physical activity, body mass index, alcohol), health (cardiovascular disease (CVD), diabetes, stroke); depressive symptoms; and APOE e4 carrier status. The present study doubles the time frame of the above-mentioned LBC1936 paper by Ritchie et al. [ 36] from 6 to 12 years of follow up, covering a more critical period for accelerated cognitive decline and dementia [37, 38], and includes several additional potential predictors (depression, living alone, physical activity, stroke). Having previously identified APOE e4 status as an independent predictor of cognitive change in this cohort, we perform separate trajectory analyses by APOE e4 carrier status. We also examine associations between predictors and a general factor of cognitive function which accounts for the shared variance across the cognitive domains.
## Participants
Participants were from the Lothian Birth Cohort 1936 (LBC1936) [39–41], a community-dwelling sample of 1091 men and women in Scotland, being studied in later life for the purposes of assessing the nature and determinants of cognitive and brain ageing. Most LBC1936 participants had taken part in a Scottish national intelligence test at age 11 years. The Scottish Mental Survey 1947 tested the cognitive ability of almost all Scottish children born in 1936, and attending school on 4 June 1947 ($$n = 70$$,805), using a validated test of general mental ability (The Moray House Test (MHT)) [42]. The first wave of the LBC1936 study was conducted between 2004 and 2007 at the age of ~70 years, and participants have been followed-up every 3 years at ages 73 ($$n = 866$$), 76 ($$n = 697$$), 79 ($$n = 550$$), and 82 ($$n = 431$$). Socio-demographic, medical history, physical function, blood-derived biomarkers, cognitive function, and lifestyle data were collected at all five waves of in-person testing. For the purposes of the current study, “completers” ($$n = 431$$) refer to participants who attended all five assessments at ages 70, 73, 76, 79, 82, and “non-completers” ($$n = 660$$) refer to the remaining participants those who took part in ≤4 assessments, and either withdrew or died before age 82 follow-up. All participants who completed at least the first wave of testing at age 70 were included in the main analyses (see Fig. S1 flowchart showing waves of testing, attrition and deaths).
## Cognitive measures
Cognitive function was measured using a detailed battery of well-validated cognitive tests administered by trained psychologists at age 70 (baseline) and the same tests were repeated at ages 73, 76, 79, and 82 years [39]. Most of the cognitive tests derive from the Wechsler Adult Intelligence Scale III-UK edition [43] and the Wechsler Memory Scale III-UK edition (WMS-IIIUK) [44]. According to previous work examining their correlational structure [7], the cognitive tests were categorised into four domains of cognitive functioning. Visuospatial ability was measured using Block Design and Matrix Reasoning (WAIS-IIIUK) and Spatial Span (Forwards and Backward) (WMS-IIIUK). Processing Speed was measured using Digit-symbol Coding and Symbol Search (WAISIII-UK) and two experimental tasks: Choice Reaction Time [45]; and Inspection Time [46]. Memory was measured using Verbal Paired Associates and Logical Memory (WMSIII-UK) and Digit-span Backwards (WAIS-IIIUK). Verbal ability was measured using the National Adult Reading Test [47], the Wechsler Test of Adult Reading [48], and Verbal Fluency [49]. A general cognitive factor was constructed based on the shared variance between the four cognitive domains (see “Statistical analysis”). The Mini-Mental State Examination (MMSE) [50], widely used as a screening test for possible dementia, was administered at each wave of testing.
## Predictor measures
Potential risk or protective factors for cognitive decline in later life were identified following a review of previous analyses of the cohort and other population studies; values were obtained from participants’ baseline assessment at age 70.
## Demographics and early-life
These predictors included age (in days), sex, age 11 IQ score, education (years of formal full-time schooling), living alone (yes/no), and SES. MHT scores from age 11 (SMS1947) were recorded and archived by the Scottish Council for Research in Education and were made available to the LBC1936 study. For the current study, MHT scores from age 11 were age corrected and converted into a standard IQ-type score for the sample (mean = 100, SD = 15)—henceforth referred to as age 11 IQ—and used a measure of childhood cognitive ability. SES was coded into six categories based on participants’ highest achieved occupation: 1 (highest professional occupations) to 5 (unskilled occupations), with 3 (skilled occupations) divided into 3N (non-manual) and 3M (manual), using the Classification of Occupations, 1980 [51].
## Lifestyle
Smoking was coded as current, former or never smoker. Physical activity was coded according to six categories: 1 (“moving only in accordance with household chores”; lowest level of activity) to 6 (“keep fit or aerobic exercise several times a week”; highest level of activity). Alcohol units per week were calculated using data collected at interview. Body mass index was calculated using height and weight measurements taken by trained nurses at the time of assessment.
## APOE e4 and health indicators
APOE e4 carrier status (yes/no) was determined by genotyping at two polymorphic sites (rs7412 and rs429358) using TaqMan technology. Depressive symptoms were measured using the Depression Subscale of the Hospital Anxiety and Depression Scale [52] with a score range of 0–21. Health indicators included self-reported history (yes/no) of CVD, diabetes, and stroke.
## Descriptive statistics
Descriptive statistics are presented for the full sample, and ANOVA and Chi-square tests were used to identify differences in baseline characteristics between study completers vs. non-completers, and between deaths to follow-up vs. survivors.
## Measurement models
We applied LGC modelling to the data to investigate level (i.e. intercept, age 70) and trajectories of change (i.e. slope, age 70–82) in cognitive functioning across all five waves of testing. Participants were included in the analytic sample even if they attended baseline-only, as the estimates for intercept (i.e. cross-sectional) and slope (i.e. longitudinal) associations are derived simultaneously from the same LGC model using all available data. A SEM-based “factor-of-curves” [53] approach was used, as has been done previously in this cohort [36, 54] which postulates the existence of common latent variables of cognitive change that underlie the distribution of explicit or observable variables (individual cognitive tests). In our models, we used the average time lag (in years) between the waves: (0, 2.98, 6.75, 9.81, 12.53) as the path weights for calculation of the slope factor. The path from the slope factor to baseline test score was set to zero. LGC analyses were conducted using the latent variable analysis package “lavaan” [55] in R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria) and the code is available online (https://www.ed.ac.uk/lothian-birth-cohorts/summary-data-resources). First, we fitted a single parallel process growth curve model at the level of the 13 individual cognitive tests; intercepts and slopes were correlated, but no hierarchical factor structure was imposed. Second, we fit separate growth curve models for each cognitive domain: visuospatial ability; processing speed; memory (Visual inspection of the fitted regression lines through the individual cognitive test scores at each wave indicated that memory might best be modelled using a non-linear factor of change (to account for the rise in mean test scores in the initial waves of testing, followed by a fall toward the end of the follow-up). To test for potential curvilinear trajectories for memory, we included a quadratic term in separate measurement models for the latent memory domain. However, these models did not converge successfully and are not discussed further.); and verbal ability. Here, the latent intercepts and slopes of each cognitive test load onto superordinate latent intercepts and latent slopes of their respective cognitive domains. The cognitive domain models were run for the full sample and also by APOE e4 carrier status (yes/no). Unstandardised (beta) estimates, standard errors, p values, and standard deviation (SD) change per year, are reported.
## Predictors of cognitive level and slope
Next, we fit both univariate and multivariate risk factor models to the cognitive data to address which factors might contribute to individual differences in cognitive level (age 70) and slope (age 70–82). First, univariate LGC models were fit to test the associations of each life-course predictor (alongside age and sex) with each cognitive domain, i.e. without the other variables present in the model. For our main analyses, we fit multivariable LGC models which included all 15 predictor variables for each cognitive domain. By including all of the predictors simultaneously, we were able to compare the degree of variance in cognitive level and change accounted for by each risk factor, whilst controlling for the effects of all the other predictors in the same model. Our analysing the paper as we have done is in response to many papers in our field that tend to focus on a single predictor with a few basic covariates (age, sex, medical conditions, etc.) isolated from other predictors. Here, a main aim was to find out how many of the commonly-used cognitive ageing predictors survives simultaneous entry. We ran an additional model representing a general cognitive factor; this hierarchical model was fitted using the latent intercepts and slopes of each of the four cognitive sub-domains, and represents the shared (common) variance between them (Fig. 1 illustrates the hierarchical model framework for general cognitive function). Fully standardised estimates, obtained using the “standardizedSolution” function in lavaan, are presented. Fig. 1Schematic latent growth curve model of general cognitive ability. A latent growth curve model in which predictors are associated with the intercept and slope of a latent factor of general cognitive function. A latent growth curve was estimated across five waves of data in a hierarchical model based on the intercepts and slopes of four cognitive domains. For illustrative purposes, not all tests are shown. The full model included at least three tests per domain. The regressions of predictors (represented by the dotted lines) on general cognitive function intercept (i) and slope (s) were the associations of interest.
## Gaussian confounds analysis
With a large set of predictors, as in the current study, we increase the proportion of variance that can be explained in our cognitive outcomes by chance. In order to test whether or not the variance accounted for by the real predictors was comparable to a set of random predictors, we generated a set of Gaussian noise (and random binary) variables and entered them into the LGC models in place of the real predictors, and compared the model R2 for each domain. To optimise comparability, we ensured that the same number of continuous vs. binary variables were used, and that the patterns of missingness were matched with the real-world predictors.
## Sensitivity analyses
We repeated the same baseline prediction models in three sensitivity analyses excluding: [1] individuals who reported a subsequent-to-baseline diagnosis of dementia (all participants were dementia-free at baseline); [2] individuals with an MMSE score <24 at any wave, as an indicator of possible pathological ageing; [3] deaths to follow-up (using linkage data obtained via National Health Service Central Register up to April 2021, provided by the National Records of Scotland).
We performed three sensitivity analyses to determine whether our results were driven by: participants who developed dementia by the age 82 assessment ($$n = 24$$); low MMSE scorers at one or more testing waves ($$n = 46$$); or deaths ($$n = 403$$). We found no substantive differences between the results of the sensitivity analyses (reported in Tables S6–8) and those reported above. The only notable result of these exclusions was an attenuation in effect sizes for the APOE e4 associations with visuospatial ability slope, of $46\%$, $22\%$, and $34\%$, respectively, across the three analyses, which were no longer significant at $p \leq 0.05.$
## Path model
In order to further examine the multivariate associations, a SEM-based path model was constructed with the latent variable of general cognitive function (g) intercept and slope as the dependent variables. The path diagram (Fig. S2) represents a life-course model with predictors from childhood to older age included. Specific assumptions regarding the direction of causal relationships were built into the model. We assumed chronological paths from childhood IQ → education → adult SES. Based on previous literature, we also assumed that childhood IQ, education, and adult SES might influence the lifestyle and health predictors, and that APOE e4 might influence CVD. All the predictors in the model have direct paths to g intercept and g slope. Direct pathways represent the unique contribution of each predictor to the outcome variable, which is not accounted for by other mediating pathways.
The life-course path model, showing significant associations (standardised beta regression weights) among the variables, is illustrated in Fig. 5 and full results can be found in Supplementary (Table S9). “ Living alone” was not included as it was not associated with any of the cognitive domains in the LGMs. Direct paths to g intercept from the earlier-life factors were significant: childhood IQ (0.666, $p \leq 0.001$); education (0.199, $p \leq 0.001$); mid-life SES (−0.117, $p \leq 0.001$), as were the direct paths from depressive symptoms (−0.066, $p \leq 0.001$) and diabetes (−0.060, $p \leq 0.001$) to g intercept. The path model did indicate mediation paths from age 11 IQ → depressive symptoms and diabetes → g intercept. The % of the direct effect from age 11 IQ → g intercept mediated by these two health factors was minimal, at $1.0\%$ and $0.8\%$, respectively. As in the multivariate LGM, the sole predictor of cognitive slope was APOE e4 carrier status (−0.240, $p \leq 0.001$). None of the lifestyle or health predictors had significant paths to cognitive slope. The association of APOE e4 with general cognitive function decline was not mediated by an increased risk of CVD as hypothesised, neither was there any indication in the model of any other mediator effects by the other life-course variables, on cognitive change. In response to a reviewer, we also tested for an interaction effect of APOE e4 × CVD on g intercept and slope in a separate model, given the role of APOE in CVD prevalence, and neither path was significant (0.012, $$p \leq 0.674$$; 0.005, $$p \leq 0.911$$, respectively). The path model demonstrates that APOE e4 status uniquely, among this set of predictors, influences cognitive change from age 70 to 82 years in the LBC1936, even when the variance from the other predictor variables is accounted for. Fig. 5Life-course path model. Path (SEM) model showing significant (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$) associations between early-, mid-, and later-life factors. All model predictors were regressed on a latent variable of general cognitive function (g) intercept and slope, estimated within the model. The numbers accompanying the paths are the beta values (standardised regression weights). Age (in days) and sex were included in the path model but not shown to reduce visual clutter. The covariances between lifestyle and health factors are not shown here. Full results are presented in Supplementary Table S9. SES is coded negatively (lower values=higher (more professional) social class).
## Model fit and significance statistics
Models were run using full information maximum likelihood (FIML) estimation to ensure models used all available data to partially mitigate the bias of estimated trajectories and associations by participation bias. Instances of non-significant negative residual variance were set to 0 to allow models to converge upon within-bounds estimates. Model fit was tested using three indices of absolute fit: comparative fit index and Tucker-Lewis Index (values > 0.95 considered acceptable); and root mean square error of approximation (values < 0.06 considered acceptable). Correction for multiple testing was applied across LGC prediction models using the false discovery rate (FDR) [56] adjustment, and results marked in boldtype are FDR-significant.
## Descriptive
Baseline characteristics and cognitive test scores for the LBC1936 sample ($$n = 1091$$) are shown in Table 1. Baseline age was 70 years (mean = 69.5, SD = 0.8), $49.8\%$ of the sample were women, and mean number of years of education was 10.7 (SD = 1.1). APOE e4 allele carriers ($$n = 306$$) made up $28.0\%$ of the overall sample. APOE e4 data were missing for 63 participants ($5.8\%$ of the sample). See Table S1 for missing covariate data. Characteristics are also presented according to completer status (completers vs. non-completers), and mortality status (deaths vs. non-deaths) by the end of the follow-up period. Compared with individuals who attended all five waves, non-completers had less education, lower childhood IQ, lower SES, lower physical activity, higher BMI, more depressive symptoms, and were more likely to be a smoker, have a history of CVD, diabetes, and stroke. Non-completers had significantly lower cognitive test scores at baseline than completers. Participants lost to follow-up as a result of death ($$n = 403$$) had a lower age 11 IQ, lower SES, lower physical activity, higher BMI, higher alcohol intake, more depressive symptoms, and were more likely to be male, a smoker and to have a medical history of CVD, diabetes and stroke, than those who survived to follow-up. Mean cognitive test scores at baseline were significantly lower in those who had died, compared with the survivors, except for Verbal Pairs (a memory test) and Verbal Fluency (a verbal ability test), for which the group differences were not significant. As noted above, we used FIML estimation in our LGC analyses to reduce any bias due to missingness. Table 1Baseline characteristics of participants overall, and according to completer status and mortality status at the end of follow-up: the Lothian Birth Cohort 1936.Overall ($$n = 1091$$)Completers ($$n = 431$$)Non-completers ($$n = 660$$)Deaths ($$n = 403$$)Non-deaths ($$n = 688$$)CharacteristicM (SD)M (SD)M (SD)p valueM (SD)M (SD)p valueAge, years69.5 (0.8)69.5 (0.8)69.6 (0.8)0.0469.5 (0.8)69.5 (0.9)0.97Education, years10.7 (1.1)10.9 (1.2)10.6 (1.1)<0.00110.7 (1.1)10.8 (1.2)0.09Age 11 IQ100.0 (15.0)102.4 (15.0)98.5 (14.8)<0.00198.4 (15.0)100.9 (14.9)0.008Adult SES2.4 (0.9)2.3 (0.9)2.5 (0.9)<0.0012.6 (0.9)2.3 (0.9)<0.001Physical activity3.0 (1.1)3.2 (1.1)2.9 (1.1)<0.0012.8 (1.2)3.0 (1.0)0.007Body mass index27.8 (4.4)27.4 (4.6)28.0 (4.0)0.0128.3 (4.9)27.5 (4.0)0.005Alcohol intake, units10.5 (14.2)9.8 (11.4)11.0 (15.7)0.1612.0 (18.0)9.7 (11.3)0.01Depressive symptoms2.8 (2.2)2.5 (2.3)3.0 (2.1)0.0013.1 (2.5)2.6 (2.1)<0.001N (%)N (%)N (%)N (%)N (%)Female543 ($49.8\%$)222 ($51.5\%$)321 ($48.6\%$)0.35170 ($42.2\%$)373 ($54.2\%$)<0.001Lives alone266 ($24.4\%$)108 ($25.0\%$)158 ($23.9\%$)0.96113 ($28.0\%$)182 ($26.5\%$)0.55Current smoker125 ($11.5\%$)16 ($3.7\%$)109 ($16.5\%$)<0.00186 ($21.3\%$)38 ($5.5\%$)<0.001APOE e4 carrier306 ($28.0\%$)113 ($26.2\%$)193 ($29.2\%$)0.24122 ($30.3\%$)184 ($26.7\%$)0.17CVD268 ($24.6\%$)90 ($20.9\%$)178 ($27.0\%$)0.02118 ($29.3\%$)150 ($21.8\%$)0.006Diabetes91 ($8.3\%$)20 ($4.6\%$)71 ($10.8\%$)<0.00157 ($14.1\%$)34 ($4.9\%$)<0.001Stroke54 ($4.9\%$)12 ($2.8\%$)42 ($6.4\%$)0.00833 ($8.2\%$)21 ($3.1\%$)<0.001Cognitive testsM (SD)M (SD)M (SD)M (SD)M (SD)Block design33.8 (10.3)35.9 (10.0)32.4 (10.3)<0.00132.1 (10.1)34.8 (10.3)<0.001Matrix reasoning13.5 (5.1)14.7 (5.0)12.7 (5.1)<0.00112.6 (5.0)14.0 (5.1)<0.001Spatial span7.4 (1.4)7.6 (1.4)7.2 (1.4)<0.0017.1 (1.4)7.5 (1.4)<0.001Digit-symbol coding56.6 (12.9)60.0 (12.0)54.4 (13.0)<0.00152.9 (13.0)58.8 (12.4)<0.001Symbol search24.7 (6.4)25.9 (6.6)23.9 (6.2)<0.00123.5 (6.5)25.4 (6.2)<0.001Choice reaction time0.642 (0.086)0.623 (0.076)0.655 (0.089)<0.0010.659 (0.093)0.632 (0.080)<0.001Inspection time112.1 (11.0)114.1 (10.0)110.8 (11.5)<0.001110.8 (11.9)112.9 (10.4)0.003Logical memory71.4 (17.9)74.6 (17.2)69.4 (18.2)<0.00169.7 (19.4)72.5 (17.0)0.013Verbal pairs26.4 (9.1)28.2 (8.3)25.2 (9.5)<0.00125.9 (9.4)26.8 (9.0)0.120Digits backwards7.7 (2.3)8.1 (2.4)7.5 (2.2)<0.0017.5 (2.1)7.9 (2.3)0.005NART34.5 (8.2)35.7 (7.8)33.7 (8.3)<0.00133.7 (8.3)35.0 (8.1)0.013WTAR41.0 (7.2)42.2 (6.7)40.3 (7.4)<0.00140.1 (7.3)41.6 (7.0)0.001Verbal fluency42.4 (12.5)43.6 (12.5)41.7 (12.5)0.0141.5 (13.0)43.0 (12.2)0.07Adult SES (classes 1–5) is scored negatively where class 1 = most professional and class 5 = manual. Completers were those participants who remained in the study through waves 1 (age 70 years) to wave 5 (age 82 years). Non-completers include participants who died or withdrew from the study at any point across waves 1 to 5. Mortality data are correct as of April 2021. p values derived from one-way ANOVA or Chi-square tests as appropriate. SES socio-economic status, CVD cardiovascular disease.
A summary of the longitudinal cognitive test scores for the whole sample is presented in Table 2. Mean cognitive test scores declined between age 70-baseline and age 82 follow-up, except for two memory tests (Logical Memory and Verbal Pairs) and the verbal ability tests (NART, WTAR, and Verbal Fluency), which were marginally higher at age 82. Logical Memory and Verbal Pairs contain memorable material, which may have resulted in a rise in score in at least the second occasion of testing as a result of practice effects. All three verbal ability tests showed little change over time, and small increases in mean scores at age 82 compared with baseline. Further descriptive information about the cognitive tests scores for completers only, and by APOE e4 carrier status, is provided in the Supplementary Materials. In the subset of completers only (Table S2); this has the advantage that the same individuals appear at all waves, all of the mean cognitive test scores were lower at age 82 follow-up compared with baseline with the exception of WTAR (where the mean score was the same), and NART and Verbal Fluency which were slightly higher at follow-up. Note that Choice Reaction *Time is* scored negatively, such that a higher score indicates a slower reaction time. Mean cognitive test scores at age 70 and age 82 differed according to APOE e4 carrier status (Table S3). At age 70, APOE e4 carriers had significantly lower scores on Matrix Reasoning, Spatial Span and Inspection Time than non-carriers. By age 82, APOE e4 carriers had significantly lower scores on Block Design, Matrix Reasoning, Spatial Span, Digit-symbol Coding, Symbol Search, Choice Reaction Time, Logical Memory, Verbal Pairs, and Digits Backwards, and the differences were larger in magnitude than at age 70. Figure 2 plots the linear fitted regression lines through the raw test data for each of the cognitive tests by APOE e4 carrier status (non-linear fitted lines through the same data can be found in Fig. S3).Table 2Longitudinal cognitive test scores for all participants. Cognitive test70 years73 years Attrition $20.6\%$76 years Attrition $19.5\%$79 years Attrition $21.1\%$82 years Attrition $21.6\%$NM (SD)NM (SD)NM (SD)NM (SD)NM (SD)Block design108533.8 (10.3)86433.6 (10.1)69132.2 (9.9)53531.2 (9.6)42029.9 (9.6)Matrix reasoning108613.5 (5.1)86313.2 (5.0)68913.0 (4.9)53512.9 (5.0)41812.9 (5.2)Spatial span10847.4 (1.4)8617.3 (1.4)6907.3 (1.4)5367.1 (1.4)4216.9 (1.4)Digit-symbol coding108656.6 (12.9)86256.4 (12.3)68553.8 (12.9)53551.2 (13.0)41851.0 (12.8)Symbol search108624.7 (6.4)86224.6 (6.2)68724.6 (6.5)53122.7 (6.7)41522.2 (6.9)Choice reaction time (s)10840.642 (0.086)8650.649 (0.090)6850.679 (0.102)5430.706 (0.114)4230.722 (0.120)Inspection time1041112.1 (11.0)838111.2 (11.8)654110.1 (12.5)465106.7 (13.6)382106.0 (12.7)Logical memory108771.4 (17.9)86474.3 (17.9)68874.6 (19.2)54272.7 (20.4)42372.1 (21.5)Verbal pairs105026.4 (9.1)84327.2 (9.5)66326.4 (9.6)49727.1 (9.6)38027.4 (9.5)Digits backwards10907.7 (2.3)8667.8 (2.3)6957.8 (2.4)5487.6 (2.2)4267.2 (2.3)NART108934.5 (8.2)86434.4 (8.2)69535.0 (8.0)54635.6 (8.2)42636.1 (7.8)WTAR108941.0 (7.2)86441.0 (7.0)69441.1 (7.0)54641.6 (7.0)42642.2 (6.6)Verbal fluency108742.4 (12.5)86543.2 (12.9)69642.9 (12.8)54743.6 (13.3)42643.6 (12.7)Ns at each wave were 1091 (70 years), 866 (73 years), 697 (76 years), 550 (79 years), and 431 (82 years). All tests are positively scored (i.e. higher scores = better performance) with the exception of Choice Reaction Time (in seconds) which is negatively scored (i.e. higher scores = slower performance).NART National Adult Reading Test, WTAR Wechsler Test of Adult Reading. Fig. 2Individual trajectory plots of raw test scores (fitted regression lines) for each cognitive test by APOE e4 status. Plots of the regression lines fitted through the raw data, normalised for baseline score, to illustrate the differences in trajectories of cognitive change with age by APOE e4 carrier status (with shaded $95\%$ confidence intervals). Red = non-carrier, blue = carrier.
## Individual cognitive tests
First, we tested whether there was significant ageing-related mean change in each of the 13 individual cognitive tests in a single parallel process LGC model (Table S4). There was a significant, negative mean slope for all tests ($p \leq 0.001$ except WTAR ($p \leq 0.05$)), with the exception of NART where the slope was non-significant. SD change per year was calculated for each cognitive test score and ranked in order of most change [1] to least change [13]. The four individual processing speed tests showed the largest SD declines over time (range, −0.120 to −0.072), followed by the three visuospatial tests (range, −0.055 to −0.038), the three memory tests (range −0.038 to −0.027), and the three verbal ability tests (range, −0.010 to 0.0001) which showed the least decline. SD change in NART scores was marginally positive but not significantly different from zero (SD change/year = 0.0001).
## Latent cognitive domains
Second, we tested whether there was significant ageing-related mean change in each of the four latent cognitive domains for all participants, and then separately by APOE e4 carrier status in LGC models (Table 3). In the full sample, there was a significant, negative mean slope of ageing-related change across all four cognitive domains. The latent variable of processing speed showed the greatest SD decline per year between age 70 and 82 (SD change/year = −0.088), followed by visuospatial ability (SD change/year = −0.054), memory (SD change/year = −0.028), and verbal ability (SD change/year = −0.003). Model fit indices for Table 3 are shown in Table S5, alongside those for Tables 4 and 5.Table 3Latent growth curve models: unstandardised means and variances for the intercept and slope of each cognitive domain, and by APOE e4 carrier status (slopes refer to change from age 70 to age 82).Cognitive domainInterceptsSlopesSD change in each domainM (SE)Variance (SE)M (SE)Variance (SE)SD change/yearRank order of SD changeAll participants Visuospatial15.888 (0.759)***13.711 (1.021)−0.201 (0.059)**0.015 (0.006)−0.0542 Processing speed97.982 (6.013)***21.971 (1.479)−0.413 (0.046)***0.084 (0.012)−0.0881 Memory72.889 (0.534)***171.046 (17.209)−0.361 (0.066)***1.722 (0.167)−0.0283 Verbal ability46.757 (1.090)***59.634 (3.009)−0.022 (0.010)*0.016 (0.004)−0.0034APOE e4 non-carriers Visuospatial16.265 (1.053)***13.879 (1.308)−0.125 (0.048)**0.008 (0.005)−0.0332 Processing speed102.548 (7.618)***21.758 (1.760)−0.318 (0.050)***0.046 (0.010)−0.0681 Memory73.074 (0.653)***182.660 (20.679)−0.135 (0.072)NS1.235 (0.155)−0.0103 Verbal ability46.607 (1.373)*58.104 (3.691)−0.026 (0.017)NS0.019 (0.005)−0.0044APOE e4 carriers Visuospatial14.657 (1.216)***12.850 (1.739)−0.232 (0.081)**0.022 (0.011)−0.0653 Processing speed93.361 (12.045)***22.652 (2.981)−0.504 (0.084)***0.167 (0.036)−0.1061 Memory71.918 (1.033)***160.519 (35.897)−0.918 (0.148)***2.714 (0.463)−0.0722 Verbal ability46.416 (2.036)***63.869 (6.017)−0.021 (0.015)NS0.015 (0.007)−0.0034Models were run separately for each domain. Path weights for calculation of the slope factor: Baseline = 0; to w2 = 2.98; to w3 = 6.75; to w4 = 9.81; to w5 = 12.53. SD change/year is the slope mean divided by the intercept standard deviation; rank order of SD change is from highest (1 = most change) to lowest (13 = least change). Model fit statistics are given in Supplementary Table S5.SE standard error.*$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001.$Table 4Univariate latent growth curve models: predictors of intercepts (age 70) and slopes of change (age 70–82) where predictors are entered separately with age and sex. PredictorsVisuospatial abilityProcessing speedMemoryVerbal abilityGeneral cognitive functionEstimate (SE)p valueEstimate (SE)p valueEstimate (SE)p valueEstimate (SE)p valueEstimate (SE)p valueIntercept on Age 11 IQa0.598 (0.022)<0.0010.533 (0.024)<0.0010.623 (0.027)<0.0010.720 (0.014)<0.0010.808 (0.014)<0.001 Educationa0.382 (0.028)<0.0010.305 (0.029)<0.0010.384 (0.031)<0.0010.536 (0.020)<0.0010.525 (0.024)<0.001 Adult SESb−0.364 (0.029)<0.001−0.335 (0.030)<0.001−0.269 (0.034)<0.001−0.446 (0.024)<0.001−0.459 (0.027)<0.001 Lives aloneb0.050 (0.034)0.14−0.025 (0.033)0.460.027 (0.036)0.46−0.008 (0.032)0.800.027 (0.034)0.42 Smoking categoryb−0.156 (0.033)<0.001−0.177 (0.032)<0.001−0.061 (0.035)0.08−0.060 (0.031)0.05−0.146 (0.032)<0.001 Physical activitya0.110 (0.035)0.0020.167 (0.035)<0.0010.091 (0.038)0.020.091 (0.033)0.0060.149 (0.037)<0.001 Body mass indexb−0.037 (0.034)0.27−0.075 (0.033)0.02−0.037 (0.035)0.30−0.187 (0.029)<0.001−0.116 (0.033)<0.001 Alcohol units, weeka0.064 (0.035)0.060.044 (0.034)0.200.101 (0.037)0.0060.069 (0.032)0.030.077 (0.034)0.02 APOE e4b−0.074 (0.034)0.03−0.072 (0.033)0.03−0.023 (0.036)0.520.010 (0.031)0.75−0.056 (0.034)0.10 Depressive symptomsb−0.146 (0.033)<0.001−0.196 (0.032)<0.001−0.127 (0.035)<0.001−0.106 (0.030)<0.001−0.185 (0.032)<0.001 CVDb−0.083 (0.033)0.013−0.127 (0.032)<0.0010.005 (0.036)0.89−0.037 (0.031)0.22−0.081 (0.033)0.014 Diabetesb−0.117 (0.033)<0.001−0.136 (0.032)<0.0010.054 (0.035)0.13−0.128 (0.030)<0.001−0.138 (0.032)<0.001 Strokeb−0.049 (0.033)0.14−0.102 (0.033)0.0020.042 (0.035)0.240.028 (0.031)0.37−0.027 (0.033)0.42Slope on Age 11 IQa−0.252 (0.068)0.001−0.026 (0.055)0.63−0.022 (0.045)0.620.077 (0.061)0.21−0.062 (0.041)0.14 Educationa−0.122 (0.070)0.08−0.001 (0.053)0.99−0.017 (0.043)0.69−0.074 (0.060)0.22−0.058 (0.041)0.16 Adult SESb0.011 (0.071)0.870.023 (0.058)0.70−0.022 (0.045)0.63−0.042 (0.094)0.650.028 (0.042)0.50 Lives aloneb−0.121 (0.068)0.070.032 (0.048)0.50−0.052 (0.045)0.260.045 (0.062)0.47−0.030 (0.042)0.47 Smoking categoryb−0.069 (0.075)0.360.039 (0.050)0.430.046 (0.048)0.34−0.203 (0.070)0.0040.005 (0.045)0.91 Physical activitya0.050 (0.073)0.500.017 (0.069)0.81−0.020 (0.049)0.680.071 (0.073)0.360.048 (0.045)0.29 Body mass indexb−0.110 (0.071)0.12−0.089 (0.053)0.09−0.036 (0.045)0.43−0.033 (0.063)0.60−0.057 (0.042)0.17 Alcohol units, weeka−0.183 (0.075)0.0150.014 (0.054)0.80−0.040 (0.050)0.420.034 (0.071)0.63−0.015 (0.046)0.74 APOE e4b−0.185 (0.066)0.005−0.215 (0.047)<0.001−0.235 (0.043)<0.001−0.044 (0.062)0.48−0.233 (0.040)<0.001 Depressive symptomsb−0.074 (0.067)0.27−0.068 (0.053)0.200.005 (0.046)0.91−0.117 (0.063)0.06−0.075 (0.042)0.07 CVDb−0.059 (0.067)0.38−0.055 (0.046)0.23−0.006 (0.046)0.89−0.087 (0.063)0.17−0.061 (0.042)0.15 Diabetesb−0.000 (0.073)0.99−0.036 (0.048)0.45−0.066 (0.048)0.17−0.052 (0.070)0.45−0.042 (0.043)0.33 Strokeb0.040 (0.079)0.610.076 (0.051)0.130.012 (0.050)0.82−0.098 (0.072)0.170.031 (0.046)0.50Model estimates are fully standardised. Path weights for calculation of the slope factor: Baseline = 0; to w2 = 2.98; to w3 = 6.75; to w4 = 9.81; to w5 = 12.53. Models were run separately for each domain; general cognitive function is based on the intercepts and slopes of the four cognitive domains. Boldtype indicates statistical significance following FDR (false discovery rate) correction. SE standard error, SES socio-economic status, CVD cardiovascular disease.aHypothesised to have a positive association with cognitive function.bHypothesised to have a negative association with cognitive function. Table 5Multivariate latent growth curve models: predictors of intercepts (age 70) and slopes of change (age 70–82) where predictors are entered simultaneously. PredictorsVisuospatial abilityProcessing speedMemoryVerbal abilityGeneral cognitive functionEstimate (SE)p valueEstimate (SE)p valueEstimate (SE)p valueEstimate (SE)p valueEstimate (SE)p valueIntercept on Ageb−0.110 (0.027)<0.001−0.149 (0.027)<0.001−0.157 (0.030)<0.001−0.089 (0.020)<0.001−0.140 (0.021)<0.001 Sex−0.261 (0.029)<0.001−0.022 (0.031)0.470.121 (0.033)<0.0010.002 (0.022)0.92−0.042 (0.023)0.07 Age 11 IQa0.494 (0.028)<0.0010.442 (0.029)<0.0010.561 (0.033)<0.0010.566 (0.020)<0.0010.668 (0.020)<0.001 Educationa0.109 (0.032)0.0010.031 (0.033)0.350.157 (0.036)<0.0010.239 (0.023)<0.0010.197 (0.025)<0.001 Adult SESb−0.124 (0.032)<0.001−0.137 (0.032)<0.0010.015 (0.036)0.69−0.110 (0.024)<0.001−0.120 (0.025)<0.001 Lives aloneb0.029 (0.028)0.30−0.003 (0.028)0.910.021 (0.031)0.50−0.033 (0.021)0.11−0.007 (0.021)0.74 Smoking categoryb−0.065 (0.028)0.02−0.095 (0.028)0.0010.008 (0.031)0.80−0.032 (0.021)0.12−0.026 (0.022)0.24 Physical activitya0.039 (0.030)0.200.082 (0.031)0.0090.044 (0.034)0.20−0.009 (0.023)0.400.035 (0.024)0.14 Body mass indexb0.084 (0.028)0.0030.051 (0.031)0.080.066 (0.032)0.03−0.053 (0.021)0.010.015 (0.022)0.50 Alcohol units, weeka0.000 (0.029)0.98−0.015 (0.047)0.740.036 (0.032)0.26−0.019 (0.021)0.37−0.011 (0.022)0.61 APOE e4b−0.100 (0.028)<0.001−0.103 (0.028)<0.001−0.038 (0.031)0.230.001 (0.021)0.96−0.056 (0.022)0.009 Depressive symptomsb−0.059 (0.028)0.03−0.101 (0.028)<0.001−0.072 (0.031)0.02−0.018 (0.021)0.38−0.066 (0.022)0.002 CVDb−0.034 (0.028)0.22−0.069 (0.028)0.0130.043 (0.031)0.170.013 (0.020)0.52−0.005 (0.021)0.80 Diabetesb−0.057 (0.028)0.04−0.057 (0.028)0.04−0.005 (0.031)0.88−0.053 (0.021)0.01−0.055 (0.021)0.01 Strokeb−0.024 (0.028)0.39−0.071 (0.028)0.0110.047 (0.031)0.120.028 (0.020)0.180.002 (0.021)0.93Slope on Ageb0.111 (0.062)0.080.029 (0.054)0.59−0.005 (0.044)0.910.262 (0.069)<0.0010.039 (0.041)0.34 Sex0.028 (0.067)0.680.075 (0.050)0.130.037 (0.048)0.440.083 (0.066)0.210.040 (0.044)0.37 Age 11 IQa−0.272 (0.077)<0.001−0.044 (0.057)0.44−0.027 (0.050)0.590.111 (0.070)0.11–0.062 (0.046)0.18 Educationa−0.094 (0.072)0.190.011 (0.058)0.85−0.027 (0.050)0.59−0.161 (0.073)0.03−0.057 (0.047)0.23 Adult SESb−0.092 (0.072)0.200.025 (0.059)0.67−0.043 (0.051)0.40−0.020 (0.069)0.77−0.010 (0.047)0.83 Lives aloneb−0.119 (0.065)0.07−0.037 (0.046)0.43−0.051 (0.045)0.260.014 (0.062)0.83−0.031 (0.042)0.45 Smoking categoryb−0.125 (0.072)0.080.022 (0.049)0.650.030 (0.049)0.53−0.192 (0.070)0.007−0.021 (0.044)0.63 Physical activitya0.047 (0.068)0.490.020 (0.055)0.760.006 (0.050)0.910.049 (0.069)0.470.062 (0.046)0.17 Body mass indexb−0.092 (0.067)0.17−0.073 (0.055)0.18−0.021 (0.047)0.66−0.004 (0.064)0.95−0.036 (0.042)0.39 Alcohol units, weeka−0.146 (0.074)0.0480.019 (0.057)0.74−0.047 (0.050)0.350.046 (0.071)0.51−0.015 (0.045)0.73 APOE e4b−0.170 (0.065)0.009−0.211 (0.047)<0.001−0.234 (0.044)<0.001−0.058 (0.061)0.35−0.246 (0.039)<0.001 Depressive symptomsb−0.100 (0.065)0.12−0.060 (0.055)0.270.013 (0.046)0.78−0.096 (0.063)0.13−0.071 (0.042)0.09 CVDb−0.064 (0.064)0.32−0.048 (0.046)0.300.005 (0.046)0.90−0.060 (0.063)0.34−0.053 (0.042)0.21 Diabetesb−0.012 (0.072)0.87−0.040 (0.050)0.42−0.088 (0.050)0.08−0.008 (0.071)0.91−0.043 (0.044)0.33 Strokeb0.012 (0.074)0.870.083 (0.051)0.100.027 (0.051)0.60−0.071 (0.071)0.320.039 (0.045)0.40Model estimates are fully standardised. Path weights for calculation of the slope factor: Baseline = 0; to w2 = 2.98; to w3 = 6.75; to w4 = 9.81; to w5 = 12.53. Models were run separately for each domain; general cognitive function is based on the intercepts and slopes of the four cognitive domains. Boldtype indicates statistical significance following FDR (false discovery rate) correction. SE standard error, SES socio-economic status, CVD cardiovascular disease.aHypothesised to have a positive association with cognitive function.bHypothesised to have a negative association with cognitive function.
In the APOE e4 non-carriers sub-group, the slopes, indicating negative mean change over time, were significant for processing speed (SD change/year = −0.068) and visuospatial ability (SD change/year = −0.033) only, but there was little (and non-significant) change in memory (−0.010) or verbal ability (−0.004). In the APOE e4 carrier sub-group, the mean slopes were negative and significant for all but verbal ability. Compared to the APOE e4 negative group, APOE e4 carriers showed greater SD decline in processing speed (SD change/year = −0.106 vs. −0.068), visuospatial ability (SD change/year = −0.065 vs. −0.033), and memory (SD change/year = −0.072 vs. −0.010). The difference was most marked in the slope for memory; APOE e4 carriers showed a seven-fold greater SD decline per year compared with APOE e4 non-carriers (and in the non-carrier group the slope for memory is non-significant). In contrast with the full sample and the APOE e4 non-carriers, memory decline was steeper than visuospatial ability decline in the APOE e4-positive group. Figure 3 presents horizontal bar plots illustrating the SD change/year in each cognitive test for all participants, and in each cognitive domain for all participants, APOE e4 carriers, and APOE e4 non-carriers. Formal tests of intercept and slope differences for APOE e4 carriers and APOE e4 non-carriers are carried out below. Fig. 3Standard deviation change per year in cognitive tests and cognitive domains from age 70 to 82.Standard deviation (SD) change per year in a each cognitive test (grouped by cognitive domain), and b each cognitive domain (grouped by all participants, and by APOE e4 non-carriers and carriers). SD change per year was derived from latent growth curve models, by calculating the slope mean divided by the intercept SD. SD change per year was converted to +ve values for illustrative purposes, with the exception of NART (National Adult Reading Test) which became –ve. Error bars represent the standard error of SD change per year.
## Univariate predictors of cognitive level and slope
First, we performed univariate analyses which regressed the intercepts and slopes at the level of each cognitive domain, and then general cognitive function, on all of the predictor variables individually. These univariate (partially-adjusted) models are distinct from the later models featuring multiple risk factors (fully-adjusted) which are the main models of interest. In the univariate models for cognitive ability level at age 70, all of the predictors except living alone were significantly associated with scores on at least one cognitive domain (full results are shown in Table 4). In the univariate models for cognitive slope, only APOE e4 status, alcohol, smoking, and age 11 IQ were significant predictors of decline across selected domains. APOE e4 carriers were more likely to show decline between age 70 and age 82 in visuospatial ability (β = −0.185, $$p \leq 0.005$$), speed (β = −0.215, $p \leq 0.001$), memory (β = −0.235, $p \leq 0.001$), and general cognitive ability (β = −0.233, $p \leq 0.001$). Smoking was associated with more decline in verbal ability (β = −0.203, $$p \leq 0.004$$) only, and a higher alcohol intake was associated with more decline in visuospatial ability only (β = −0.183, $$p \leq 0.015$$). Finally, a higher childhood cognitive ability (β = −0.252, $$p \leq 0.001$$) was associated with more decline in visuospatial ability only.
## Multivariate predictors of cognitive level at age 70
Next, we ran multivariate models to simultaneously estimate the associations of multiple risk factors on cognitive level at age 70. We ran collinearity diagnostics and inspected tolerance and variance inflation errors. Variance inflation factor and tolerance levels were within acceptable limits (tolerance > 0.10 and variance inflation factors < 10.0 [57]; and thus did not indicate multicollinearity. When all 15 predictors were modelled at the same time, 13 (not living alone or alcohol intake) made a significant contribution to the variability in cognitive ability level at age 70 (i.e. the intercept) in at least one of the cognitive domains (upper section, Table 5). Performance on all four cognitive domains and the general factor of cognitive function was associated with age (within-wave differences) (range, standardised beta (β) = −0.089 to −0.157, $p \leq 0.001$) and age 11 IQ (range β = 0.442 to 0.668, $p \leq 0.001$); age 11 IQ accounted for the most variance in cognitive level of any of the predictors, with the largest effect size (β = 0.668) for general cognitive function. Education and SES predicted performance in the general factor, and three out of four of the domains (no association between education-speed and between SES-memory), with an average (β) effect size across the four domains of −0.176 and −0.123, respectively. The directions of associations were as expected, such that individuals with better age 70 cognitive ability level were younger, had a higher childhood intelligence, were more educated, and were from more professional occupational classes. Male sex (β = 0.261, $p \leq 0.001$) was a predictor of better visuospatial ability level, and female sex was a predictor of better memory level (β = 0.121, $p \leq 0.001$), but sex was not a significant predictor of general cognitive function.
Healthy lifestyle factors were selectively associated with better cognitive ability at age 70: more physical activity (β = 0.082, $$p \leq 0.009$$) and less smoking (β = −0.095, p = − 0.001) with better processing speed. A higher BMI (a measure of obesity) was associated with a lower verbal ability (β = −0.053, $$p \leq 0.01$$) but conversely with higher visuospatial ability (β = 0.084, $$p \leq 0.003$$). Alcohol intake did not significantly predict age 70 cognitive ability in any domain. None of the lifestyle factors measured were significantly associated with general cognitive function in the multivariate model. APOE e4-positive carrier status predicted poorer visuospatial ability (β = −0.100, $p \leq 0.001$), processing speed (β = −0.103, $p \leq 0.001$) and general cognitive function (β = −0.056, $$p \leq 0.009$$) at age 70. History of disease was associated with lower cognitive scores but not consistently across domains: CVD (β = −0.069, $$p \leq 0.013$$) and stroke (β = −0.071, $$p \leq 0.011$$), were associated with lower processing speed, in addition to a non-FDR-significant association with diabetes (β = −0.057, $$p \leq 0.04$$). Diabetes was associated with lower verbal ability (β = −0.053, $$p \leq 0.01$$) and general cognitive function (β = −0.055, $$p \leq 0.01$$). Depressive symptoms were associated with lower processing speed (β = −0.101, $p \leq 0.001$) and general cognitive function (β = −0.066, $$p \leq 0.002$$). Notably, many of the previous univariate associations between individual predictors and cognitive level at age 70 (across selected domains) became non-significant in the multivariate models.
## Multivariate predictors of cognitive slope between age 70 and 82
In contrast to cognitive level at age 70, we found that few predictors were associated with longitudinal cognitive change between age 70 and 82 (as shown in Table 5 for slope, lower section) once all 15 predictors were entered simultaneously. APOE e4 carrier status accounted for the most variability in cognitive slopes. Possessing the APOE e4 allele was associated with significantly steeper decline in visuospatial ability (β = −0.170, $$p \leq 0.009$$), processing speed (β = −0.211, $p \leq 0.001$), memory (β = −0.234, $p \leq 0.001$), and general cognitive function (β = −0.246, $p \leq 0.001$), but not with verbal ability (β = −0.058, $$p \leq 0.35$$). Moreover, APOE e4 was the only unique significant predictor of cognitive change in processing speed, memory, and general cognitive function, with resultant effect sizes markedly larger in magnitude than any of the other variables. Other than being an APOE e4 allele carrier, a steeper slope in visuospatial ability was also associated with a having a higher age 11 IQ (β = −0.272, $p \leq 0.001$). The only predictors of a steeper verbal ability slope were more smoking (β = −0.192, $$p \leq 0.007$$), and contrary to expectations, a lower age (β = 0.262, $p \leq 0.001$). Comparisons between the univariate and multivariate predictor models for cognitive slope indicate that the univariate association between higher alcohol intake and greater decline in visuospatial ability (β = −0.183, $$p \leq 0.015$$) was non-significant in the multivariate model (β = −0.146, $$p \leq 0.05$$).
Figure 4 illustrates the unique variance (R2) accounted for by the 15 predictor variables in Table 5 for each cognitive domain, vs. a matched set of simulated random variables. These comparisons allow us to check whether our predictor group performed better than the same number of null variables, and are presented as stacked barplots showing the real data (in colour) and random data (in grey). The overall R2 for the set of real predictors was significantly larger than the null scenario across the domains: visuospatial ability (real = $20\%$, null = $4\%$); processing speed (real $8\%$ = null = $2\%$); memory (real = $8\%$, null = $1\%$); verbal ability (real = $16\%$, null = $4\%$); general cognitive function (real = $9\%$, null = $2\%$).Fig. 4Unique variance explained by model predictors vs. simulated (random) variables. Stacked barplots showing the unique variance (R2) in cognitive domain slopes explained by the predictor variables in the multivariate models (Table 5). Grey columns show the R2 explained by the same number of simulated (random) variables in each cognitive domain as a comparison.
## Discussion
We examined 12-year trajectories of cognitive functioning, using multiple measurement points across later life, in a birth cohort of community-dwelling older adults for whom childhood cognitive ability scores are available. Five waves of cognitive assessments were used to model change in visuospatial ability, processing speed, memory, and verbal ability from age 70 to 82 years, allowing a robust examination of rates of cognitive decline. Using a multivariate approach, we examined the relative contributions of determinants of individual differences in age 70-cognitive level and age 70 to 82-cognitive change, using 15 of the most commonly used candidate risk factors in the field of cognitive ageing. Our key finding is that APOE e4 status was the single most important factor determining longitudinal cognitive decline when all of the predictors were modelled simultaneously. Carriers of the APOE e4 allele show significantly steeper declines across the three “fluid” domains of memory, processing speed, and visuospatial ability, compared to non-carriers, even after adjusting for many other potential predictors which were strong correlates of age 70 cognitive level (including childhood IQ, education, adult socio-economic status, lifestyle, and health). APOE e4 status was the sole predictor of decline in general cognitive function—with a moderate to large effect size of 0.25 [58]—comparable in magnitude, for instance, to the reduction in risk of dying from head injuries associated with wearing a cycling helmet [59]. This contrasts with the relatively modest cross-sectional associations between APOE e4 and cognitive functioning at age 70 which suggests that the effect of APOE e4 on cognitive deficits becomes more manifest in later life. These findings are striking given that when many other candidate predictors of cognitive ageing slope are entered en masse, their unique contributions account for relatively small proportions of variance, beyond variation in APOE e4 status, and might indicate an increasing genetic influence on cognitive outcomes as individuals’ progress into their eighth and ninth decades of life.
The presence of faster rates of decline in APOE e4 carriers, across several different domains of cognitive functioning, adds valuable new data to the debate on whether APOE e4 influences “normal” cognitive ageing. Our findings stand in contrast with some studies reporting null findings such as the Australian PATH study [60], and the HALCyon programme which provided only very limited evidence of an effect of APOE e4 on a test of word recall, but not on other cognitive measures [19]. Discrepancies in findings may reflect differences in sample age; both samples were considerably younger than the present study, perhaps too young to show e4-related decrements. Our results extend prior work that does find an effect of APOE e4 in the following ways. First, we report that APOE e4 exerts broad and general adverse effects on cognitive functioning, typically only reported in cross-sectional meta-analytic data across many piecemeal studies [25] but not in a single longitudinal analysis. Second, we found a particularly deleterious effect of APOE e4 on memory decline, consistent with two single-candidate studies using a single memory test [23, 61]. Here, we show this association is robust to simultaneous adjustments in a multi-candidate study, and reliable across a broad cognitive trait of memory, captured by the latent domain. Third, we show that the relationship between APOE e4 and long-term cognitive decline is largely independent of childhood cognitive ability, an important confound (but rarely available measure) in studies of cognitive ageing [62]. Fourth, we were able to show that the APOE e4 allele affects age-related cognitive decline independently of possible cognitive impairment, dementia, and deaths to follow up, suggesting that this relationship is present, not just in dementia and AD [17, 63], but in cognitively “healthy” individuals.
Our results suggest that differences in cognitive functioning between e4 and non-e4 carriers become more pronounced with advancing age, regardless of any pathological changes. This finding aligns with earlier reports of an age effect of APOE e4 on cognition across the lifespan in single-determinant studies, with associations rarely seen in those <70 years [19, 23]. Age effects are consistent with theories that APOE e4 carriers are more vulnerable to damage accumulated over their lifetime, via reductions in neural protection and repair [64]. The APOE e4 allele is implicated in exacerbating neurodegeneration, tau pathology and inflammation; all pathological hallmarks of AD [65, 66]. Yet, the precise mechanisms by which APOE e4 exerts a deleterious effect on brain health in non-pathological ageing is currently unclear. In some studies, common neuropathologies including B-amyloidosis and tau tangle densities account for nearly all age-related cognitive decline [67, 68], raising the possibility that estimates of cognitive decline may be inflated by undiagnosed AD. However, residual effects of APOE e4 on cognition in cognitively-normal individuals have been reported even after controlling for AD pathology [69]. A recent neuroimaging study in UK Biobank has found that APOE e4 genotype associates with an increased burden of white matter hyperintensities, a marker of poor cerebrovascular health [70].
The presence of preclinical dementia may account for observed associations between APOE e4 and cognitive function [21, 71] leading to an overestimation of the effect of APOE e4 in age-associated, non-pathological cognitive decline. In the current study, the associations remained robust even after the exclusion of individuals with low MMSE scores indicating impaired cognition. With the exception of visuospatial ability, the effect sizes were of similar magnitude, indicating that the APOE e4-cognition associations were not driven by a sub-group who subsequently developed dementia. Our results are consistent with those of another study involving our sister cohort, the LBC1921, with whom we share similar methodology. Addressing a common criticism of studies investigating “normal” cognitive ageing—lack of diagnostic follow-up for dementia ascertainment—the authors used evidence from medical records, deaths certificates and clinical reviews to ascertain dementia status after 16 years of follow-up. They found that unrecognised dementia at baseline (age 79 years) had a small or no effect on the determinants of cognitive ageing including APOE e4 [72]. Given their conclusions, we judge that prodromal or undiagnosed dementia had little influence on our findings of a robust association of APOE e4 status and cognitive slope.
We found limited evidence in the LBC1936 that individual health behaviours alter rates of decline between ages 70–82 years when modelled in tandem with other life-course predictors. Those with a history of smoking showed faster declines in verbal ability, consistent with prior work documenting the detrimental effects of smoking on cognition and brain health [27, 29, 30], though the change in this crystallised domain was minimal over time. One major question for the field of cognitive ageing is whether various lifestyle choices all compete for a limited opportunity to enhance cognitive function or whether the effects could be additive, as part of a synergistic lifestyle pattern [73, 74]. While there were few individual effects, Fig. 4 makes it clear that together, lifestyle predictors account for a greater amount of the variance in cognitive decline than might be attributed to chance. In accordance with a “marginal gains” theory of cognitive ageing [28], individual differences in cognitive trajectories among our sample, probably reflect an accumulation of many small influences from numerous lifestyle (and other) factors. Though the magnitude of the observed associations between the various individual lifestyle factors and cognitive change were mostly small, if these associations represent a causal effect, their cumulative efforts are likely to have significance for cognitive health at the population level.
The presence of a significant intercept but not slope relationship with some past or premorbid factors supports a “passive” model of cognitive reserve [75]. That describes the situation, for instance, where highly-educated individuals continue to perform at a higher level of cognitive functioning than their less educated peers (i.e. influencing baseline differences, which we found), rather than having the ability to compensate for deficits (i.e. differential rates of cognitive decline over time, which we did not find). Other studies on cognitive decline show comparable findings for early-life socio-economic advantage [76] and education [77]. Here, this finding extends to early cognitive ability. Consistent with previous studies [36, 78], a higher childhood IQ—the strongest predictor of higher cognitive level in our sample—did not confer an advantage in terms of protection from steeper declines in the long-term. In fact, higher early-life cognition was associated with greater decline in visuospatial ability. This counterintuitive finding was surprising but not unusual, and may indicate regression to the mean, that is, a consequence of higher ability individuals performing relatively more poorly on tests with known ceiling effects when followed longitudinally [79]. Nevertheless, the current study benefits from knowing individuals’ cognitive starting point in order to ascertain degree of decline and to rule out confounding or reverse causation. Early-life cognition is associated with a subsequent cascade of social, behavioural and clinical effects [80], such that children with higher cognitive ability tend to become brighter and healthier adults [28], thus being able to remove this confound is important to reduce the likelihood of the observed associations being artefacts of the relationship between childhood IQ and healthy life markers. In doing so, our findings help to address an important issue in cognitive ageing research, namely, distinguishing differential preservation from preserved differentiation [8, 81]. With the clear exception of APOE, our results support the preserved differentiation of cognitive function only—whereby level of ability is a manifestation of prior ability—but not differential preservation (which leads to differences in subsequent rates of decline).
Finally, we observed that declines in processing speed between age 70 and 82 were greater than those of the other domains which supports the theory that processing speed is the core issue responsible for deficits in performance on complex cognitive measures in ageing populations [82–84]. Memory declined less steeply, across the whole sample, than processing speed and visuospatial ability, even in the ninth decade when one might expect to see more pronounced changes in this domain [85]. However, memory tests repeated longitudinally are subject to practice effects, whereby participants may improve or maintain their tests scores in spite of a cognitive decline [86]. Despite the potential of practice effects to obscure the variance in memory performance measured over time (e.g. in tests containing memorable information in stories or word lists), ageing effects were still present in the data, and if anything, they may lead to an underestimation of true effect sizes. Moreover, in the current study, we are interested in individual differences in changes over time. Salthouse has shown that there are no different predictors of individual differences in practice effects (other than chronological age, which is not a variable of concern in the LBC1936, owing to its being a narrow-age cohort) in longitudinal cognitive test scores from those of cognitive ageing [87]. Therefore, one may treat the various waves as a growth curve, supported by the model fit indices, even if there are temporary slight upward changes in some tests in some waves for some participants. Verbal ability showed evidence of stability with age, as expected [38, 88, 89]. Nevertheless, the observation of concomitant rises in word knowledge alongside marked declines in other cognitive measures with age, is still of empirical value.
The study results should be interpreted with several limitations in mind. Along with other cohort studies, the LBC1936 study has healthy participant bias. Lower rates of dropouts were seen among healthier individuals with a lower presence of comorbidities, and those with more education and a higher SES. We acknowledge the potential for underestimating the effects of smoking on cognitive ageing as a result of higher rates of premature mortality, particularly among long-term and/or heavy smokers. The LBC1936 study has a modest $20\%$ attrition rate over each successive follow-up, comparable to those of other highly valuable longitudinal cohort studies with repeated assessments, such as the Swedish National Study on Aging [90] and the English Longitudinal Study of Ageing [91]. However, using FIML in our LGC analyses partly addresses the issue of attrition from dropout or death by including all available data from each time-point, not just those who completed all five waves, resulting in less biased estimates. We relied upon self-report of medical history; a limitation which has implications for potential misclassification bias and some residual confounding. As some physiological processes preceding cognitive decline may occur before older age, the influence of some health behavioural factors, such as physical activity and BMI, may be stronger from mid-life compared with later-life measures [92–94], leading to an underestimation of their effects. We were also unable to explore associations according to APOE e4 allele variations; low numbers in each allele group were insufficient to conduct further comparisons between e2, e3 and e4 genotypes. We recognise that our cognitive intercept at age 70 is likely to be a conflation of both intercept and some degree of slope (i.e. cognitive ageing experienced up to that point). Without knowing individuals’ mid-age (reflecting peak cognitive function) to older-age trajectories, we cannot fully address the issue of preserved differentiation vs. differed preservation, though childhood IQ functions as a good proxy measure given its stability across the lifespan [95]. Finally, as a volunteer sample, the LBC1936 represent a well-educated and generally healthy group, which might preclude the generalisation of these findings to the broader ageing population, and as such, replication in other larger samples is warranted.
The major strengths of the LBC1936 are an unusually comprehensive cognitive battery, enabling good characterisation of cognitive domains across later life, and the availability of childhood IQ scores. Studies that can account for early-life cognitive ability are rare in studies of cognitive ageing and valuable with respect to the temporal primacy of cognitive changes. Identical tests and testing location were used at five measurement points over a 12-year follow-up period, covering an age-critical window in later life for accelerated cognitive decline. Modelling latent cognitive variables reduced the influence of potential measurement error inherent in using single cognitive tests. We further improved the robustness of our results by using FDR-adjustment for multiple associations, thereby reducing the chance of type I errors, and conducting sensitivity tests for incident dementia and death. Here we have used a baseline-value prediction approach. In future analyses, bi-/multivariate growth curve modelling could look at the changes over time in predictors and their associations with cognitive ageing.
In summary, we found that APOE e4 status was the single most important predictor of longitudinal cognitive decline from age 70 to 82, when fifteen potential predictors were modelled simultaneously, despite there being many life-course correlates of cognitive level at age 70. APOE e4 allele carriers experienced significantly steeper 12-year declines across the three “fluid” domains of memory, processing speed, and visuospatial ability, and a general factor of cognitive function, than non-carriers, denoting an increasingly widening gap in cognitive functioning as individuals’ progress into older age. Our findings suggest that [1] when many other candidate predictors of cognitive ageing slope are entered en masse, their unique contributions account for relatively small proportions of variance, beyond variation in APOE e4 carrier status, [2] APOE e4 status is important for identifying those a greater risk for accelerated cognitive ageing, even among ostensibly healthy individuals.
## Supplementary information
Supplementary Information The online version contains supplementary material available at 10.1038/s41380-022-01900-4.
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---
title: Increased intrinsic and synaptic excitability of hypothalamic POMC neurons
underlies chronic stress-induced behavioral deficits
authors:
- Xing Fang
- Yuting Chen
- Jiangong Wang
- Ziliang Zhang
- Yu Bai
- Kirstyn Denney
- Lin Gan
- Ming Guo
- Neal L. Weintraub
- Yun Lei
- Xin-Yun Lu
journal: Molecular Psychiatry
year: 2022
pmcid: PMC10005948
doi: 10.1038/s41380-022-01872-5
license: CC BY 4.0
---
# Increased intrinsic and synaptic excitability of hypothalamic POMC neurons underlies chronic stress-induced behavioral deficits
## Abstract
Chronic stress exposure induces maladaptive behavioral responses and increases susceptibility to neuropsychiatric conditions. However, specific neuronal populations and circuits that are highly sensitive to stress and trigger maladaptive behavioral responses remain to be identified. Here we investigate the patterns of spontaneous activity of proopiomelanocortin (POMC) neurons in the arcuate nucleus (ARC) of the hypothalamus following exposure to chronic unpredictable stress (CUS) for 10 days, a stress paradigm used to induce behavioral deficits such as anhedonia and behavioral despair [1, 2]. CUS exposure increased spontaneous firing of POMC neurons in both male and female mice, attributable to reduced GABA-mediated synaptic inhibition and increased intrinsic neuronal excitability. While acute activation of POMC neurons failed to induce behavioral changes in non-stressed mice of both sexes, subacute (3 days) and chronic (10 days) repeated activation of POMC neurons was sufficient to induce anhedonia and behavioral despair in males but not females under non-stress conditions. Acute activation of POMC neurons promoted susceptibility to subthreshold unpredictable stress in both male and female mice. Conversely, acute inhibition of POMC neurons was sufficient to reverse CUS-induced anhedonia and behavioral despair in both sexes. Collectively, these results indicate that chronic stress induces both synaptic and intrinsic plasticity of POMC neurons, leading to neuronal hyperactivity. Our findings suggest that POMC neuron dysfunction drives chronic stress-related behavioral deficits.
## Introduction
Chronic stress induces maladaptive behaviors and triggers the development of neuropsychiatric disorders, including depression, anxiety, and cognitive dysfunction. Extensive studies have focused on the brain regions that are typically associated with emotional, motivational and cognitive processes, such as the prefrontal cortex, hippocampus and amygdala, in these stress-related disorders [3]. However, the neural substrates and the precise circuit mechanisms that drive maladaptive behaviors and contribute to vulnerability to neuropsychiatric conditions remain poorly understood. The arcuate nucleus (ARC), located in the mediobasal hypothalamus around the third ventricle near the median eminence, has emerged as a brain site integrating and coordinating neural, neuroendocrine and behavioral responses to stress [1, 4–12].
The ARC contains two distinct populations of neurons that express proopiomelanocortin (POMC) or agouti-related protein (AgRP). POMC-derived alpha-melanocyte-stimulating hormone (α-MSH) is an endogenous agonist that activates melanocortin 3 and 4 receptors, whereas AgRP acts as an endogenous antagonist at the same receptors [13]. POMC and AgRP neurons in the ARC exhibit similar projection patterns throughout the brain [6, 14], innervating brain regions involved in neuroendocrine control and adaptive behaviors related to stress, such as the paraventricular nucleus of the hypothalamus (PVN), bed nucleus of the stria terminalis, and amygdala [14]. Nonetheless, these two distinct neuronal populations have so far predominately been studied in the context of feeding and energy balance [15–23]. However, while stimulating AgRP neurons induces a rapid and robust feeding response and weight gain, activation of POMC neurons causes only a marginal effect on feeding and body weight [17, 24, 25], which is in contrast to pharmacological studies with melanocortin receptor agonists [4, 5, 26]. We and others have demonstrated that central injection of α-MSH or its analogs induces stress-like endocrine and behavioral reactions [5, 27, 28], whereas blockade of melanocortin 4 receptors attenuates endocrine and behavioral responses to stress [27, 29–31]. Importantly, POMC gene variants in humans have been reported to interact with stress life events and associate with antidepressant treatment responses [32]. Exposure to different types of stressors such as restraint, immobilization or inescapable foot shock increases expression levels of POMC mRNA in the ARC [33–35]. We have previously shown that POMC neurons in the ARC can be activated rapidly by acute restraint and forced swim stress [4]. Likewise, POMC neurons recorded after acute stress or in the acute phase after repeated stress exposure exhibit hyperexcitability [9]. These results suggest that the endogenous POMC system is involved in stress responses.
Recently, we have shown that chronic unpredictable stress (CUS), a stress paradigm that generates behavioral deficits such as anhedonia and behavioral despair [1, 2], suppresses AgRP neuron activity through increasing synaptic inhibition and decreasing intrinsic neuronal excitability [1]. This hypoactivity of AgRP neurons correlates with the expression of CUS-induced behavioral deficits [1]. Moreover, direct stimulation of AgRP neurons was sufficient to reverse CUS-induced anhedonia and behavioral despair [1]. Given the anatomical and functional interactions with AgRP neurons, we hypothesize that POMC neurons may also undergo chronic stress-induced synaptic and intrinsic plasticity to modulate behavioral adaptation. In this study, we set out to determine how POMC neurons undergo stress-induced plastic changes and contribute to shaping behavioral susceptibility to chronic stress. Several important questions were addressed: a) how chronic stress modulates excitatory and inhibitory synaptic transmission and intrinsic excitability of POMC neurons; b) whether stimulation of POMC neurons mimics stress-induced behavioral responses; and c) whether activation and inhibition of POMC neurons affect stress susceptibility and chronic stress-induced behavioral deficits. To answer these questions, two lines of transgenic reporter mice were used for whole-cell patch clamp recordings to determine synaptic inputs and intrinsic membrane properties of POMC neurons following stress exposure. Additionally, a Cre-dependent DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) approach was employed to remotely manipulate POMC neuron activity to test the causal relationship between POMC neuron activity and behavioral consequences.
## Animals
Wild-type C57BL/6J, Pomc-Cre mice (Stock No. 005965), Pomc-GFP mice (Stock No. 009593) and Ai14 mice (Stock No. 007914) were purchased from Jackson Laboratory (Bar Harbor, ME, USA). Ai14 mice have a loxP-flanked STOP cassette preventing transcription of a CAG promoter-driven red fluorescent protein variant (tdTomato) and inserted into the Gt(ROSA)26Sor locus (Gt(ROSA)26Sortm14(CAG-tdTomato)). Ai14 mice express robust tdTomato fluorescence following Cre-mediated recombination [36]. Male Pomc-Cre mice were crossed with Ai14 tdTomato female mice to obtain Pomc-Cre;tdTomato mice with tdTomato fluorescence in Cre-expressing cells, which was used to identify POMC neurons. All animal procedures were approved by the Institutional Animal Care and Use Committees of University of Texas Health Science Center at San Antonio and Augusta University. For further details see SI Materials and Methods.
## Viral injections
Pomc-Cre mice at 7 weeks of age were used for virus injection as described elsewhere [1, 2, 37]. For further details see SI Materials and Methods.
## Whole-cell patch-clamp recordings
Electrophysiological recordings were performed as previously described [1, 2, 38]. For further details see SI Materials and Methods.
## Behavioral procedures
Behavioral tests were performed in adult female and male mice at 9–11 weeks of age. Animals were transferred to a testing room and habituated to the room conditions for 3–4 h before the beginning of behavioral experiments. Behavioral testing procedures were performed in the late light cycle except for the sucrose preference test, which was carried out during the first 2 h of the dark cycle. For the behavioral tests involving chemogenetic activation or inhibition, mice received an intraperitoneal (i.p.) injection of 0.3 mg/kg clozapine N-oxide (CNO; Sigma-Aldrich, Saint Louis, MO, USA) 30 min before testing. Behaviors were scored by investigators who were blinded to the treatments.
## Chronic unpredictable stress
Mice (7–9 weeks old) were subjected to different types of stressors at different times of the day for 10 consecutive days. The stressors included 2-h restraint, 15-min tail pinch, 24-h constant light, 24-h wet bedding with 45° cage tilt, 10-min inescapable foot shocks, 30-min elevated platform and social isolation (Table 1). Stress procedures were conducted in a procedure room. Mice exposed to the CUS procedure were singly housed. Control mice were group housed and briefly handled daily in the housing room. Table 1Subthreshold and chronic unpredictable stress procedures. StressorsAMPMDay 12-h restraint15-min tail pinchSUSa (3 days)CUSa (10 days)Day 224-h constant light2-h restraintDay 32-h restraint24-h 45° cage tilt and wet beddingDay 410-min inescapable shock (0.3 mA, 2-s duration, at random intervals with an average of 16 s)2-h restraintDay 52-h restraint30-min elevated platformDay 615-min tail pinch2-h restraintDay 72-h restraint24-h constant lightDay 824-h 45° cage tilt and wet bedding2-h restraintDay 92-h restraint10-min inescapable shock (0.3 mA, 2-s duration, at random intervals with an average of 16 s)Day 1030-min elevated platform2-h restraintaSUS subthreshold unpredictable stress, CUS chronic unpredictable stress.
For further details of each behavioral test, see SI Materials and Methods.
## Statistical analysis
All results are presented as mean ± s.e.m. ( standard error of mean). Statistical analyses were performed using GraphPad Prism 8.0 (GraphPad Software, Inc., CA). The Shapiro–Wilk test and the F test were used to test the normality and the equality of variances, respectively. For further details of statistical analysis, see SI Materials and Methods.
## Chronic unpredictable stress alters spontaneous firing patterns of POMC neurons
Our recent studies have demonstrated that repeated exposures to a variety of stressors in an unpredictable and uncontrollable manner for 10 consecutive days (CUS; Table 1) induce behavioral deficits in both male and female mice [1, 2]. Given the rapid responsiveness of POMC neurons in the ARC to acute stress [4], we examined whether chronic repeated exposure to stress causes persistent changes in the activity ARC POMC neurons. To visualize POMC neurons, Pomc-Cre mice were crossed with the Ai14-tdTomato mice to produce Pomc-Cre;tdTomato reporter mice, which enable the identification of tdTomato-positive cells as POMC neurons. Male and female Pomc-Cre;tdTomato mice at 7-8 weeks of age were subjected to 10 days of unpredictable stress, i.e., CUS. Whole-cell patch-clamp recordings under the current-clamp mode were made from POMC neurons of control mice and CUS mice 1 day after the last stress exposure (Fig. 1a1). First, data from male and female Pomc-Cre;tdTomato mice were combined for statistical analysis. We found that the frequency of spontaneous firing of POMC neurons was increased (Fig. 1a2–a3) and membrane potential was more depolarized after CUS exposure (Fig. 1a4). Moreover, we noticed that the percentage of silent POMC neurons (at frequencies <0.5 Hz) decreased by CUS [control $30\%$ (20 out of 67 neurons); CUS $14\%$ (11 out of 81 neurons)]. Then, male and female groups were analyzed separately to detect potential sex-specific effects of CUS. Both male and female mice exhibited increased spontaneous firing rates (Fig. 1a3) and depolarized membrane potential after CUS exposure (Fig. 1a4). These data indicate that POMC neurons become hyperactive after CUS exposure in mice of both sexes. Fig. 1Chronic unpredictable stress modulates spontaneous firing patterns of POMC neurons. Pomc-Cre;tdTomato mice. a1 Timeline of experimental procedures. a2 Left, representative fluorescent images of a coronal brain slice from a Pomc-Cre;tdTomato mouse showing fluorescent POMC neurons in the arcuate nucleus (ARC). Scale bars, 200 µm for low magnification (5×) and 20 µm for high magnification (40×). Right, representative traces of spontaneous action potentials of POMC neurons from control and CUS groups. a3, a4 Spontaneous firing rate (a3) and membrane potential (a4). Left, male and female combined, individual neurons (firing rate: Mann-Whitney test, $P \leq 0.001$; membrane potential: Mann-Whitney test, $P \leq 0.001$); middle-left, male and female combined, group neurons per mouse (firing rate: Welch’s test, $$P \leq 0.0049$$; membrane potential: Mann-Whitney test, $$P \leq 0.0022$$); middle-right, male mice-individual neurons (firing rate: Mann Whitney test, $$P \leq 0.0129$$; membrane potential: Mann Whitney test, $P \leq 0.001$); right, female mice-individual neurons (firing rate: Mann Whitney test, $$P \leq 0.0196$$; membrane potential: Mann Whitney test, $$P \leq 0.0256$$). a5 Spontaneous firing patterns. Upper panel: spontaneous firing patterns from male and female mice combined. Left, cumulative probability distributions of coefficients of variation; middle-left, average coefficients of variation, individual neurons (Mann Whitney test, $$P \leq 0.0060$$); middle-right, average coefficients of variation, group neurons per mouse (t[10] = 2.866; $$P \leq 0.0168$$); right, correlation analysis between spontaneous firing rates and coefficients of variation. Middle panel: spontaneous firing patterns from male mice (Mann Whitney test, $$P \leq 0.0102$$). Lower panel: spontaneous firing patterns from female mice (Mann Whitney test, $$P \leq 0.1266$$). Control (Ctrl): $$n = 67$$ neurons from three male (31 neurons) and three female (36 neurons) mice. CUS: $$n = 81$$ neurons from three male (44 neurons) and three female (37 neurons) mice. Pomc-GFP mice. b1 Experimental timeline. b2 Left, representative fluorescent images of a coronal brain slice from a Pomc-GFP mouse showing fluorescent POMC neurons in the ARC. Scale bars, 200 µm for low magnification (5×) and 20 µm for high magnification (40×). Right, representative traces of spontaneous action potentials of POMC neurons from control and CUS groups. b3, b4 Spontaneous firing rate (b3) and membrane potential (b4). Left, male and female mice combined, individual neurons (firing rate: Mann Whitney test, $P \leq 0.001$; membrane potential: Mann Whitney test, $$P \leq 0.0027$$); middle-left, male and female mice combined, group neurons per mouse (firing rate: t[11] = 4.244, $$P \leq 0.0014$$; membrane potential: t[11] = 3.180, $$P \leq 0.0088$$); middle-right, male mice-individual neurons (firing rate: Mann-Whitney test, $$P \leq 0.0083$$; membrane potential: t[39] = 1.973, #$$P \leq 0.0556$$); right, female mice-individual neurons (firing rate: t[26] = 4.228, $P \leq 0.001$; membrane potential: Mann Whitney test, $$P \leq 0.0215$$). b5 Spontaneous firing patterns. Upper panel: male and female mice combined. Left, cumulative probability distributions of coefficients of variation; middle-left, average coefficients of variation, individual neurons (Mann Whitney test, $$P \leq 0.0016$$); middle-right, average coefficients of variation, group neurons per mouse (t[11] = 3.867, $$P \leq 0.0026$$); right, correlation analysis between spontaneous firing rates and coefficients of variation. Middle panel: male mice (t[37] = 2.011, #$$P \leq 0.0517$$). Lower panel: female mice (Mann Whitney test, $$P \leq 0.0209$$). Ctrl: $$n = 32$$ neurons from three male (21 neurons) and three female (11 neurons) mice. CUS: $$n = 37$$ neurons from four male (20 neurons) and three female (17 neurons) mice. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs control group.
To determine the effects of CUS on firing patterns of POMC neurons, we analyzed the inter-spike interval distribution and the coefficient of variation, a measure of spike train irregularity. Under control conditions, POMC neurons displayed highly irregular spike times (coefficient of variation of the interspike intervals: male-mean, 1.127; female-mean, 0.7709). Analyses of the combined data from male and female mice revealed that CUS caused a shift in the cumulative probability distribution of interspike intervals to the left and resulted in a decrease in the coefficient of variation (Fig. 1a5). There was a negative correlation between firing rates and coefficients of variation (Fig. 1a5). These results indicate that POMC neurons fire more rapidly and regularly after CUS exposure. Further analysis of male and female groups separately showed that the cumulative probability distribution of interspike intervals was shifted to the left and the coefficient of variation was decreased by CUS in male but not in female mice (Fig. 1a5). Both male and female mice showed a negative correlation between firing rates and coefficients of variation under control and CUS conditions (Fig. 1a5).
In Pomc-Cre;tdTomato reporter mice, tdTomato-labeled POMC neurons could result from transient Cre expression during development [39]. To address this, we utilized Pomc-GFP mice to confirm the effects of CUS on the activity of POMC neurons. Pomc-GFP mice express enhanced green fluorescent protein (GFP) under control of the mouse Pomc promoter/enhancer regions, which accurately label the neurons with endogenous Pomc transcription in ARC [39, 40]. The stress procedure and the patch-clamp recording protocols used for Pomc-GFP mice were the same as used for Pomc-Cre;tdTomato mice (Fig. 1b1). First, data from male and female Pomc-GFP mice were combined for statistical analysis. Similar to that observed in Pomc-Cre;tdTomato mice, CUS resulted in an increase in firing rates (Fig. 1b2–b3) and a depolarization of the membrane potential (Fig. 1b4) of POMC neurons in mice of both sexes combined. Further analysis for male and female mice separately showed that CUS-induced changes in spontaneous firing of POMC neurons were not sex-specific. In addition, analysis of firing patterns of Pomc-GFP neurons revealed a shift in the cumulative frequency distribution of interspike intervals to the left and a decrease in the coefficient of variation of interspike intervals in male and female mice (Fig. 1b5). Additionally, a negative correlation between firing rates and coefficients of variation of interspike intervals was also confirmed in control and chronically stressed Pomc-GFP mice (Fig. 1b5). These data indicate that CUS increased firing rates and regularity of POMC neurons.
## Chronic unpredictable stress induces synaptic and intrinsic plasticity in POMC neurons
Alterations in synaptic drive could underlie the increased spontaneous firing rates in ARC POMC neurons. To test this possibility, we examined synaptic transmission at excitatory and inhibitory synapses of POMC neurons 1 day after the last stress exposure of CUS in Pomc-Cre;tdTomato mice (Fig. 2a). Whole-cell voltage-clamp recordings of EPSCs and IPSCs were performed at −60 mV holding potential. Spontaneous EPSCs, recorded in the presence of 100 μM picrotoxin, a GABAA receptor antagonist used to block GABAergic transmission, in ARC POMC neurons showed no significant changes in the frequency or amplitude (Fig. 2b1–b4). Recordings of spontaneous IPSCs in POMC neurons were made in the presence of AMPA and NMDA receptor antagonists to block glutamatergic synaptic transmission (Fig. 2c1). CUS decreased the mean frequency and amplitude of spontaneous IPSCs when data were pooled from males and females (Fig. 2c2); similar trends were observed when data were analyzed separately by sex (Fig. 2c3, c4). These results suggest that both GABAergic drive to POMC neurons, a presynaptic effect, and POMC neuron responsiveness to GABAA receptor activation, a postsynaptic response, were decreased by CUS, thus leading to synaptic disinhibition of POMC neurons. Fig. 2Chronic unpredictable stress affects spontaneous synaptic neurotransmission in POMC neurons.a-c Results from Pomc-Cre;tdTomato mice. a Experimental timeline. b Spontaneous EPSCs (sEPSCs) from Pomc-Cre;tdTomato mice. b1 Representative traces depicting sEPSCs. b2 sEPSCs from male and female Pomc-Cre;tdTomato mice combined. Left, cumulative probability plot for the interevent interval. Left insert, average frequency of sEPSCs, individual neurons (Mann Whitney test, $$P \leq 0.5796$$). Middle-left, average frequency of sEPSCs, group neurons per mouse (Mann Whitney test, $$P \leq 0.5887$$). Middle-right, cumulative probability plot for the amplitude. Middle-right insert, average amplitude of sEPSCs, individual neurons (Mann Whitney test, $$P \leq 0.6830$$). Right, average amplitude of sEPSCs, group neurons per mouse (t[10] = 0.2574, $$P \leq 0.8021$$). b3 sEPSC from male Pomc-Cre;tdTomato mice. Left, cumulative probability plot for the interevent interval. Left insert, average frequency of sEPSCs, individual neurons (Mann Whitney test, $$P \leq 0.3963$$). Right, cumulative probability plot for the amplitude. Right insert, average amplitude of sEPSCs, individual neurons (Mann Whitney test, $$P \leq 0.6875$$). b4 sEPSC from female Pomc-Cre;tdTomato mice. Left, cumulative probability plot for the interevent interval. Left insert, average frequency of sEPSCs, individual neurons (Mann Whitney test, $$P \leq 0.1576$$). Right, cumulative probability plot for the amplitude. Right insert, average amplitude of sEPSCs, individual neurons (Mann Whitney test, $$P \leq 0.8833$$). Ctrl: $$n = 55$$ neurons from three male (24 neurons) and three female (31 neurons) mice. CUS: $$n = 56$$ neurons from three male (27 neurons) and three female (29 neurons) mice. c Spontaneous IPSCs (sIPSCs) from Pomc-Cre;tdTomato mice. c1 Representative traces depicting sIPSCs. c2 sIPSC from male and female Pomc-Cre;tdTomato mice combined. Left, cumulative probability plot for the interevent interval. Left insert, average frequency of sIPSCs, individual neurons (Mann Whitney test, $$P \leq 0.0016$$). Middle-left, average frequency of sIPSCs, group neurons per mouse (t[10] = 2.190, #$$P \leq 0.0534$$). Middle-right, cumulative probability plot for the amplitude. Middle-right insert, average amplitude of sIPSCs, individual neurons (Mann Whitney test, $$P \leq 0.0069$$). Right, average amplitude of sIPSCs, group neurons per mouse (t[10] = 1.390, $$P \leq 0.1948$$). c3 sIPSC from male Pomc-Cre;tdTomato mice. Left, cumulative probability plot for the interevent interval. Left insert, average frequency of sIPSCs, individual neurons (Mann Whitney test, #$$P \leq 0.0588$$). Right, cumulative probability plot for the amplitude. Right insert, average amplitude of sIPSCs, individual neurons (Mann Whitney test, $$P \leq 0.0466$$). c4 sIPSC from female Pomc-Cre;tdTomato mice. Left, cumulative probability plot for the interevent interval. Left insert, average frequency of sIPSCs, individual neurons (Mann Whitney test, $$P \leq 0.0163$$). Right, cumulative probability plot for the amplitude (Mann Whitney test, $$P \leq 0.1321$$). Right insert, average amplitude of sIPSCs, individual neurons. Ctrl: $$n = 57$$ neurons from three male (28 neurons) and three female (29 neurons) mice. CUS: $$n = 53$$ neurons from three male (22 neurons) and three female (31 neurons) mice. d–h Results from Pomc-GFP mice. d Experimental timeline. e sEPSCs from Pomc-GFP mice (e1-representative traces of sEPSCs in POMC neurons; e2-male and female combined: frequency-individual neurons, Mann Whitney test, $$P \leq 0.7626$$; frequency-group neurons per mouse, Mann Whitney test, $$P \leq 0.7294$$; amplitude-individual neurons: Mann Whitney test, $$P \leq 0.7265$$; amplitude-group neurons per mouse: t[10] = 0.05081, $$P \leq 0.9605$$; e3-male only: frequency-individual neurons, Mann Whitney test, $$P \leq 0.7561$$; amplitude-individual neurons, t[20] = 1.293, $$P \leq 0.2106$$; e4-female only: frequency-individual neurons, t[23] = 0.03616, $$P \leq 0.9715$$; amplitude-individual neurons, t[23] = 0.3747, $$P \leq 0.7113$$). Ctrl: $$n = 21$$ neurons from three male (9 neurons) and three female (12 neurons) mice. CUS: $$n = 26$$ neurons from three male (13 neurons) and three female (13 neurons) mice. f sIPSCs from Pomc-GFP mice (f1-representative traces of sIPSCs in POMC neurons; f2-male and female combined: frequency-individual neurons, Mann Whitney test, $$P \leq 0.0065$$; frequency-group neurons per mouse, Mann Whitney test, $$P \leq 0.0169$$; amplitude-individual neurons: t[43] = 0.9380, $$P \leq 0.3535$$; amplitude-group neurons per mouse: Mann Whitney test, $$P \leq 0.6200$$; f3-male only: frequency-individual neurons, Unpaired t test with Welch’s correction, $$P \leq 0.0402$$; amplitude-individual neurons t[15] = 0.6136, $$P \leq 0.5487$$; f4-female only: frequency-individual neurons, Mann Whitney test, $$P \leq 0.0401$$; amplitude-individual neurons, t[26] = 0.8378, $$P \leq 0.4098$$). Ctrl: $$n = 25$$ neurons from three male (10 neurons) and four female (15 neurons) mice. CUS: $$n = 20$$ neurons from three male (7 neurons) and four female (13 neurons) mice. g Miniature EPSCs (mEPSCs) from Pomc-GFP mice (g1-representative traces of mEPSCs in POMC neurons; g2-male and female combined: frequency-individual neurons, Mann Whitney test, $$P \leq 0.9308$$; frequency-group neurons per mouse, t[10] = 1.181, $$P \leq 0.2651$$; amplitude-individual neurons: Mann Whitney test, $$P \leq 0.4993$$; amplitude-group neurons per mouse: t[10] = 0.9731, $$P \leq 0.3535$$; g3-male only: frequency-individual neurons, t[25] = 0.07453, $$P \leq 0.9412$$; amplitude-individual neurons t[25] = 0.3181, $$P \leq 0.7530$$; g4-female only: frequency-individual neurons, Mann Whitney test, $$P \leq 0.5930$$; amplitude-individual neurons, t[19] = 1.484, $$P \leq 0.1543$$). Ctrl: $$n = 23$$ neurons from three male (13 neurons) and three female (10 neurons) mice. CUS: $$n = 25$$ neurons from three male (14 neurons) and three female (11 neurons) mice. h Miniature IPSCs (mIPSCs) from Pomc-GFP mice (h1-representative traces of mIPSCs in POMC neurons; h2-male and female combined: frequency-individual neurons, Mann Whitney test, $$P \leq 0.1668$$; frequency-group neurons per mouse, Mann Whitney test, $$P \leq 0.1200$$; amplitude-individual neurons: Mann Whitney test, $$P \leq 0.7338$$; amplitude-group neurons per mouse: Mann Whitney test, $$P \leq 0.7104$$; h3-male only: frequency-individual neurons, Mann Whitney test, $$P \leq 0.9046$$; amplitude-individual neurons, Mann Whitney test, $$P \leq 0.4369$$; h4-female only: frequency-individual neurons, Mann Whitney test, $$P \leq 0.0943$$; amplitude-individual neurons, t[39] = 0.05230, $$P \leq 0.9586$$). Ctrl: $$n = 47$$ neurons from three male (23 neurons) and four female (24 neurons) mice. CUS: $$n = 33$$ neurons from three male (16 neurons) and three female (17 neurons) mice. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs control group.
The effects of CUS on spontaneous EPSCs and IPSCs in POMC neurons were also examined in the ARC of Pomc-GFP mice (Fig. 2d). Similar to the observations made in Pomc-Cre;tdTomato mice, neither the frequency nor the amplitude of spontaneous EPSCs was altered in POMC neurons from Pomc-GFP mice after CUS exposure (Fig. 2e). By contrast, CUS decreased the frequency, but not the amplitude, of spontaneous IPSCs in POMC neurons (Fig. 2f).
Spontaneous synaptic events (EPSCs and IPSCs) could be driven by action potential-dependent and/or -independent transmitter release. To determine whether CUS affects action potential-independent synaptic events, spontaneous, miniature EPSCs (mEPSCs) and miniature IPSCs (mIPSCs) were recorded in POMC neurons from Pomc-GFP mice in the presence of 1 µM tetrodotoxin to block sodium channels and action potentials. There were no significant changes in the frequency or amplitude of mEPSCs (Fig. 2g) or mIPSCs (Fig. 2h), suggesting that chronic stress facilitates synaptic inhibitory transmission through an action potential-dependent mechanism.
Alterations of intrinsic firing properties of POMC neurons could also contribute to the increased spontaneous firing rates in CUS mice. To explore this possibility, spontaneous, intrinsic action potentials in POMC neurons from Pomc-GFP mice were isolated pharmacologically using fast synaptic blockers to inhibit ionotropic glutamate and GABAA receptors. We analyzed the rate, pattern and shape of firing of action potentials of POMC neurons from control mice and mice subjected to 10 days of unpredictable stress (Fig. 3a). The intrinsic firing frequency was increased (Fig. 3b, c) and the membrane potential was depolarized after CUS (Fig. 3d) when data were pooled from males and females. Concomitantly, the percentage of silent POMC neurons (at frequencies <0.5 Hz) decreased from $29\%$ (10 out of 35 neurons recorded) in control mice to $3\%$ (1 out of 31 neurons) in CUS mice. These data indicate that the intrinsic activity of POMC neurons was dramatically increased by CUS. Analysis of the intrinsic firing patterns of POMC neurons revealed a shift in the cumulative frequency distribution of coefficient of variation of interspike intervals to the left and an increase in the firing regularity (Fig. 3e). The intrinsic firing rates correlated negatively with coefficients of variation of interspike intervals under both control and CUS conditions, with a shallower slope in CUS mice (Fig. 3e). Next, we assessed the effects of CUS on action potential waveform parameters (Fig. 3f). CUS had no effect on the threshold (Fig. 3f2) but decreased the amplitude of action potentials (Fig. 3f3). Moreover, POMC neurons from CUS mice exhibited increased action potential rise time (Fig. 3f4) and half-width (Fig. 3f5) and exhibited trends toward greater duration (Fig. 3f6) and decay time (Fig. 3f7). Furthermore, afterhyperpolarization, or AHP, the hyperpolarizing phase of a POMC neuron’s action potential was measured. The amplitude of AHP in POMC neurons was not consistently affected by CUS (Fig. 3f8). These data suggest that chronic exposure to unpredictable stress induces adaptations in the kinetics of action potentials of POMC neurons that may be partially related to changes in intrinsic firing properties. Fig. 3Chronic unpredictable stress increases intrinsic activity of POMC neurons.a Timeline of the CUS procedure and patch-clamp recordings of POMC neurons from Pomc-GFP mice in the presence of synaptic blockers. b Representative traces showing intrinsic action potentials of POMC neurons. Intrinsic firing rate (c) and membrane potential (d). Left, male and female mice combined, individual neurons (firing rate: Mann Whitney test, $P \leq 0.001$; membrane potential: t[64] = 2.307, $$P \leq 0.0243$$); middle-left, male and female mice combined, group neurons per mouse (firing rate: unpaired t test with Welch’s correction, $$P \leq 0.0106$$; membrane potential: t[10] = 0.7205, $$P \leq 0.4877$$); middle-right, male mice-individual neurons (firing rate: Mann Whitney test, $P \leq 0.001$; membrane potential: t[41] = 1.630, $$P \leq 0.1108$$); right, female mice-individual neurons (firing rate: unpaired t test with Welch’s correction, #$$P \leq 0.0685$$; membrane potential: t[21] = 0.9288, $$P \leq 0.3636$$). e Intrinsic firing pattern. Upper panel: male and female mice combined. Left, cumulative probability distributions of coefficients of variation; middle-left, average coefficients of variation, individual neurons (Mann Whitney test, $P \leq 0.001$); middle-right, average coefficients of variation, group neurons per mouse (t[10] = 2.669, $$P \leq 0.0235$$); right, correlation analysis between spontaneous firing rates and coefficients of variation. Middle panel: male mice (Mann Whitney test, $P \leq 0.001$). Lower panel: female mice (Mann Whitney test, $$P \leq 0.0076$$). f Action potential (AP) waveform. f1 Representative AP waveforms recorded in POMC neurons from control (brown line) and CUS (green line) mice. f2 AP threshold (male and female combined-individual neurons: t[64] = 1.467, $$P \leq 0.1471$$; male and female combined-group neurons per mouse: t[10] = 0.9658, $$P \leq 0.3569$$; male-individual neurons: t[41] = 1.456, $$P \leq 0.1529$$; female-individual neurons: t[21] = 1.286, $$P \leq 0.2124$$). f3 AP amplitude (male and female combined-individual neurons: t[64] = 2.376, $$P \leq 0.0205$$; male and female combined-group neurons per mouse: Mann Whitney test, $$P \leq 0.0152$$; male-individual neurons: t[41] = 2.201, $$P \leq 0.0334$$; female-individual neurons: Unpaired t test with Welch’s correction, $$P \leq 0.0060$$). f4 AP rise time (male and female combined-individual neurons: Mann Whitney test, $$P \leq 0.0080$$; male and female combined-group neurons per mouse: t[10] = 1.900, $$P \leq 0.0866$$; male-individual neurons: Mann Whitney test, $$P \leq 0.0318$$; female-individual neurons: t[21] = 1.900, $$P \leq 0.0173$$). f5 AP half width (male and female combined-individual neurons: Mann Whitney test, $$P \leq 0.0314$$; male and female combined-group neurons per mouse: Mann Whitney test, $$P \leq 0.1017$$; male-individual neurons: Mann Whitney test, $$P \leq 0.1743$$; female-individual neurons: t[21] = 3.186, $$P \leq 0.0044$$). f6 AP duration (male and female combined-individual neurons: Mann Whitney test, $$P \leq 0.0539$$; male and female combined-group neurons per mouse: Mann Whitney test, $$P \leq 0.0823$$; male-individual neurons: Mann Whitney test, $$P \leq 0.2714$$; female-individual neurons: t[21] = 2.901, $$P \leq 0.0085$$). f7 AP decay time (male and female combined-individual neurons: Mann Whitney test, $$P \leq 0.0905$$; male and female combined-group neurons per mouse: t[10] = 2.570, $$P \leq 0.0279$$; male-individual neurons: Mann Whitney test, $$P \leq 0.478$$; female-individual neurons: t[21] = 2.712, $$P \leq 0.0130$$). f8 AHP amplitude (male and female combined-individual neurons: t[64] = 1.948, $$P \leq 0.0558$$; male and female combined-group neurons per mouse: t[10] = 0.7414, $$P \leq 0.4755$$; male-individual neurons: t[41] = 1.504, $$P \leq 0.1402$$; female-individual neurons: Mann Whitney test, $$P \leq 0.6244$$). f2–f8 Left, male and female mice combined, individual neurons; middle-left, male and female mice combined, group neurons per mouse; middle-right, male mice-individual neurons; right, female mice-individual neurons. Ctrl: $$n = 35$$ neurons from three male (19 neurons) and three female (16 neurons) mice. CUS: $$n = 31$$ neurons from three male (24 neurons) and three female (7 neurons) mice. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs control group.
## Chemogenetic activation of POMC neurons induces anhedonia and behavioral despair
Next, we asked whether acute and chronic activation of POMC neurons can mimic stress-induced behavioral changes. Activation of POMC neurons was achieved by using Cre-dependent, AAV-mediated stimulatory DREADD-hM3Dq to depolarize Cre-expressing POMC neurons in Pomc-Cre transgenic mice. This method has been widely used to manipulate POMC neuron activity [21, 24]. AAV vectors expressing Cre-dependent hM3Dq, or AAV-DIO-hM3Dq-mCherry, were injected into the ARC of Pomc-Cre mice (Fig. 4a). Whole-cell patch clamp electrophysiological recordings confirmed that application of 5 µM CNO to hypothalamic slices increased the firing rates of POMC neurons expressing hM3Dq-mCherrry and depolarized their membrane potential (Fig. 4b). To test whether acute activation of POMC neurons can induce behavioral changes, male Pomc-Cre mice received bilateral injections of AAV-DIO-hM3Dq-mCherry or AAV-DIO-mCherry and were injected with a single dose of CNO (0.3 mg/kg, i.p.) 30 min prior to each behavioral test. Sucrose preference was measured within the first 2 h in the dark cycle and showed no difference between the two treatment groups (Fig. 4c1). Sniffing of estrus female urine by male mice is a sex-related reward-seeking behavior [41]. Acute CNO injection failed to produce an effect in the female urine sniffing test in male Pomc-Cre mice treated with hM3Dq (Fig. 4c1). These data indicate that acute stimulation of POMC neurons did not affect hedonic responses in male mice. Mice were also tested in the forced swim and locomotor activity tests after an acute CNO injection. Neither behavioral test showed significant differences between hM3Dq- and mCherry-treated male mice (Fig. 4c1). It has been reported that the effects of a single dose of CNO injection can persist more than 9 h [42]. Next, we extended the CNO treatment to 3 days (0.3 mg/kg, once daily) in a separate cohort of male Pomc-Cre mice expressing hM3Dq and mCherry. Sucrose preference was significantly decreased by 3 days of activation of POMC neurons; however, immobility in the forced swim test and locomotor activity were unaffected (Fig. 4c2). Next, we asked whether chronic activation of POMC neurons in male mice for 10 days could mimic behavioral deficits induced by CUS. To test this possibility, another cohort of male Pomc-Cre mice expressing hM3Dq and mCherry were treated with CNO (0.3 mg/kg, i.p. once daily) for 10 consecutive days. As shown in Fig. 4c3, 10 days of CNO treatment decreased sucrose preference, increased despair behavior in the forced swim test and induced a trend toward lower locomotor activity in male mice. To test whether chronic stimulation of POMC neurons impacts female urine sniffing time, a separate cohort of male mice were subjected 10 days of CNO injection (0.3 mg/kg daily). Time spent in sniffing female urine was reduced in mice treated with hM3Dq in comparison with those injected with mCherry (Fig. 4c3), suggesting that chronic stimulation of POMC neurons can induce different types of anhedonia in male mice. In contrast to male mice, female mice showed no significant changes in hedonic or despair behaviors following acute (single CNO injection), subacute (3-day CNO injection) or chronic activation (10-day CNO injection) of POMC neurons, as assessed in the sucrose preference test, the forced swim test or the open field test (Fig. 4d). The reason for this difference is unclear, but estrogens in intact, cycling female mice could increase the excitability of POMC neurons [43], which might lead to less responsiveness to CNO-mediated activation. Another possibility could be that female mice are more sensitive to potential confounding effects of anesthesia with ketamine that has sustained antidepressant properties [44].Fig. 4Repeated stimulation of POMC neurons induces behavioral deficits in male mice.a Schematic illustration showing stereotaxic injections of AAV-DIO-hM3Dq-mCherry or AAV-DIO-mCherry in the ARC of Pomc-Cre mice and a representative image showing mCherry-labeled POMC neurons in the ARC. b Left, representative trace of action potentials recorded in POMC neurons expressing hM3Dq in response to bath application of CNO (5 µM); middle, firing rate; right, membrane potential, $$n = 3$$ neurons per group. c Behavioral responses of male mice to CNO injection (0.3 mg/kg, i.p.). c1 A single CNO injection. Sucrose preference test (t[14] = 0.5232, $$P \leq 0.6090$$): AAV-DIO-mCherry, $$n = 7$$; AAV-DIO-hM3Dq-mCherry, $$n = 9$.$ Female urine sniffing test (treatment: F[1, 34] = 0.006, $$P \leq 0.9374$$; odor source: F[1, 34] = 72.98, $P \leq 0.0001$; treatment × odor source: F[1, 34] = 0.4274, $$P \leq 0.5177$$): AAV-DIO-mCherry, $$n = 9$$; AAV-DIO-hM3Dq-mCherry, $$n = 10$.$ Forced swim test (t[15] = 1.190, $$P \leq 0.2525$$) and locomotor activity (t[15] = 1.385, $$P \leq 0.1863$$): AAV-DIO-mCherry, $$n = 8$$; AAV-DIO-hM3Dq-mCherry, $$n = 9$.$ c2 Three days of CNO injections (once daily). Sucrose preference, t[10] = 3.518, $$P \leq 0.0056.$$ Forced swim test, unpaired t test with Welch’s correction, $$P \leq 0.8930.$$ Locomotor activity, t[10] = 0.01612, $$P \leq 0.9875.$$ AAV-DIO-mCherry, $$n = 6$$; AAV-DIO-hM4Di-mCherry, $$n = 6$.$ c3 Ten days of CNO injections (once daily). Sucrose preference test (Mann Whitney test, $$P \leq 0.0292$$), forced swim test (t[27] = 2.211, $$P \leq 0.0357$$) and locomotor activity (Mann Whitney test, $$P \leq 0.0868$$): AAV-DIO-mCherry, $$n = 14$$; AAV-DIO-hM3Dq-mCherry, $$n = 15$.$ Female urine sniffing test (F[1, 54] = 3.410, $$P \leq 0.0703$$; odor source: F[1, 54] = 60.87, $P \leq 0.0001$; treatment × odor source: F[1, 54] = 3.293, $$P \leq 0.0751$$): AAV-DIO-mCherry, $$n = 13$$; AAV-DIO-hM3Dq-mCherry, $$n = 16$.$ d Behavioral responses of female mice to CNO injection (0.3 mg/kg, i.p.). d1 A single CNO injection. Sucrose preference test (t[15] = 0.8355, $$P \leq 0.4165$$): AAV-DIO-mCherry, $$n = 8$$; AAV-DIO-hM3Dq-mCherry, $$n = 9$.$ d2 Three days of CNO injections (once daily). Sucrose preference test (Mann Whitney test, $$P \leq 0.5403$$): AAV-DIO-mCherry, $$n = 8$$; AAV-DIO-hM3Dq-mCherry, $$n = 9$.$ Forced swim test (t[12] = 1.125, $$P \leq 0.2827$$) and locomotor activity (t[12] = 0.5193, $$P \leq 0.6130$$): $$n = 7$$ per group. d3 Ten days of CNO injections (once daily). Sucrose preference test, t[16] = 0.6782, $$P \leq 0.5074.$$ Forced swim test, t[16] = 0.6083, $$P \leq 0.5516.$$ Locomotor activity, t[16] = 1.335, $$P \leq 0.1943.$$ AAV-DIO-mCherry, $$n = 9$$; AAV-DIO-hM3Dq-mCherry, $$n = 9$$ per group. * $P \leq 0.05$, **$P \leq 0.01$vs mCherry group.
## Chemogenetic activation of POMC neurons increases susceptibility to subthreshold levels of unpredictable stress
Our next question was whether acute activation of POMC neurons could increase susceptibility to subthreshold levels of unpredictable stress. We have previously shown that mice exposed to 3 days of unpredictable stress show no significant change in sucrose preference [1]. In the present study, multiple behavioral tests, including sucrose preference, forced swimming and open field tests, were conducted to assess behavioral consequences 1 day after exposure to unpredictable stress (first 3 days in Table 1). As expected, none of these behaviors were significantly altered by this short duration of unpredictable stress (Fig. 5a). Thus, this stress paradigm was used as a subthreshold form of unpredictable stress (SUS) to assess the impact of selective activation of POMC neurons on stress susceptibility. We have previously shown that POMC neurons can be rapidly activated by acute stress, as evidenced by c-fos induction [4]. Given the findings described above that 10 days of CUS increased the spontaneous firing activity of POMC neurons, we first asked whether the SUS protocol can induce long-lasting changes in neuronal firing activity of POMC neurons. To address this question, mice were subjected to 3 days of SUS (Table 1) and POMC neurons were recorded in hypothalamic slices 1 day after the last stress exposure. We found that the firing rate and the membrane potential of POMC neurons were not significantly affected by SUS (Fig. 5b). To test whether acute activation of POMC neurons increases susceptibility to stress, mice expressing hM3Dq and mCherry in Pomc-Cre neurons were tested for sucrose preference after acute CNO injection, then subjected to 3 days of SUS followed by behavioral tests after acute CNO injection (Fig. 5c). As shown in Fig. 4c1, d1, acute activation of POMC neurons by a CNO injection had no effect on sucrose preference in male or female mice prior to SUS exposure but significantly reduced sucrose preference in both male and female mice after SUS exposure (Fig. 5c1, c2) and increased immobility time in the forced swim test in female but not male mice (Fig. 5c3). Neither male nor female mice showed significant changes in locomotor activity (Fig. 5c4), which suggests that the forced swim results were not confounded by non-specific changes in mobility. These results indicate that acute activation of POMC neurons increases stress susceptibility in both male and female mice. Fig. 5Acute activation of POMC neurons increases susceptibility to subthreshold unpredictable stress (SUS) in both male and female mice.a Left, Experimental timeline. Male wild-type C57BL/6J mice (sucrose preference: t[17] = 4.578, $$P \leq 0.1329$$; forced swim: unpaired t test with Welch’s correction, $$P \leq 0.2852$$; locomotor activity: t[17] = 0.0991, $$P \leq 0.9222$$): Ctrl, $$n = 10$$; SUS, $$n = 9$.$ b Recordings of POMC neurons from Pomc-GFP mice after exposure to SUS. Left-top, experimental timeline; left-bottom, representative whole-cell recording traces of POMC neurons; middle, firing rates (Mann Whitney test, $$P \leq 0.8874$$); right, membrane potential (t[81] = 1.248, $$P \leq 0.2157$$). Ctrl: $$n = 36$$ neurons from two male (15 neurons) and two female (21 neurons) mice. SUS: $$n = 47$$ neurons from two male (22 neurons) and two female (25 neurons) mice. c A combination of stimulation of POMC neurons and SUS exposure in Pomc-Cre mice. Upper panel, experimental timeline. c1 Sucrose preference test in male mice (before SUS: Mann Whitney test, $$P \leq 0.6930$$; after SUS: t[15] = 2.391, $$P \leq 0.0304$$). c2 Sucrose preference test in female mice (before SUS: Mann Whitney test: $$P \leq 0.1728$$; after SUS: Mann Whitney test: $$P \leq 0.0062$$). c3 Forced swim test in male (left, unpaired t test with Welch’s correction, $$P \leq 0.5930$$) and female (right, t[16] = 2.703, $$P \leq 0.0157$$) mice. c4 Locomotor activity of male (left, unpaired t test with Welch’s correction, $$P \leq 0.3261$$) and female (right, t[16] = 0.2257, $$P \leq 0.8243$$) mice. Male: AAV-DIO-mCherry, $$n = 8$$; AAV-DIO-hM3Dq-mCherry, $$n = 9$.$ Female: AAV-DIO-mCherry, $$n = 10$$; AAV-DIO-hM3Dq-mCherry, $$n = 8$.$ * $P \leq 0.05$, **$P \leq 0.01$ vs control or mCherry group.
## Chemogenetic inhibition of POMC neurons is sufficient to reverse anhedonia and despair behavior induced by CUS
We next asked whether inhibition of POMC neurons can reverse CUS-induced behavioral deficits. First, we confirmed the effect of CNO on POMC neurons by whole-cell patch clamp recordings from hM4Di-expressing POMC neurons from Pomc-Cre mice injected with AAV-DIO-hM4Di-mCherry in the ARC (Fig. 6a). CNO application decreased the firing rate and hyperpolarized the membrane potential (Fig. 6b), demonstrating that CNO-mediated activation of hM4Di inhibited the activity of POMC neurons. To test the behavioral effects of CNO-induced inhibition of POMC neurons, male and female Pomc-Cre mice received intra-ARC injection with AAV-DIO-hM4Di-mCherry or AAV-DIO-mCherry and were then divided into two groups for 10 days of CUS exposure or daily brief handling as control. The hM4Di- and mCherry-treated mice showed no differences in their sucrose preference prior to stress exposure and in the absence of CNO injection (Fig. 6c1, d1). As shown previously [1], CUS significantly decreased sucrose preference in both male and female mice prior to CNO injection (Fig. 6c1, d1). This reduction was reversed by an acute CNO injection (0.3 mg/kg, i.p.) in Pomc-Cre mice expressing hM4Di, compared with mCherry-expressing control Pomc-Cre mice (Fig. 6c1, d1). In addition, CUS induced despair behavior, as indicated by increased immobility in the forced swim test; this effect was also reversed by acute inhibition of POMC neurons with CNO injection in hM4Di-expressing Pomc-Cre mice (Fig. 6c2, d2), whereas locomotor activity was not altered by either CUS or CNO treatment (Fig. 6c3, d3). These results suggest that acute inhibition of POMC neurons is sufficient to reverse CUS-induced behavioral deficits. Fig. 6Inhibition of POMC neurons reverses chronic unpredictable stress-induced behavioral deficits.a Schematic illustration showing stereotaxic injection of AAV-DIO-hM4Di-mCherry or AAV-DIO-mCherry in the ARC of Pomc-Cre mice and a representative image showing mCherry-labeled POMC neurons. b Left, representative traces of action potentials recorded in POMC neurons expressing hM4Di in response to bath application of CNO (5 µM); middle, firing rate (paired t-test, t[2] = 5.756, $$P \leq 0.0289$$); right, membrane potential (paired t-test, t[2] = 4.488, $$P \leq 0.0462$$). $$n = 3$$ neurons per group. c Timeline of experimental procedures in male Pomc-Cre mice. c1 Sucrose preference test before and after CUS without or with acute CNO injection (0.3 mg/kg, i.p.). Pre-CUS: Kruskal-Wallis test, $$P \leq 0.1529$$; post-CUS w/o CNO: Kruskal-Wallis test, $P \leq 0.001$; post-CUS w/ CNO: Kruskal-Wallis test, $$P \leq 0.0143.$$ c2 Forced swim test after CUS with acute CNO injection (Brown-Forsythe ANOVA test, $$P \leq 0.0015$$). c3 Locomotor activity after CUS with acute CNO injection (F[2,27] = 0.2077, $$P \leq 0.8138$$). Ctrl+mCherry, $$n = 9$$; CUS + mCherry, $$n = 10$$; CUS + hM4Di-mCherry, $$n = 11$.$ d Timeline of experimental procedures in female Pomc-Cre mice. d1 Sucrose preference test before and after CUS without or with acute CNO injection. Pre-CUS: Kruskal-Wallis test, $$P \leq 0.6505$$; post-CUS w/o CNO: Kruskal-Wallis test, $$P \leq 0.0023$$; post-CUS w/CNO: F[2,26] = 16.77, $P \leq 0.001.$ Ctrl+mCherry, $$n = 9$$; CUS + mCherry, $$n = 9$$; CUS + hM4Di-mCherry, $$n = 11$.$ d2 Forced swim test after CUS with acute CNO injection (F[2,27] = 10.05, $P \leq 0.001$). Ctrl+mCherry, $$n = 9$$; CUS + mCherry, $$n = 10$$; CUS + hM4Di-mCherry, $$n = 11$.$ d3 Locomotor activity after CUS with acute CNO injection (F[2,26] = 0.8191, $$P \leq 0.4519$$). Ctrl+mCherry, $$n = 9$$; CUS + mCherry, $$n = 9$$; CUS + hM4Di-mCherry, $$n = 11$.$ * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ vs Ctrl+mCherry group or CUS + mCherry group.
## Discussion
In the ARC of the hypothalamus, POMC and AgRP neurons are well-positioned to relay and integrate peripheral and central signals to elicit adaptive and maladaptive behavioral responses to environmental challenges. In parallel with the investigations of synaptic and intrinsic plasticity of AgRP neurons using a CUS paradigm [1], this study assessed the impact of the same chronic stress paradigm on POMC neuron firing and behavioral consequences of DREADD-mediated control of POMC neuron activity. We demonstrated that CUS depolarized POMC neurons and increased their firing rates through modulating both synaptic and intrinsic neuronal properties. Repeated activation of POMC neurons was sufficient to induce anhedonia and behavioral despair, mimicking repeated exposure to stress. By contrast, acute inhibition of POMC neurons was able to reverse behavioral deficits induced by CUS. Collectively, these data suggest that POMC neurons are both necessary and sufficient for chronic stress-induced behavioral phenotypes.
Anhedonia, loss of interest and pleasure, is a common symptom in depression and other psychiatric illnesses. The ARC has been shown to be involved in reward processing and motivated behaviors [45, 46], but only recently dysfunction of specific neuronal populations in the ARC was discovered to be associated with stress-induced anhedonia [1, 9]. Using the same CUS paradigm, we have demonstrated that chronic stress hyperpolarizes AgRP neurons and decreases their firing rates [1]. Furthermore, inhibition of AgRP neurons increases stress susceptibility, whereas activation of AgRP neurons reverses anhedonia and behavior despair in CUS mice [1]. In contrast to the impact of CUS on AgRP neurons, we found that CUS depolarizes POMC neurons and increases their firing frequency. Notably, in the present study, the whole-cell patch clamp recordings of POMC and AgRP neurons were performed 1 day after the final stress session to eliminate acute stress effects. This is in contrast to a recent study that recorded the activity of POMC and AgRP neurons immediately after exposure to restraint stress [9]. Given that POMC neurons have been shown to be activated quickly by restraint stress, as evidenced by c-fos induction 30 min after stress exposure [4], it is not surprising that POMC neuron firing was increased in mice recorded after a single exposure to restraint stress or immediately following the last stress session of repeated restraint stress [9]. The initial activation of neurons in response to acute stress has been reported to be followed by a decline or depression of neuronal activity with the termination of stress [47]. Indeed, when recorded at 1 day following restraint stress in a subthreshold unpredictable stress paradigm (3 days of stress exposure), we observed no change in POMC neuron firing. These findings suggest that chronicity, unpredictability and variability in stress exposure are important factors in driving persistent hyperactivity of POMC neurons.
POMC neurons in the ARC receive both GABAergic and glutamatergic inputs from multiple brain regions [14, 48, 49]. The observed hyperactivity of POMC neurons following chronic stress exposure could result from modulation of excitatory and inhibitory synaptic transmission [50–53]. Under basal conditions, there are more excitatory than inhibitory synapses on POMC neurons [49]. We found that CUS had no effect on excitatory synaptic transmission, but decreased inhibitory synaptic inputs to POMC neurons. The frequency of spontaneous IPSCs was decreased in POMC neurons following CUS, reflecting presynaptic modifications. Furthermore, this decrease was eliminated by blocking action potential formation and its propagation, suggesting that CUS induces a presynaptic hyperpolarization in GABAergic terminals which synapse onto POMC neurons. Given that AgRP neurons can release GABA onto POMC neurons in the ARC [54] and that AgRP neurons are hyperpolarized by CUS [1], it is reasonable to assume that the decreased inhibitory synaptic transmission in POMC neurons is caused in part by hyperpolarized GABAergic AgRP terminals. This notion is supported by the findings that ablation of AgRP neurons causes a dramatic reduction in spontaneous GABAergic synaptic transmission in POMC neurons [55], and that optogenetic stimulation of AgRP neurons inhibits POMC neuron firing [54, 56]. However, some studies reported that AgRP neurons may not be a primary source of GABA onto POMC neurons, and the relevance of GABAergic inputs from AgRP to POMC neurons is state-dependent [48, 50, 57, 58]. The exact interplay between POMC neurons and AgRP neurons in stress responses and adaptations requires further investigation. Nonetheless, CUS-induced weakening of GABAergic inputs to POMC neurons, in the absence of changes in glutamatergic inputs, would cause the synaptic excitation/synaptic inhibition balance to shift toward excitation. This could contribute to hyperactivity of POMC neurons.
In POMC neurons, persistent increases in intrinsic excitability occur in parallel with synaptic modifications following CUS. The intrinsic neuronal excitability determines the translation of synaptic input to the output function of a given neuron. One possible mechanism for increased intrinsic excitability of POMC neurons is the regulation of expression and distribution of ion channels inserted into the membrane of POMC neurons that contribute to the electrical properties and depolarization potential [59, 60]. POMC neurons were reported to possess ATP-sensitive potassium (KATP) channels and express the KATP channel subunits Kir6.2 and SUR1 [61, 62]. KATP channel openers induce an outward K+ current in the vast majority of POMC neurons [62], leading to membrane hyperpolarization and reduced neuronal activity [62, 63]. Conversely, pharmacological blockade of KATP channels can activate POMC neurons [61]. These studies suggest the importance of KATP channels for normal activity of POMC neurons. However, it is unknown whether CUS suppresses expression and/or function of the KATP channels, leading to closure of the channels. Alternatively, inhibition of Ca2+-dependent K+ (SK) currents may contribute to hyperactivity of POMC neurons observed in this study. Blocking SK channels was reported to reactivate POMC neurons [60]. Future investigations of ion channel regulation will provide insights into the mechanisms underlying CUS-induced hyperactivity of POMC neurons.
Notably, POMC neurons exhibited higher degrees of firing regularity after CUS exposure, while they fire spontaneously in an irregular manner under control conditions. The mechanisms driving variability in spike-timing of POMC neurons are unknown. Dendritic morphology plays a critical role in determining neuronal firing patterns [64–67]. Chronic stress alters dendritic morphology in many brain regions [68, 69]. It is possible that CUS may induce changes in dendritic morphology of POMC neurons, contributing to firing regularity. Another determinant of neuronal firing patterns is the composition and density of ion channels [67, 70]. It has been shown that SK channels control firing regularity by modulating sodium channel availability [71, 72]. Voltage-gated K channels [73–75] and HCN channels [76, 77] are also involved in regulating the waveform and spike regularity. In addition, the firing regularity can be influenced by the properties and variability of synaptic inputs [78, 79]. Future studies will identify the key mechanism that controls firing patterns of POMC neurons and how firing regularity influences neuronal information processing.
While exposure to CUS induced similar effects on POMC neuron excitability in male and female mice, behavioral responses to repeated activation of POMC neurons exhibited sex differences. Repeated activation of POMC neurons in stress-naïve male mice for 3 or 10 days induced behavioral deficits, including decreased sucrose preference, decreased sex-related reward seeking behavior and increased behavioral despair. However, stress-naïve female mice failed to show any behavioral changes. Although acute activation of POMC neurons was not sufficient to induce significant behavioral effects in stress-naive mice, the susceptibility of both male and female mice to subthreshold unpredictable stress was increased by acute stimulating POMC neurons. On the other hand, acute inhibition of POMC neurons was able to rescue behavioral deficits induced by CUS in both male and female mice. These studies suggest that hyperactivity of POMC neurons is required for the induction and expression of CUS-induced behavioral deficits. Among behavioral tests, sucrose preference has been widely used as a reliable measure of anhedonia in both male and female mice [80]. It is conceivable that the impact of altering POMC neuron activity on sucrose preference could be consequential to changes in caloric consumption rather than a true preference for sweet taste. In this study, however, sucrose preference was conducted within the first 2 h in the dark cycle in mice provided with free access to food and water. Previous studies have shown that neither chemogenetic activation by i.p. injection or continuous infusion of CNO, nor optogenetic activation of POMC neurons, affects food intake within 2 h [17, 21, 24, 25]. Thus, the observed changes in sucrose preference were unlikely to reflect the impact of POMC neuron activity on metabolism.
Collectively, our findings indicate that activation of POMC neurons in the ARC is both necessary and sufficient to mediate stress susceptibility and induce anhedonia and behavioral despair. Further studies investigating the mechanisms underlying the synaptic disinhibition and intrinsic hyperexcitability of POMC neurons will provide insight into how POMC neurons modulate stress-related behaviors. Together with our previous findings that stimulating AgRP neurons decreases stress susceptibility and reverses CUS-induced behavioral deficits [1], these results suggest that POMC neurons act in opposition to AgRP neurons in behavioral and neural plasticity to chronic stress. Thus, hypothalamic POMC and AgRP neurons can be viewed as Yin-Yang partners in modulating responses and adaptations to stress. Whether efferent projections from these neurons converge on the same downstream targets to control behavioral susceptibility to stress, and whether their influences require melanocortin receptor signaling, need to be investigated.
## Supplementary information
Supplementary Information The online version contains supplementary material available at 10.1038/s41380-022-01872-5.
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|
---
title: Antidepressants that increase mitochondrial energetics may elevate risk of
treatment-emergent mania
authors:
- Manuel Gardea-Resendez
- Brandon J. Coombes
- Marin Veldic
- Susannah J. Tye
- Francisco Romo-Nava
- Aysegul Ozerdem
- Miguel L. Prieto
- Alfredo Cuellar-Barboza
- Nicolas A. Nunez
- Balwinder Singh
- Richard S. Pendegraft
- Alessandro Miola
- Susan L. McElroy
- Joanna M. Biernacka
- Eva Morava
- Tamas Kozicz
- Mark A. Frye
journal: Molecular Psychiatry
year: 2022
pmcid: PMC10005962
doi: 10.1038/s41380-022-01888-x
license: CC BY 4.0
---
# Antidepressants that increase mitochondrial energetics may elevate risk of treatment-emergent mania
## Abstract
Preclinical evidence suggests that antidepressants (ADs) may differentially influence mitochondrial energetics. This study was conducted to investigate the relationship between mitochondrial function and illness vulnerability in bipolar disorder (BD), specifically risk of treatment-emergent mania (TEM). Participants with BD already clinically phenotyped as TEM+ ($$n = 176$$) or TEM− ($$n = 516$$) were further classified whether the TEM associated AD, based on preclinical studies, increased (Mito+, $$n = 600$$) or decreased (Mito−, $$n = 289$$) mitochondrial electron transport chain (ETC) activity. Comparison of TEM+ rates between Mito+ and Mito− ADs was performed using generalized estimating equations to account for participants exposed to multiple ADs while adjusting for sex, age at time of enrollment into the biobank and BD type (BD-I/schizoaffective vs. BD-II). A total of 692 subjects ($62.7\%$ female, $91.4\%$ White, mean age 43.0 ± 14.0 years) including 176 cases ($25.3\%$) of TEM+ and 516 cases ($74.7\%$) of TEM- with previous exposure to Mito+ and/or Mito- antidepressants were identified. Adjusting for age, sex and BD subtype, TEM+ was more frequent with antidepressants that increased ($24.7\%$), versus decreased ($13.5\%$) mitochondrial energetics (OR = 2.21; $$p \leq 0.000009$$). Our preliminary retrospective data suggests there may be merit in reconceptualizing AD classification, not solely based on monoaminergic conventional drug mechanism of action, but additionally based on mitochondrial energetics. Future prospective clinical studies on specific antidepressants and mitochondrial activity are encouraged. Recognizing pharmacogenomic investigation of drug response may extend or overlap to genomics of disease risk, future studies should investigate potential interactions between mitochondrial mechanisms of disease risk and drug response.
## Introduction
As the pharmacopoeia for major depressive episodes in bipolar disorder (BD) is markedly underdeveloped, antidepressants are invariably used with little evidence base. This clinical practice is of significant consequence as antidepressant prescriptions for BD in the USA have more than doubled in the last two decades from $17.9\%$ to $40.9\%$ [1]. In addition to relatively high rates of treatment non-response, antidepressants have the potential to increase the likelihood of a switch process, invariably defined as antidepressant-induced mania (AIM) [2], treatment-emergent mania (TEM+) [3], and/or cycle acceleration [4]. The increased energy expenditure of mania associated with impulsivity, poor judgment, psychosis, and/or loss of insight can drive high risk behaviors often resulting in hospitalization or incarceration; further, the aftermath of mania can have enduring negative impact on quality of life [5–7].
A random effects meta-analysis of 35 clinical trials of bipolar depressed patients reported a switch rate of $12.5\%$ with and $7.5\%$ without antidepressant use [8]. A Swedish registry study identified that patients with BD treated with antidepressant (AD) monotherapy, in comparison to AD with concurrent mood stabilization, were at significant increased risk of treatment-emergent mania (TEM+), most notably, during the first 3 months of treatment (hazard ratio=2.83, $95\%$ CI = 1.12, 7.19) [9]. The clinical factors most associated with TEM+ include younger age, female sex, mixed symptoms, and type I BD [6]. As there is increasing interest is developing a cumulative risk model of TEM+ based on clinical and biological markers, when not use an antidepressant is a focused area of biomarker development with great potential to impact practice by primary or secondary prevention of mania [10, 11].
It has long been recognized that the neurobiology of BD is driven, in part, by mitochondrial dysfunction as exemplified by reduced expression of electron transport chain (ETC) genes in frontal cortex and hippocampus [12, 13]. The resulting impaired oxidative phosphorylation with a shift towards glycolysis and overall decreased adenosine-5’-triphosphate (ATP) production in response to energy demands has been proposed to be one of the main drivers of BD pathophysiology [14, 15]. Suboptimal mitochondrial function (SMF) in BD has been operationalized at several critical time points of illness vulnerability including early brain development resulting in structural and/or functional change in plasticity, genetic risk before illness, relapse risk into mania, psychosis, or depression in established disease, and relapse based on non-specific symptoms characteristic to mitochondrial disorders (i.e., fatigue, circadian rhythm disturbance) [16–19].
Targeting antidepressants and mitochondrial function is further justified based on our preclinical rodent model of ACTH-driven, imipramine treatment-resistant depression whereby electrode implantations to the nucleus accumbens elicited mania-like behavior in a subset of animals ($30\%$). This behavioral phenotype was associated with increased mitochondrial respiration, specifically an increased state 3/state 4 respiration control ratio (RCR), suggestive of increased respiratory efficiency [20]. This finding was not driven exclusively by imipramine, the tricyclic antidepressant first shown in controlled evaluations to be associated with a high incidence of mania [31], but more likely an interaction between imipramine and an acute inflammatory response associated with DBS electrode placement in nucleus accumbens [47].
Mechanistically, antidepressants have been shown to differentially impact ETC complex activity [21–23]. While the specific mechanism of antidepressant associated increase of ETC activity is not fully understood, animal models suggest an upregulation of mitochondrial activity, including cellular respiration, occurring during acute antidepressant treatment, followed by decreased or unchanged activity in chronic treatment (≥28 days) [24]. The purpose of our study was to assess whether antidepressants that increase mitochondrial activity are associated with higher rates of TEM+.
## Methods and materials
A subset of participants from the Mayo Clinic Bipolar Disorder Biobank with known history of antidepressant exposure and clinical outcome measure were included in this study [25]. The Biobank sample consists of patients aged 18 through 80 years of age at time of enrollment with a type I or II bipolar disorder or schizoaffective bipolar disorder as confirmed by structured interview. Participants completed a questionnaire focused on demographics, illness variables and environmental influences and provided a blood sample [26]. Exclusion criteria included active psychosis or active suicidal ideation. Recruitment sites for the Biobank included Mayo Clinic, Lindner Center of HOPE/University of Cincinnati, University of Minnesota, Universidad Autónoma de Nuevo Leon (Mexico) and Universidad de los Andes (Chile). Each of the study sites received approval by their institutional review and every participant provided written informed consent for inclusion. Further details on study design and phenotyping are reviewed extensively in earlier work [25].
Through the Bipolar Biobank Clinical Questionnaire (BiB-CQ), research clinicians assessed and documented comorbid conditions and psychotropics used across lifetime, including antidepressants, as well as history of TEM+ while on each medication [6]. Based on an earlier meta-analytic work, which emphasized the importance of standardizing a narrow phenotype [27], TEM+ was defined as a manic/hypomanic episode by DSM criteria occurring within 60 days of starting or increasing an antidepressant dose [6, 28]. TEM− controls were characterized as ≥60-day exposure to an antidepressant with no associated manic/hypomanic episode.
Emmerzaal et al. 2021 [22], assessed the impact of psychotropic drugs on each complex of the ETC (Fig. 1) including state 3 (ADP stimulated respiration) and state 4 respiration (non-ADP stimulated), citrate synthase activity (first step of the Krebs cycle and proxy of mitochondrial mass) and malate dehydrogenase (final step of the Krebs cycle). Based on this recent preclinical review bupropion, nortriptyline, paroxetine, and venlafaxine were identified as antidepressants that increased mitochondrial function (Mito+), while amitriptyline and escitalopram as antidepressants that decreased mitochondrial function (Mito-). In this line [21], Table 1 reflects the distribution of biobank antidepressant drug exposure and clinical outcome (TEM+ vs TEM−).Fig. 1Global effect of antidepressants on mitochondrial respiratory chain complexes. A Schematic overview of the mitochondrial electron transport chain (ETC), a cluster of protein complexes and electron transporters in the inner mitochondrial membrane that generate ATP. The electrons generated during the oxidation of NADH and FADH2, in complexes I and II, respectively, are transported through coenzyme Q (CoQ), complex III (CIII), cytochrome C (Cyt C) and complex IV (CIV or COX). As electrons are transferred through the chain, energy is released to pump protons (H+), generating an electrochemical gradient across the membrane. Finally, in complex V (CV), also known as ATP synthase, the electrochemical gradient is used to catalyze the production of ATP from ADP. B Summary of the global effect of the investigated antidepressants and their effect on each complex of the ETC [22] based on preclinical studies [55–59]: A green box with an upward arrow indicates an increase in the activity of the specific complex after exposure to an antidepressant, a blue box with a downward arrow indicates a decrease in the activity of the specific complex after exposure to an antidepressant and a yellow box with a “~” sign indicates no effect observed. Abbreviations: CI Complex I, NADH:ubiquinone oxidoreductase, NADH reduced nictotinamide adenine dinucleotide, NAD+ nictotinamide adenine dinucleotide, CII Complex II, succinate-coenzyme Q reductase, CoQ coenzyme Q, FADH reduced flavin adenine dinucleotide, FAD flavin adenine dinucleotide, CIII Complex III, coenzyme Q – cytochrome c reductase, Cyt C cytochrome C, CIV Complex IV, cytochrome C oxidase, CV Complex V, ATP synthase, ADP adenosine diphosphate, ATP adenosine triphosphate. Table 1Participants with history of exposure to the assessed antidepressants. Mito+ antidepressantsNTEM+ ($$n = 148$$)TEM− ($$n = 452$$)Bupropion40068 ($17.0\%$)332 ($83.0\%$)Nortriptyline292 ($6.9\%$)27 ($93.1\%$)Paroxetine22749 ($21.6\%$)178 ($78.4\%$)Venlafaxine21945 ($20.6\%$)174 ($78.4\%$)Mito− antidepressants($$n = 39$$)($$n = 250$$)Amitriptyline615 ($8.2\%$)56 ($91.8\%$)Escitalopram25035 ($14.0\%$)215 ($86.0\%$) We first assessed whether rates of TEM+ were different with respect to potential confounders such as sex, age, race, BD type, BD illness (e.g., psychosis) and psychiatric comorbidities (Table 2). We used chi-square tests and two-sample t-tests to formally assess these differences for categorical and continuous variables, respectively, when large differences were observed. To assess our primary aim of whether TEM+ rates differ between Mito+ and Mito- antidepressants, we compared the rate of TEM+ between Mito+ and Mito− using generalized estimating equations (using a logit link and symmetric correlation structure) to account for patients that took both Mito+ and Mito− Ads during the course of treatment. To account for the potential confounders of TEM+ rates and based on previous clinical studies, we adjusted this analysis for sex, age at time of enrollment into the biobank and BD type (BD-I/schizoaffective vs. BD-II). [ 6, 29, 30] As the analysis was conducted using data from retrospective assessment of TEM+, it was out of the scope of this analysis to adjust for other factors that may dynamically influence mitochondrial health, such as lifestyle factors, childhood trauma, chronic stress, exercise, and dietary habits. Table 2Demographic variables and bipolar disorder subtype in participants treated with antidepressants that increase versus decrease mitochondrial activity. Antidepressants that ↑ mitochondrial activity (Mito+)Antidepressants that ↓ mitochondrial activity (Mito−)VariableTEM+ ($$n = 148$$)TEM− ($$n = 452$$)TEM+ ($$n = 39$$)TEM− ($$n = 250$$)Sex (female), n (%)96 ($64.9\%$)278 ($61.5\%$)28 ($71.8\%$)173 ($69.2\%$)Age, mean (SD)41.1 ± 13.644.7 ± 13.737.9 ± 14.343.1 ± 13.8Race (White), n (%)131 ($89.1\%$)413 ($92.2\%$)36 ($92.3\%$)237 ($95.2\%$)Ethnicity (Hispanic), n (%)13 ($9.3\%$)22 ($4.9\%$)4 ($10.8\%$)14 ($5.6\%$)SCID diagnosis, n (%) Type I BD116 ($78.4\%$)314 ($69.5\%$)26 ($66.7\%$)184 ($73.6\%$) Type II BD31 ($20.9\%$)131 ($29.0\%$)13 ($33.3\%$)62 ($24.8\%$) Schizoaffective, bipolar1 ($0.7\%$)7 ($1.5\%$)0 ($0.0\%$)4 ($1.6\%$)History of psychosis, yes n (%)78 ($53.4\%$)275 ($61.7\%$)15 ($38.5\%$)100 ($40.7\%$)History of suicide attempts, yes n (%)62 ($41.9\%$)162 ($36.2\%$)14 ($36.8\%$)103 ($41.5\%$)Adult attention deficit disorder, yes n (%)33 ($22.8\%$)109 ($24.4\%$)11 ($28.2\%$)57 ($23.2\%$)Generalized anxiety disorder, yes n (%)217 ($48.5\%$)73 ($50.7\%$)19 ($48.7\%$)124 ($51.7\%$)Obsessive compulsive disorder, yes n (%)18 ($12.7\%$)68 ($15.2\%$)7 ($17.9\%$)41 ($16.8\%$)
## Results
A total of 692 subjects ($62.7\%$ female, $91.4\%$ White, mean age 43.0 ± 14.0 years) including 176 cases ($25.3\%$) of TEM+ and 516 cases ($74.7\%$) of TEM-with previous exposure to Mito+ and/or Mito− antidepressants were identified. At the time of enrollment into the biobank, TEM+ participants were significantly younger than their TEM− counterpart (40.6 ± 13.8 vs. 43.8 ± 13.9; $$p \leq 0.009$$), but there were no significant differences in frequency of BD type I between groups (TEM + $76.1\%$ vs. $70.9\%$; $$p \leq 0.31$$). As shown in Table 2, there were also no large differences in the frequency of history of psychosis or rates of psychiatric comorbidities (Table 2).
Participants were further classified based on whether the specific AD they had been exposed to increased (Mito+ = 600) or decreased (Mito− = 289) mitochondrial activity; noting that some participants have been exposed to both types of ADs. Adjusting for sex and BD subtype, and after accounting for patient overlap between Mito+ and Mito- groupings, TEM + was more frequent with use of antidepressants that increase mitochondrial activity versus those that decrease it (Mito+ $24.7\%$ vs. Mito− $13.5\%$; OR = 2.21; $$p \leq 0.000009$$) after adjusting for age, sex, and BD type (Fig. 2 and Table 2).Fig. 2Increased rates of TEM+ with antidepressants that increase mitochondrial function. Rates of treatment-induced mania with antidepressants that increase (Mito+) were two times more frequent than with those that decrease (Mito−) mitochondrial function [TEM+ Mito+ = $24.7\%$; TEM+ Mito− = $13.5\%$; OR = 2.21; $$p \leq 0.000009$$].
## Discussion
To our knowledge, this is the first study to clinically investigate the mitochondrial energetics profile of specific antidepressants [20] and its association with the adverse drug related event of treatment-emergent mania. When compared to participants exposed to antidepressants that decrease mitochondrial activity, treatment-emergent mania was two times more common in patients exposed to antidepressants that increase mitochondrial energetics. The higher rates of TEM+ observed with Mito+ ADs align with clinical evidence suggesting an increased risk of mood switch with venlafaxine and, to a lesser extent, paroxetine [2, 31–33].
This study has several strengths, most notably a hypothesis driven novel classification of antidepressants beyond conventional drug mechanism of action, and inclusion of a cohort of patients with clinician-defined treatment-emergent mania. One of the main findings from an earlier metanalytic review from our group was the lack of consensus defining the clinical phenotype [26]; the duration of causality of starting antidepressant and subsequent mania for the six studies was up to 52 weeks. The narrower time frame of the phenotype (8 weeks) was a strength of the original study [6, 27] which was used for this current investigation. An additional strength of this study is adjusting for gender and bipolar subtype, which are known risk factors for mood switch [29, 30].
There are a number of study limitations. Due to the retrospective nature of the assessment, age at time of TEM+ occurrence was not obtained, and thus not used as a covariate. Similarly, the study did not control for confounding factors present at the time of study enrollment such as concurrent psychotropic use or comorbid conditions that significantly impact tissue-specific mitochondrial activity (including, but not limited to, diet and BMI, type 2 diabetes, tobacco use, trauma, stress) [34]. These variables should be targeted in future prospective studies assessing TEM and mitochondrial function. Additional limitations include data related to psychotropics used at the time of TEM+ which was not uniformly available. While this is a limitation, previous research has shown the switch rate with antidepressants is greater for BD-I vs BD-II patients, despite greater use of antimanic mood stabilizers [30, 35]; these new clinical data, alongside animal models that suggest an upregulation of mitochondrial activity during acute antidepressant treatment [24] may provide plausible rationale as to mechanism for antidepressants breaking through antimanic mood stabilization. Finally, the classification of antidepressants increasing and decreasing mitochondrial energetics for this clinical study is based on a preclinical systematic review [22]. The preclinical studies were highly variable in study design, with the majority of drugs having mixed results, including the tricyclic imipramine, arguably, after venlafaxine, the antidepressant with the clearest signal for affective switch [36]. While the mixed results category limits strength of the aggregate classification, the data for paroxetine, venlafaxine, nortriptyline, bupropion vs escitalopram and amitriptyline all have preclinical mitochondrial functioning data (state $\frac{3}{4}$ respiration, citrate synthase and malate dehydrogenase activity) that are all uniformly positive and negative, respectively. Clearly, future prospective clinical studies on specific antidepressants and mitochondrial activity are encouraged.
As previously mentioned, electron transport chain activity is sensitive to a variety of intrinsic and extrinsic stressors, many of which are common in BD, and to stress mediators (i.e., glucocorticoids) that can lead to a mitochondrial allostatic overload [37]. For instance, early life trauma has been associated with structural and functional mitochondrial changes and impaired energy production; chronic stress is linked with decreased ETC activity, including impaired complex I activity, oxidative stress and mtDNA damage [17, 38]. As an example, it has been hypothesized that suboptimal mitochondrial function in the central nervous system (CNS) can lead to increased likelihood of PTSD through insufficient energy production to cover the increased energy demands of higher CNS glucose metabolism, resulting in increased compensatory complex I activity. Likewise, in the context of PTSD and prolonged stress, release of cytochrome C from the mitochondrial membrane into the cytosol triggers the apoptotic pathway resulting in cell death [39] Within metabolic conditions, obesity and high fat diets reduce mitochondrial number and respiratory capacity, including reduced complex IV and cytochrome C activity, due, in part, to an overload of glucose and fatty acids. The resultant increase in the reduced form of nictotinamide adenine dinucleotide (NADH) production and increased electron availability to the ETC complexes increases reactive oxygen species (ROS) and inflammation [40]. While the overall net effect of obesity is a decrease in mitochondrial energetics, different changes occur through the ETC, including an increase in complexes II and IV and a decrease in complex I and III and ATP synthase [40]. It is worth noting that the specific effects on mitochondria vary not only between diseases but by tissues as well; for instance, in skeletal muscle and adipose tissues, diabetes mellitus and obesity are associated with reduced total activity of the ETC [34, 41]. Contrary to the progressive deleterious effects of metabolic disorders on mitochondrial function, evidence suggests that healthy dietary patterns, including dietary restrictions with and without exercise, indirectly improve mitochondrial capacity by increased expression of genes involved in mitochondrial function [39] Mitochondria are highly sensitive as well to environmental toxicants, such as tobacco smoke, that can alter mitochondrial DNA, oxidant generation and mitochondrial respiration [42]The latter is partially altered by the susceptibility of ETC complexes to inactivation by carbon monoxide leading to a diminished ATP generation. Similar to the impact that healthy lifestyle has on mitochondrial function, cessation of smoking can lead to a restoration of the mitochondrial function and health. Hence the collective relevance of environmental factors that might impact mitochondrial function that, in consequence, might convey increased disease susceptibility in the context of bipolar disorders.
Attempts to clarify the directionality of the complex interrelationship between mitochondrial function and antidepressants must take into consideration the influences exerted by drug combination, duration of treatment and whether there is a cell-type or tissue-specific effect [24]. For example, in preclinical models, the combination of olanzapine/fluoxetine has been shown to increase hippocampal complex I activity both in acute and chronic treatment phases, compared to fluoxetine alone, that increased in acute treatment only [43]. Similarly, Abdel-Razaq et al. [ 2011] identified, in an in vitro study, that complexes I and IV may be more sensitive to an acute antidepressant-induced inhibition at treatment initiation than other ETC complexes [44]. Lithium is known to increase mitochondrial ETC complex I activity in leukocytes of subjects with bipolar depression, and mitochondrial ETC activity was positively associated with plasma lithium levels [45]. The high rates of polypharmacy in BD may interfere with the measurement of ETC activity, adding a layer of complexity to the assessment of the interrelationship of mitochondrial function and psychotropics [45].
Understanding the primary pathophysiology of ETC dysfunction in BD (i.e., disease risk) may help guide pharmacogenomic studies. A systematic review of 10 ETC microarray gene expression studies in BD would suggest a main driver of ETC dysfunction is in complex I, with reduced gene expression of NDUFV1, NDUFS1, NDUFS8, and NDUFS7. Importantly, NDUFS7 directly couples electron transfer between the iron sulfur cluster and ubiquinone, a critical exchange of electrons for cellular energy production [12, 46]. Assessment of the ETC complex I, the entry enzyme of oxidative phosphorylation, and complex IV, the final enzyme with a rate-limiting role in the cellular respiration, may serve as a proxy of mitochondrial bioenergetics of the brain in psychiatric disorders, as they are known to be impacted by psychotropics [47, 48]. This is further supported by clinical data on upregulation of complex I subunits during mania compared to depressive episodes and healthy controls, suggesting mitochondrial complex activation [48, 49]. Our group previously identified, through mtDNA sequence data of BD-1 patients ($$n = 224$$), a higher risk of psychosis with U haplogroup, as well as a variation in ND4 gene, implicated in ETC energy regulation [50]. Additionally, increased ceramide concentrations, involved in mitochondria-mediated apoptosis and associated with decreased activity of complexes I, IV and V, have been demonstrated in individuals with BD [51–53] and are likely aggravated by certain antidepressant medications (i.e., fluoxetine, fluvoxamine, paroxetine, escitalopram and amitriptyline) [54].
In conclusion, our study provides early evidence that support the hypothesis of an amplified response in mitochondrial energetics of select antidepressants that drive, in part, the pathophysiology of treatment-emergent mania. In addition, these data suggest categorizing antidepressants based on mitochondrial energetics, and not solely monoaminergic conventional drug mechanism of action (SSRI, SNRI, TCA, MAOI), may be of value and warrant further consideration for future larger clinical and pharmacogenomic studies. Recognizing pharmacogenomic investigation of drug response may extend or overlap to genomics of disease risk, future clinical studies should investigate potential interactions between mitochondrial mechanisms of disease risk that may predispose to antidepressant associated treatment-emergent mania and may have a clear impact on the management and treatment of patients with bipolar disorder.
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|
---
title: Preparation of polyclonal anti-Schistosoma mansoni cysteine protease antibodies
for early diagnosis
authors:
- Alyaa Farid
journal: Applied Microbiology and Biotechnology
year: 2023
pmcid: PMC10006032
doi: 10.1007/s00253-023-12408-4
license: CC BY 4.0
---
# Preparation of polyclonal anti-Schistosoma mansoni cysteine protease antibodies for early diagnosis
## Abstract
### Abstract
In many parts of the tropics, schistosomiasis is a major parasitic disease second only to malaria as a cause of morbidity and mortality. Diagnostic approaches include microscopic sampling of excreta such as the Kato-Katz method, radiography, and serology. Due to their vital role in many stages of the parasitic life cycle, proteases have been under investigation as targets of immunological or chemotherapeutic anti-Schistosoma agents. Five major classes of protease have been identified on the basis of the peptide hydrolysis mechanism: serine, cysteine, aspartic, threonine, and metalloproteases. Proteases of all five catalytic classes have been identified from S. mansoni through proteomic or genetic analysis. The study aimed to produce polyclonal antibodies (pAbs) against schistosomal cysteine proteases (CP) to be used in the diagnosis of schistosomiasis. This study was conducted on S. mansoni-infected patients from highly endemic areas and from outpatients’ clinic and hospitals and other patients infected with other parasites (Fasciola, hookworm, hydatid, and trichostrongyloids). In this study, the produced polyclonal antibodies against S. mansoni cysteine protease antigens were labeled with horseradish peroxidase (HRP) conjugate and used to detect CP antigens in stool and serum samples of S. mansoni-infected patients by sandwich ELISA.
The study involved 200 S. mansoni-infected patients (diagnosed by finding characteristic eggs in the collected stool samples), 100 patients infected with other parasites (Fasciola, hookworm, hydatid, and trichostrongyloids), and 100 individuals who served as parasite-free healthy negative control. The prepared pAb succeeded in detecting CP antigens in stool and serum samples of S. mansoni-infected patients by sandwich ELISA with a sensitivity of $98.5\%$ and $98.0\%$ respectively. A positive correlation was observed between S. mansoni egg counts and both stool and serum antigen concentrations. Purified 27.5 kDa CP could be introduced as a suitable candidate antigen for early immunodiagnosis using sandwich ELISA for antigen detection.
### Key points
• Detection of cysteine protease antigens can replace parasitological examination.
• Sandwich ELISA has a higher sensitivity than microscopic examination of eggs.
• Identification of antigens is important for the goal of obtaining diagnostic tools.
## Introduction
Schistosomiasis is a parasite illness that dates back to ancient Egypt. Case detection and subsequent community treatment, morbidity evaluation, and control strategy assessment are all critical for schistosomiasis management (Peng et al. 2008). Direct parasitological methods and indirect approaches (detection of antibodies (Abs) or serum circulating antigens (Ags)) are used to diagnose schistosomiasis (Magaisa et al. 2015).
Because of its cheap operating expenses and survivability under unstable laboratory structure situations, microscopic presentation of the parasite’s eggs in feces and urine remains the most popular approach. However, this method has many disadvantages like time-consuming, significant egg count fluctuations, and low infection rates (Ross et al. 2001). Immunodiagnostic methods, on the other hand, offer significantly better sensitivity and simplicity of use (Ross et al. 2002; Maher et al. 2022), and several antibody tests have been created (van Dam et al. 2004).
Proteases were investigated as goals for immunologic or therapeutic anti-schistosomal drugs due to their critical participation in several phases of the parasite life cycle (Brindley and Pearce, 2007). The peptide hydrolysis process distinguishes five primary protease classes: serine, cysteine, aspartic, threonine, and metallo-protease (Lecaille et al. 2002). Only a few known S. mansoni proteases have been assigned function and the great majority of them are digestive proteases involved in the digestion of metabolic food or the penetration of host tissues (Delcroix et al. 2006).
Researchers have collected and purified the excretory/secretory (E/S) products, and evaluated their functional capabilities by culturing adult worms in vitro (Chappell and Dresden, 1987). Cysteine proteases (CPs) were a key component of the E/S products. CP was isolated from E/S products in our investigation, and the purified CP antigens were utilized in rabbit vaccination to produce anti-CP immunoglobulin G (IgG) polyclonal antibodies (pAbs). Sandwich ELISA was performed to diagnose S. mansoni infection using the produced IgG pAb. The results were compared to those obtained using standard parasitological examination techniques.
## Animals
Before the tests, New Zealand male white rabbits weighing around 3 kg and aged around 4 months were tested and found to be free of S. mansoni and other parasite infections. Rabbits were immunized with S. mansoni protease worm extract for the generation of pAbs, as reported by Tendler et al. [ 1991]. All experimental procedures and animal maintenance were performed according to the international care and use of laboratory animals’ guidelines, and were approved by the Institutional Animal Care and Use Committee, Egypt. The manuscript reporting adhered to the ARRIVE guidelines for the reporting of animal experiments.
## Parasites
Adult S. mansoni worms were recovered by perfusion from infected mice (6–8 weeks) after infection with 50 cercariae each. To eliminate the host blood and contaminating bacteria, phosphate-buffered saline (PBS, pH=7.4) was used to wash worms of S. mansoni several times.
## S. mansoni CP antigen preparation
Mature S. mansoni worms were incubated, for 16 h at 37°C, in Roswell Park Memorial Institute (RPMI) 1640 medium (pH 7.3) that contains glucose ($2\%$), 30mM HEPES (N-2-hydroxyethyl piperazine-N-2-ethanesulfonic acid), and 25 mg/ml gentamycin. The medium was collected after incubation and centrifuged at 15,000 g for 30 min to remove the parasite eggs. The supernatant was aliquoted and kept at −20°C as E/S products. Inhibition of the protease activity of the E/S products of adult fluke was examined using 2.7 μm l−1 aportinin (Sigma-Aldrich, Burlington, Massachusetts, USA), 1 pM pepstatin (Sigma-Aldrich, Burlington, Massachusetts, USA), and 1 μM l-trans-epoxysccinyl-leucylamid-(4-guanidino)-butane (E64) (Sigma-Aldrich, Burlington, Massachusetts, USA). 7-amino-4-methylcomarin (Z-Phe-Arg-AMC), a flurogenic substrate, was used to test the activity of CP in each fraction, where a spectrometer (LS50) was used to measure the fluorescent AMC group at exciter (370 nm) and analyzer wavelengths (440 nm). The protein content of the prepared Ag was described by the method of Bradford [1976]. Ion exchange chromatography (Sheehan and Gerald 1996), gel filtration chromatography on a Sephacryl S-200 HR column, and sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) (Thaumaturgo et al. 2002) were all used to purify CP. Indirect ELISA was utilized to test the reactivity of CP Ags (Engvall and Perlman 1971).
## Immunization of rabbits for production of pAbs
Rabbit anti-S. mansoni antibodies were produced through the immunization of two New Zealand white rabbits with the prepared S. mansoni CP Ag. Each rabbit was immunized by the following: [1] the intramuscular injection of the priming dose (1 mg S. mansoni CP prepared Ags mixed with complete Freund’s adjuvant (Sigma-Aldrich, Burlington, Massachusetts, USA) by the ratio 1:1), [2] two booster doses (0.5 mg of S. mansoni CP prepared Ags emulsified in incomplete Freund’s adjuvant). The 1st booster dose was 2 weeks later after the priming dose; and the 2nd booster dose was 1 week after the 1st booster dose. Blood samples were collected from the animals: [1] prior to the immunization process to make sure that the rabbit was clean from any infection with other parasites, [2] at each following injection (boosting) to measure the titer of produced polyclonal antibodies. After 1 month, the animals were scarified for the collection of blood samples. Serum was fractionated and stored at −20°C until used.
## Purification of rabbit anti-S. mansoni IgG pAb
IgG pAb was purified by [1] ammonium sulfate precipitation method, [2] caprylic acid purification method, and [3] DEAE anion exchange chromatographic method. After each step, SDS-PAGE was used to analyze the fractions.
## Ammonium sulfate precipitation method
One hundred grams of pure ammonium sulfate salt was dissolved in 100 ml of dist. H2O. After complete dissolving (2 days), the supernatant was separated and the pH was adjusted to 7–7.2 by drops of concentrated ammonia. Saturated ammonium sulfate solution was added dropwise to rabbit anti-S. mansoni serum to reach $50\%$ saturation, with continuous stirring followed by centrifugation for 20 min at 4°C. The supernatant was discarded and the ammonium sulfate precipitation was repeated several times on the precipitate.
## Caprylic acid purification method
Anti-S. mansoni IgG pAb, partially purified by ammonium sulfate precipitation, was diluted with 2 volumes of 60 mM Na acetate buffer, pH 4.8. This step may be replaced by dialysis versus 3 mM sodium acetate (pH 4.8). Caprylic acid ($7\%$) was added dropwise with slow magnetic stirring for 30 min at 4°C. The mixture was centrifuged at 1000 g for 30 min. Albumin and other non-IgG proteins were precipitated except IgG. The precipitate was separated while the supernatant (containing nearly pure IgG) was further purified.
## DEAE Sephadex A-50 ion exchange chromatography
Generally, protein can be separated by DEAE chromatography based on their charge. DEAE group has a +ve charge which can be neutralized by a counter ion, like chloride ions. Since antibodies have more basic isoelectric point than the majority of other serum proteins, they bind weaker to the DEAE group than albumin. Also, IgG is more basic than IgM due to the presence of more lysine and arginine than glutamate and aspartate residues. If the ionic strength was increased by increasing the NaCl salt concentration in the eluting buffer, chloride ions will compete with the bound proteins in the binding to the positive DEAE group and the proteins are eluted. IgG molecules is eluted earlier than IgM and albumin. IgG fractions separated by this method have high degree of purity. One gram of DEAE Sephadex A-50 (Pharmacia, Uppsala, Sweden) powder was swelled in 0.5M Tris buffer (200 ml, pH 7) and washed with 3 bed volumes 20mM Tris buffer, pH 7 five times. The swelled beads suspension was poured in 30×2.5cm column (Bio-Rad) using a glass rod, avoiding air bubbles. Following beads settling in the column, the surface was covered with the binding buffer followed by the determination of the approximate column binding capacity. Samples were dialyzed against the binding buffer (20mM Tris-HCl, pH 7). The outlet tubing of the column was then closed and the buffer above the beads was removed. IgG sample with suitable volume and protein content <$10\%$ of column bed capacity was applied to the column using a Pasteur pipette. The outlet tubing was opened until the sample penetrates the beads, and then closed again for 10 min for IgG binding to the beads. The outlet was opened and connected to an automated fraction collector (LKB, Dusseldoef, W Germany); 1 ml fraction was collected in each tube. The absorbance (280 nm) was measured for each fraction using a spectrophotometer (Perkin-Elmer Lambda 1A). Fractions exhibiting high absorbance at first peak were pooled together. The protein content and purity of pAbs were measured by Bio-Rad protein assay and SDS-PAGE, respectively. Testing for reactivity of pAbs was performed by indirect ELISA.
At fraction number 11, single maximum peak (2.645) represented the antigen (Fig. 1A).Fig. 1Elute profile for chromatography of E/S products on DEAE Sephadex A-50 ion exchange chromatography (A) and Sephacryl S-200 column (B)
## pAb labeling with horseradish peroxidase (HRP, periodate method) according to Tijssen and Kurstak (1984)
Five-milligram HRP was dissolved in 1.2 ml dist. H2O; then, 0.3 ml sodium periodate was added, and the mixture was incubated at room temperature for 20 min. At 4°C, the HRP mixture was dialyzed many times overnight versus 1mM sodium acetate buffer (pH 4). Five milligrams per milliliter of the pAb IgG was mixed with 0.02M carbonate buffer (pH 9.6). Half milliliter of pAb solution was mixed HRP solution, followed by 2 h of incubation at room temperature. One hundred microliters of sodium borohydride was mixed with the solution, followed by incubation at 4°C for 2 h. The HRP-conjugated pAb was dialyzed versus PBS (0.01M, pH 7.2).
## Samples’ collection
The study involved 200 patients infected with S. mansoni from highly endemic areas and outpatients’ clinic and hospitals, where they were diagnosed by finding characteristic eggs in the collected stool samples, in addition to 100 patients that were infected with other parasites (Fasciola, hookworm, hydatid, and trichostrongyloids) and 100 healthy negative control individuals. Samples (serum and stool) were collected and kept at −80°C until used. The study was conducted in accordance with the World Medical Association Declaration of Helsinki for human subjects and all participants gave an informed written consent.
## Parasitological examinations (stool examination)
Microscopic examination of stool samples was performed by merthiolate-iodine-formaldehyde concentration technique (MIFC) according to Blagg et al. [ 1955] and egg count using Kato-Katz technique (Engels et al. 1997).
## Standardization of sandwich ELISA
The IgG pAb maximum concentration as a coating and peroxidase conjugated layer was 10 and $\frac{1}{20}$ μg/ml, respectively. The assay was performed according to Qiu et al. [ 2000], Hegazy et al. [ 2015], and Kamel et al. [ 2013].
## Optimization of working dilution of stool samples
One part of sample was mixed with two parts of dist. H2O in a 15-ml polypropylene tube, followed by well stirring and centrifugation for 10 min to prepare aqueous elutes from each sample. Serial dilution of diluted stool (the supernatant) was assessed for Ag detection by sandwich ELISA to obtain maximum values with minimal background reaction. Five dilutions (1:10, 1:20, 1:30, 1:40 and 1:50 in $2.5\%$ FCS in PBS/T) of diluted stool (diluted 1:3 before) were used.
## Sandwich ELISA
Plate was coated with 100μl/well of purified IgG pAb at a concentration of 10 μg/ml followed by incubation at room temperature overnight. Plate was washed three times with washing buffer (0.1 M PBS, $0.05\%$ Tween (v/v), pH 7.2). The plate was blocked by adding 200μl/well of blocking buffer ($2.5\%$ FCS in PBS/T); followed by incubation for 2 h at 37°C. After plate was washed three times, samples (100 μl/well) were added to the wells followed by 2 h of incubation 37°C, then washing. One hundred microliters/well of HRP-conjugated pAb IgG (diluted $\frac{1}{20}$ for IgG) was added to the plates. The plates were incubated for 1 h at room temperature, followed by five times washing. One hundred microliters of substrate was added to each well, and the plate was incubated for half an hour in darkness at room temperature, followed by the addition of 50 μl/well H2SO4 to stop the reaction (Farid et al. 2022a). The absorbance was estimated by ELISA reader at 492nm.
Note: Substrate was prepared by mixing OPD, dissolved in 25ml of 0.05M phosphate-citrate buffer (pH 5), with 5 μl H2O2.
## Validity of results
Diagnostic sensitivity of a method refers to the frequency of positive test results detected by a particular method in individuals with a particular disease. Therefore, the higher percentage of the test sensitivity, the higher the number of positive results in diseased individuals.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{Sensitivity}\ \left(\%\right)=\left[\textrm{true}+\textrm{ve}\ \textrm{cases}/\left(\textrm{true}+\textrm{ve}\ \textrm{cases}+\textrm{false}\hbox{--} \textrm{ve}\ \textrm{cases}\right)\right]\times 100$$\end{document}Sensitivity%=true+vecases/true+vecases+false--vecases×100 Diagnostic specificity of a method refers to the frequency of negative test results detected by a particular method in individuals without the particular disease. Therefore, the higher percentage of the test specificity, the higher the number of negative cases in healthy individuals.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{Specificity}\ \left(\%\right)=\left[\textrm{true}-\textrm{ve}\ \textrm{cases}/\left(\textrm{true}-\textrm{ve}\ \textrm{cases}+\textrm{false}+\textrm{ve}\ \textrm{cases}\right)\right]\times 100$$\end{document}Specificity%=true-vecases/true-vecases+false+vecases×100 The mean percentages of positive results that were true positive and negative results that were true negative were estimated by PPV (positive predictive value) and (NPV) negative predictive value, respectively.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{PPV}\ \left(\%\right)=\left[\textrm{true}+\textrm{ve}\ \textrm{cases}/\left(\textrm{true}+\textrm{ve}\ \textrm{cases}+\textrm{false}+\textrm{ve}\ \textrm{cases}\right)\right]\times 100$$\end{document}PPV%=true+vecases/true+vecases+false+vecases×100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{NPV}\ \left(\%\right)=\left[\textrm{true}-\textrm{ve}\ \textrm{cases}/\left(\textrm{true}-\textrm{ve}\ \textrm{cases}+\textrm{false}-\textrm{ve}\ \textrm{cases}\right)\right]\times 100$$\end{document}NPV%=true-vecases/true-vecases+false-vecases×100
## Statistical analysis
The data were presented as mean± standard deviation (SD). Correlation between number of S. mansoni eggs in stool sample and the optical density (OD) of ELISA technique was estimated by correlation coefficient (r) according to Snedecor and Cochran [1981]. The data were considered significant if $P \leq 0.05.$
## Sephacryl S-200 column chromatography
The resulted E/S antigen fraction number 11 was further processed by Sephacryl S-200 column chromatography. The obtained single peak, at fraction number 7 with OD280 value 1.910, represents the column elution volume fractions which comprised cysteine proteases (Fig. 1B). The fractions of the eluted protein, from the previous purification techniques, were evaluated by $12.5\%$ SDS-PAGE and revealed just single band at 27.5 kDa that represented cysteine proteases (Fig. 2).Fig. 2SDS-PAGE of target E/S Ags eluted from affinity chromatography columns (stained with coomassie blue). Lane 1: Low molecular weight standard. Lane 2: E/S products. Lane 3: Target Ag eluted from DEAE Sephadex A-50 ion exchange chromatography. Lane 4: Target Ag eluted from Sephacryl S-200 gel filtration chromatography
## Measurement of total protein content of S. mansoni CP Ags
Total protein was 8 mg/ml in the collected E/S product from the adult worms, and 4.6 and 2.3 mg/ml after purification with DEAE Sephadex A-50 ion exchange and Sephacryl S-200 chromatography, respectively.
## Characterization of S. mansoni Ag
S. mansoni-infected patients’ sera samples showed a vigorous response against S. mansoni CP antigen with mean OD492 equivalent to 1.309. No cross reactions were detected with patients’ serum infected by Fasciola, hydatid, and hookworm (Table 1).Table 1Reactivity of purified target CP Ag by indirect ELISAOD readings at 492 nm as mean (SD)Serum samples1.309 (0.342)S. mansoni0.264 (0.201)Fasciola0.106 (0.094)Hydatid0.182 (0.082)Hook wormOD, optical density; SD, standard deviation
## Production of pAbs
Before the injection of each immunizing dose, blood samples from New Zealand white rabbit were collected. Indirect ELISA was used for testing the presence of specific anti-S. mansoni antibodies. An elevating antibody level was recorded started 1 week after the first booster dose. Three days after the second booster dose, immune serum samples gave strong high titers against S. mansoni CP with OD492 of 2.8 ($\frac{1}{250}$ dilution) (Fig. 3).Fig. 3Reactivity of rabbit anti-S. mansoni antibodies (diluted $\frac{1}{250}$) against S. mansoni CP by indirect ELISA
## Polyclonal antibodies purification
Crude rabbit’s serum had a total protein content of 12.5 mg /ml. The produced purified anti-S. mansoni IgG pAb was measured from the evaluation of the protein content after each purification stage. The protein content was 5.8 mg/ml using the $50\%$ ammonium sulfate precipitation form, while the content decreased to 3.1 mg/ml after a $7\%$ caprylic acid precipitation procedure. Eventually, the extremely pure anti-S. mansoni IgG pAb after chromatography was 2.4 mg/ml. The eluted IgG was expressed at fraction number 10 by a single OD peak (2.88). Analysis by 12.5 % SDS-PAGE revealed that precipitated proteins appeared as several lines. The pure IgG pAb was expressed at 53 and 31 kDa that represent the heavy (H) and light (L) chain pairs. The pAb showed up free of other proteins (Fig. 4).Fig. $412.5\%$ SDS-PAGE of anti-S. mansoni IgG pAb before and after purification (stained with coomassie blue). Lane 1: Molecular weight of standard protein. Lane 2: Precipitated proteins after $50\%$ ammonium sulfate treatment. Lane 3: Nearly pure IgG pAb after $7\%$ caprylic acid treatment. Lane 4: Purified IgG pAb after ion exchange chromatography
## Specificity of purified antibodies
The produced anti-S. mansoni IgG pAb gave high reactivity to S. mansoni CP. The OD492 for S. mansoni were 2.91 compared to 0.432, 0.210, 0.426, and 0.271 for other parasite (Table 2).Table 2Reactivity of rabbit anti-S. mansoni antibodies against many parasitic Ags by indirect ELISA (OD reading= 492 nm)OD readings at 492 nmParasitic Ag2.84 (0.45)S. mansoni0.462 (0.13)Fasciola0.310 (0.22)Hookworm0.406 (0.34)Hydatid0.281 (0.24)TrichostrongyloidsOD, optical density
## Parasitology investigation
According to stool analysis, 185 patients from 200 patients were positive with S. mansoni ova. Therefore, the sensitivity of parasitological diagnosis technique was $92.5\%$. According to the infection intensity, patients were divided into two subgroups: Light infection that included 90 patients with number of ova/three slides of stool ranging from 6 to 25 ova with mean of 16±9.8. Heavy infection that included 95 patients with number of ova/three slides of stool more than 25 ova with mean of 42±17.91.
## Measurement of S. mansoni CP Ags in stool samples (coproantigens)
The positivity cutoff value equaled 0.486. As shown in Table 3, the OD492 value of S. mansoni-infected patients’ group (1.86±0.317) was higher than that of the uninfected control group (0.290±0.098), and that of the other parasite-infected patients’ groups (Table 3). Three patients out of 200 S. mansoni-infected patients gave false negative results. Those patients were from the highly infected subgroup where the ova number was 9±0.201.Table 3Detection of coproantigens in stool samples of infected humanGroup (no of cases)Positive casesNegative casesNo. X (SD)No. X (SD)Healthy control ($$n = 100$$)--1000.290 (0.098)S. mansoni ($$n = 200$$)1971.86 (0.317)3*0.406 (0.070)Fasciola ($$n = 25$$)1**0.509 (0.069)240.332 (0.109)Hookworm ($$n = 25$$)--250.297 (0.185)Hydatid ($$n = 25$$)1**0.594 (0.121)240.317 (0.185)Trichostrongyloid ($$n = 25$$)--250.301 (0.128)X, mean; SD, standard deviation. * False negative result. ** False positive result So the sensitivity of the assay was $98.5\%$. All the OD492 values of 100 uninfected controls were lower than the cutoff value, while the OD492 values of 2 patients from 100 other parasite-infected patients were higher than the cutoff value giving $99.0\%$ specificity. The PPV was $98.9\%$ while the NPV was $98.5\%$ (Table 5).
## Measurement of S. mansoni CP Ags in serum samples
The positivity cutoff value was 0.4. The OD492 of S. mansoni-infected group (1.53±0.308) were more than those of the uninfected control group (0.198±0.101) and the other parasite-infected groups (Table 4). Four patients out of 200 Schistosoma-infected patients showed false -ve result and the sensitivity was $98.0\%$. The OD492 of control individuals were lower than the cutoff value while three patients of 100 other parasite-infected patients showed OD492 value higher than the cutoff value giving a specificity of $98.5\%$ (Table 5).Table 4Detection of circulating S. mansoni Ag in serum of human subjects infected with S. mansoni or other parasite in comparison to healthy controlGroup (no of patients)Positive casesNegative casesNo. X (SD)No. X (SD)Healthy control ($$n = 100$$)--1000.198 (0.101)S. mansoni ($$n = 200$$)1961.53 (0.308)4*0.300 (0.081)Fasciola ($$n = 25$$)2**0.641 (0.243)230.244 (0.154)Hookworm ($$n = 25$$)--250.201 (0.112)Hydatid ($$n = 25$$)1**0.436 (0.209)240.195 (0.200)Trichostrongyloid ($$n = 25$$)--250.209 (0.094)X, mean; SD, standard deviation. * False negative result. ** False positive resultTable 5Sensitivity, specificity, PPV, and NPV of sandwich ELISA used for detection of S. mansoni Ags in stool and serum samplesSamplesSensitivitySpecificityPPVNPVHumanStool$98.5\%$$99.0\%$$98.9\%$$98.5\%$Serum$98.0\%$$98.5\%$$98.4\%$$98.0\%$ In S. mansoni-infected patients, there was a strong positive correlation between the number of ova per gram stool and coproantigen level (Fig. 5A; $r = 0.417$ and $$P \leq 0.01$$), or Ag level in serum (Fig. 5B; $r = 0.687$ and $$P \leq 0.001$$).Fig. 5A Correlation between ova count/gm stool and S. mansoni CP Ag level in stool (coproantigen) of S. mansoni-infected patients ($r = 0.417$; $$P \leq 0.01$$**). B Correlation between ova count/gm stool and S. mansoni Ag level in serum of S. mansoni-infected patients ($r = 0.687$; $$P \leq 0.001$$***)
## Discussion
Schistosomiasis is a major parasite illness in many regions of the tropics, second only to malaria (van der Werf et al. 2003; McManus et al. 2018). Agricultural practices transmit schistosomiasis, especially when there is inadequate sanitation and continuous contact with water (McManus and Loukas 2008). Schistosoma spp. continually excrete and secrete chemicals (which are known as E/S) into their surroundings during skin penetration, either to facilitate passage or as part of their metabolism. Different kinds of proteins and lipids have been found in E/S products (Liu et al. 2009). Proteomic study of the tegument of adult worms, E/S products, and egg shells of S. mansoni produced several thousand proteins, according to Braschi et al. [ 2006] and Liu et al. [ 2006]. Proteases are one of the main components of excretory/secretory products, and they have a significant importance in all living forms (Barrett et al. 2004; McKerrow et al. 2006).
By culturing adult worms in vitro, E/S product has been collected to be analyzed, where proteases were found to constitute a high percent. Proteases play an important role in all living organisms (Barrett et al. 2004). Hatching, excystment, tissue-cell invasion, nutrition acquisition, and immune evasion are all processes that require the use of proteases (McKerrow et al. 2006). Proteases function at the host/parasite interface for trematode worms that cause diseases of veterinary and medical consequence, aiding movement, digesting protein molecules, and likely immune evasion (Dalton et al. 2006). Many parasites rely on CPs for their metabolism, and CP inhibitors have been proven to eliminate protozoan parasites in both culture (Rosenthal, 2004; Verma et al. 2016) and animal disease models (Rosenthal, 2004; Farid et al. 2013).
Schistosomes have a variety of CPs that help in nutrition, reproduction, and proteins’ turnover (Caffrey et al. 2000). CP inhibitors (fluoromethyl ketone) have been demonstrated to reduce worm and egg loads in S. mansoni-infected mice (Caffrey et al. 2004).
In the present study, adult S. mansoni worms were cultured in RPMI 1640 medium, and E/S products had been collected in order to purify and analyze CPs. CPs were purified from excretory/secretory products by ion exchange and gel filtration chromatography and in which CP appeared as one band at 27.5 kDa by SDS-PAGE. Excretory/secretory products generated by worms have long been thought to be a significant source of antigens capable of eliciting an immunological response in their hosts (Wasilewski et al. 1996). Some excretory/secretory products have been linked to specific secretory and excretory organs. *Molecules* generated from the helminths tegument, on the other hand, contribute significantly to the contents of the parasite’s E/S products in vitro, and are likely also discharged in vivo (Bobardt et al. 2020). In vitro and in vivo, surface components of S. mansoni schistosomules and mature worms were also shown to be shed (Ryan et al. 2020). Through their possible triggering effect on the immune response of susceptible hosts, the active release of antigens from the surface of helminths should have a potentially major impact on the experimental therapeutics of the helminth/host connection (Cutts and Wilson 1997).
In this study, anti-S. mansoni IgG pAb was prepared by immunization of rabbit with S. mansoni E/S Ags. Each rabbit recieved 1 mg of S. mansoni proteinase worm extract in the first dose and half milligram for the 2nd and 3rd booster dose injection. Two weeks following the priming dose, the first boosting dose was given, where according to Tendler et al. [ 1991], the appropriate boosting doses were administered at weekly intervals. The purification processes used in this work were acceptable; 3 purification stages were used for IgG pAb: ammonium sulfate precipitation, $7\%$ caprylic acid precipitation, and lastly ion exchange chromatography. The quality of IgG pAb was determined using a $12\%$ SDS-PAGE; the heavy and light chain bands of pure pAb IgG were 53 and 31 kDa, respectively, indicating that the purified Ab seems to be devoid of unrelated proteins. By these procedures, the output of pAb as protein content was 2.3 mg/ml from an initial protein content of 12.5 mg/ml in the excretory/secretory products. Compared to the amount of pure immunoglobulin obtained from any biological sample using similar separation techniques, the yield was reasonable (Ayón-Núñez et al. 2018; Bride et al. 1995). Then, using an indirect ELISA against E/S products, the response of the pure pAb towards S. mansoni CP and other parasites’ Ag (Fasciola, hookworm, hydatid, and trichostrongyloids) was assessed using the Engvall and Perlmann [1971] method with some modifications of Voller et al. [ 1974]. It is worth noting that ELISA has been regarded as a reliable assay for detecting antibodies to fluke antigens in rabbits. As an immunodiagnostic approach for numerous parasite illnesses and for quantitative evaluation of many immunological markers, the ELISA has received the greatest attention in many studies (Abdel-Monaem et al. 2015; Madbouly et al. 2021; Farid et al., 2022b).
In this study, the produced anti-S mansoni IgG pAb was utilized for the detection of CP Ags in stool (coproantigens) and sera samples of infected patients by sandwich ELISA. The standardization of different reagents used in sandwich ELISA was undertaken by testing different concentrations of one reagent in the presence of fixed excess amount of the other reagents. The highest concentration of pure IgG PAb as a coating layer was 10 μg/ml, whereas the highest dilution of IgG PAb as a peroxidase-conjugated layer was $\frac{1}{20}$ μg/ml. Moreover, the standard curve was constructed for IgG pAb against different concentrations of E/S CP Ag. The curve was linear based on the concentration of Ag. The lowest detecting concentration for E/S was 2.3 ng/ml.
Also, the study compared the ordinary parasitological examination methods with sandwich ELISA in the diagnosis of schistosomiasis. The study was conducted on 200 S. mansoni-infected patients, 100 patients infected by other parasites, and 100 healthy volunteers. According to parasitological examination and the egg load in stool (counted by Kato technique), 185 patients from 200 patients were positive with S. mansoni ova. Samples were distributed into two main groups: the first group represented light infection (< 40 ova/g stool) where the mean egg counts were 16 ± 9.8 in infected human; the second group represented heavy infection (> 40 ova/g stool) and the mean egg counts were 42 ± 17.91 ova/gm stool for infected human. The sensitivity of that test was $92.5\%$.
The rabbit IgG raised against purified 27.5 kDa S. mansoni CP Ag successfully detected CP Ags in stool samples of patients with a sensitivity of $98.5\%$ and specificity of $99.0\%$, respectively. Although all of the negative control stool samples showed a negative reaction, one patient with Fasciola and another patient with hydatid infection showed cross reactivity. Three patients infected with S. mansoni had false negative results. The test had a $98.9\%$ PPV and a $98.5\%$ NPV, respectively. To detect circulating S. mansoni antigens, serum samples were also used in a sandwich ELISA experiment. Although none of the negative control sera samples showed a positive response, two patients with Fasciola and another patient with hydatid infection showed cross reactivity. The test specificity was $98.5\%$. Four cases of S. mansoni-infected patients, on the other hand, yielded a false negative result; the test’s sensitivity was $98.0\%$. The test has a PPV of $98.4\%$ and a NPV of $98.0\%$, respectively.
Both stool and serum antigen concentrations were found to have a positive connection with S. mansoni egg load. Because the density of flukes in the host affects the stool egg count, it is reasonable to assume that stool antigen levels in S. mansoni-infected patients were directly proportional to the number of adult worms. It is possible that the absence of parasite antigen in certain patients’ stools and sera was due to a low parasite burden, resulting in undetectable concentrations of antigen in the stool and sera. A similar link has also been observed in S. mansoni-infected patients in several studies (Grenfell et al. 2013).
Controlling schistosomiasis requires accurate and timely diagnosis, which includes early diagnosis and subsequent population therapy, measurement of morbidity, and assessment of control programs (Peng et al. 2008). Thus, it can be concluded that purified 27.5 kDa CP obtained from S. mansoni E/S products can be used as an appropriate applicant antigen for immunodiagnosis. Moreover, the purified IgG pAb successfully detects CP Ags in stool and sera samples of S. mansoni-infected patients with light infection. This study reported that, sandwich ELISA has a higher sensitivity ($98.5\%$ in stool samples and $98.0\%$ in serum samples) than microscopic examination of eggs in stool ($92.5\%$).
Based on our findings, we infer that CP antigen detection can either substitute parasitological testing or be utilized as a supplemental screening tool in the situation of weak infection. Moreover, CP antigen determination is the preferred method for determining treatment and diagnosing active infection in endemic areas. Moreover, identification of a S. mansoni Ag that elicits an intense humoral response in human cases is important for the goal of obtaining better diagnostic tool. E/S Ag as shown in the present study was a sensitive and specific Ag in the different immunodiagnostic techniques used even in its crude form which could be prepared by an easy and simple method and in adequate amount.
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|
---
title: No strong association among epigenetic modifications by DNA methylation, telomere
length, and physical fitness in biological aging
authors:
- Yasuhiro Seki
- Dora Aczel
- Ferenc Torma
- Matyas Jokai
- Anita Boros
- Katsuhiko Suzuki
- Mitsuru Higuchi
- Kumpei Tanisawa
- Istvan Boldogh
- Steve Horvath
- Zsolt Radak
journal: Biogerontology
year: 2023
pmcid: PMC10006047
doi: 10.1007/s10522-022-10011-0
license: CC BY 4.0
---
# No strong association among epigenetic modifications by DNA methylation, telomere length, and physical fitness in biological aging
## Abstract
Cellular senescence is greatly accelerated by telomere shortening, and the steps forward in human aging are strongly influenced by environmental and lifestyle factors, whether DNA methylation (DNAm) is affected by exercise training, remains unclear. In the present study, we investigated the relationships between physiological functions, maximal oxygen uptake (VO2max), vertical jump, working memory, telomere length (TL) assessed by RT-PCR, DNA methylation-based estimation of TL (DNAmTL), and DNA methylation-based biomarkers of aging of master rowers ($$n = 146$$) and sedentary subjects ($$n = 95$$), aged between 37 and 85 years. It was found that the TL inversely correlated with chronological age. We could not detect an association between telomere length and VO2max, vertical jump, and working memory by RT-PCR method, while these physiological test results showed a correlation with DNAmTL. DNAmGrimAge and DNAmPhenoAge acceleration were inversely associated with telomere length assessed by both methods. It appears that there are no strong beneficial effects of exercise or physiological fitness on telomere shortening, however, the degree of DNA methylation is associated with telomere length.
## Introduction
Healthy aging with relatively high physiological function is the goal of human society. To achieve this, it would be important to understand the aging process and then develop interventions that help to reach the goals. One of the mechanisms, known to regulate the aging process is based on the limiting factors of cell proliferation, namely on telomeres (Greider 1996). Telomeres are protective caps on the ends of chromosomes with repeated deoxyribonucleic acid sequences rich in TTAGGG, between 3- and 20 kb- long in humans (Hande 2004). Telomeres are complexes comprising not only (T2AG3)n DNA repeats, but also protecting loops, including proteins and RNA. Attrition disturbs these loops, which triggers DNA damage response and then cellular senescence and inflammation (Greider 1996). It also has to be mentioned, that importance of the telomere to evaluate the progress of aging is under debate (Simons 2015).
Oxidative stress causes accelerated telomere shortening primarily through the oxidation of guanine due to its lowest oxidation potential among nucleic acid bases, especially in telomere quadruplexes (Radak and Boldogh 2010). After a certain number of cell divisions, which results in loss of telomere length, dividing cells cannot replicate anymore reaching cellular senescent (Linskens et al. 1995). Although the enzyme of telomerase can add de novo base pairs to telomere (Greider and Blackburn 1989), the age-related shortening is well documented (Campisi 2005; Bize et al. 2009). Indeed, accumulating evidence suggests a strong link between telomere length and aging and age-associated diseases (Morin 1997; Sikora 2013; Fyhrquist and Saijonmaa 2012; Sahin and DePinho 2012).
It has been shown that telomere length (TL) is dependent on various lifestyle factors. It has been shown that in preschool children obesity was linked to reduced levels of docosahexaenoic acid, an increased arachidonic acid/ docosahexaenoic acid ratio, and shortened telomere in leukocytes (Liu et al. 2021). Short-term administration of nicotinamide mononucleotide resulted in significant elongation of TL in peripheral blood mononuclear cells of C57BL/6 mice (Niu et al. 2021). Studies suggest that physical exercise also has beneficial effects on TL (Lee et al. 2013; Manoy et al. 2020; Loprinzi et al 2015). Even a short-term exercise program for 12 weeks, with low frequency, moderate intensity, and explosive-type resistance training could have beneficial effects on telomeres. Indeed, above mentioned explosive resistance training lessened telomere shortening and correlated with the amelioration of redox homeostasis (Dimauro et al. 2016).
Because aging is strongly influenced by lifestyle Horvath and Hannum developed DNA methylation-based epigenetic aging clocks, which more precisely reflect aging than chronological age (Horvath 2015; Hannum et al. 2013). Moreover, in a huge cohort ($$n = 5713$$) it has been shown that there is an interaction between genome-wide methylation, TL, and epigenetic aging (Lee et al. 2019). Based on previous studies, we hypothesize that the level of physical fitness would affect epigenetic aging clocks and TL of whole blood samples in aged individuals. Moreover, we aimed to compare the interactions between physiological test results and TL assessed by the RT-PCR method and DNA methylation-based estimation (Pearce et al. 2022).
## Subjects
Subjects were volunteers, who participated in the 2019 Masters World Rowing Championships in Venice, Hungary, and aged-matched sedentary individuals. The master rowers were recruited at the championships using pamphlets, while sedentary subjects were by calls published in a newspaper in Budapest. The investigation was carried out voluntarily with the ethics license provided by the Hungarian Scientific and Research Ethical Committee 25167-$\frac{6}{2019}$/EUIG. Our cohort consisted of a total of 241 people, 146 masters (mean age 59 ± 9.7 years) and 95 sedentary (mean age 62 ± 12.3 years) (Table 1).Table 1Characteristics and results of the subjectsn = 241Master rowersSedentary subjectsNumber of patients14694Age (years), Mean ± SD59 ± 9.762 ± 12.3Male7830Age (years), Mean ± SD60.0 ± 10.660.5 ± 14Female6865Age (years), Mean ± SD57.1 ± 8.463.4 ± 11.4TL, Mean ± SD8.57 ± 0.368.56 ± 0.34DNAmTL, Mean ± SD6.85 ± 0.276.79 ± 0.29BMI (kg/m2), Mean ± SD24.48 ± 2.827.01 ± 4.3Vertical jump (cm), Mean ± SD30.63 ± 7.224.10 ± 8.3Working memory, Mean ± SD6.35 ± 1.55.91 ± 1.1VO2 max (ml/kg/min), Mean ± SD44.70 ± 9.934.03 ± 7.9DNAmPhenoAge, Mean ± SD46.96 ± 9.351.31 ± 12.0AgeAccelPheno, Mean − 0.430.63DNAmGrimAge, Mean ± SD58.53 ± 8.562.09 ± 9.9AgeAccelGrim, Mean − 0.250.33
## Physiological tests
Body mass and height were measured and the body mass index was described by the body composition monitor BF214 (Omron, Japan). Relative maximum hand gripping force a measure of age-associated decline in general muscle strength (Eika et al. 2019) was assessed by the CAMRY EH101 dynamometer. Relative maximal oxygen uptake is one of the best markers of viability and a higher level of VO2max is associated with decreased levels of a wide range of diseases (Hawkins and Wiswell 2003; Carnethon et al. 2005). We used the Chester step test to appraise the level of VO2max (Izquierdo et al. 2019). A Digit span test was applied to assess the working memory (Martinez-Diaz et al. 2020), where larger values indicate better verbal short-term memory.
## Determination of hematologic and telomere length
Determination of hematologic and biochemical variables, blood samples were collected before the subjects performed the VO2max evaluation test, and were stored in evacuated tubes containing EDTA as an anticoagulant. Blood samples were centrifuged and stored at − 80 °C degrees. The biochemical tests were carried out in the Clinical Analysis Laboratory of Semmelweis University, Budapest.
## DNA isolation
DNA was isolated from the K2-EDTA anticoagulated blood samples using a DNA isolation kit (Pure LinkTM Genomic DNA Mini kit, Thermo Fisher, Carlsbad, CA, USA), according to the manufacturer’s instructions.
## Measurement of telomere length
The average relative telomere length of genomic DNA was determined from whole blood samples using Cawthon’s PCR-based method (Wan et al. 2019) with the commercially available PCR kit (ScienCell Research Laboratories inc., San Diego CA Catalog no. # 8908). During this reaction, telomere-specific primers recognize and amplify telomeric sequences. For each DNA sample, two consecutive reactions were performed: the first for amplification of a single-copy reference (SCR) gene and the second for the telomeric sequence. The former recognizes and amplifies a 100 bp region on human chromosome 17 and serves as a reference for calculating the telomere length of the target samples. The PCR reactions were performed in a final volume of 20 μl. We used 5 ng reference/genomic DNA sample (final concentration = 0.625 ng/μl), 2 μl telomere primer, and 10 μl 2XMaster Mix, PCR conditions were as follows: first 95 °C for 10 min, followed by 32 cycles of 95 °C for the 20 s, 52 °C for 20 s, and 72 °C for 45 s. All samples were tested in triplicate.
In addition, the telomere length was also evaluated by Horvath’s software which estimates the telomere length from methylation (Pearce et al. 2022).
## Measurement of DNA methylation
Epigenome-wide DNA methylation 850 K was measured with the Infinium MethylationEPIC BeadChip (Illumina Inc., San Diego, CA) according to the manufacturer’s protocol. Briefly, 500 ng of genomic DNA was bisulfite converted using the EZ-96 DNA Methylation MagPrep Kit (Zymo Research, Irvine, CA, USA) with the KingFisher Flex robot (Thermo Fisher Scientific, Breda, Netherlands). The samples were plated in randomized order. The bisulfite conversion was performed according to the manufacturer’s protocol with the following modifications: For binding of the DNA 15 µl MagBinding Beads were used. The conversion reagent incubation was done according to the following cycle protocol: 16 cycles of 95 °C for 30 s followed by 50 °C for 1 h. After the cycle protocol, the DNA was incubated for ten minutes at 4 °C. Next, DNA samples were hybridized on the Infinium MethylationEPIC BeadChip (Illumina Inc., San Diego, CA) according to the manufacturer’s protocol with the modification that 8 µl bisulfite-treated DNA was used as starting material. Quality Control of the DNA methylation data was performed using, Meffil and Ewastools packages with R version 4.0.0 (Min et al. 2018, Murat et al. 2020).
Acceleration for PhenoAge and GrimAge is defined as the raw residuals i.e. the difference between the observed and the expected value when the methylation-based age estimator regressed on chronological age. Indeed, methylation-based clocks are better at predicting health outcomes if blood cell composition is incorporated (Chen et al. 2016). Both PhenoAge and GrimAge are known to be associated with changes in leukocyte distribution like the decrease in naive CD8 cells, increase in granulocytes, etc. However, after blood cell count adjustment the decrease in p-value is minimal for GrimAgeAccel (Lu et al. 2019) as well as for PhenoAgeAccel (Levine et al. 2018).
Samples that failed technical controls, including extension, hybridization, and bisulfite conversion, according to the criteria set by Illumina, were excluded. Samples with a call rate < $96\%$ or at least $4\%$ of undetected probes were also excluded. Probes with a detection p-value > 0.01 in at least $10\%$ of the samples were set as undetected. Probes with a bead number < 3 in at least $10\%$ of the samples were excluded. We used the "noob" normalization method in R to quantify the methylation level (Triche et al. 2013). The details on the processing of DNAm data and the calculation of the measures of aging, or pace of aging, were calculated using Horvath’s online age calculator (https://dnamage.genetics.ucla.edu/).
## Statistics
The results were subjected to statistical tests. Statistical analysis was done by using Statistica 13 software (TIBCO). After testing for normal distribution, the relevant parametric and non-parametric test methods were applied. The differences between groups were examined by multiway ANOVA. The interdependence of the individual variables was analyzed using multivariate regression analysis.
## Results
We have measured or estimated the TL with two different methods one based on RT-PCR measurements while the other was calculated methylation-based estimation of TL. There were no significant differences between the telomere length of masters and sedentary, measured or estimated (TL: masters mean 8.57 ± 0.36, sedentary mean 8.56 ± 0.34; DNAmTL masters mean 6.85 ± 0.27, sedentary mean 6.79 ± 0.29). The results from the RT-PCR measurements revealed that the TL showed a weak, but significant negative relationship with the chronological aging of the subjects (r = − 0.23; $$p \leq 0.0003$$) (Fig. 1A). However, we could not detect a significant correlation between age and TL in masters (masters: r = − 0.03; $$p \leq 0.7142$$ vs sedentary: r = − 0.35; $$p \leq 0.0004$$) (Fig. 1B, C).Fig. 1The association between telomere length and chronological age. Telomere length measured by RT-PCR negatively correlated with chronological age in all cases (Panel A $$n = 241$$) and in sedentary (Panel C $$n = 95$$), but not in masters (Panel B $$n = 146$$) As far as the DNAm-based estimation was concerned significant relationships were found in the whole cohort, both masters and controls (Fig. 2A–C). The evaluation of the relationship between DNAmTL and TL revealed a significant relationship ($r = 0.366$, $p \leq 0.0001$).Fig. 2The association between estimated telomere length and chronological age. Estimated telomere length negatively correlated with chronological age in all cases (Panel A $$n = 241$$) in the masters (Panel B $$n = 146$$) and in sedentary (Panel C $$n = 95$$) According to results measured by RT-PCR, there is no relationship between BMI, (r = − 0.02; $$p \leq 0.7450$$, Fig. 3A; masters: r = − 0.0356; $$p \leq 0.6698$$, sedentary: $r = 0.099$; $$p \leq 0.3419$$), vertical jump results ($r = 0.12$; $$p \leq 0.0689$$, Fig. 3B; masters: $r = 0.15$; $$p \leq 0.0705$$, sedentary: $r = 0.086$; $$p \leq 0.4097$$), and the scores of working memory (r = − 0.032; $$p \leq 0.622$$, Fig. 3C; masters: r = − 0.097; $$p \leq 0.2450$$, sedentary: r = − 0.097; $$p \leq 0.2450$$). Despite this, the DNAm-based estimation of TL showed similar results with BMI (r = − 0.12; $$p \leq 0.0668$$, Fig. 3D; masters: r = − 0.09; $$p \leq 0.2869$$, sedentary: r = − 0.096; $$p \leq 0.3595$$), the vertical jump results correlated significantly with DNAmTL ($r = 0.29$; $p \leq 0.0001$, Fig. 3E; masters: $r = 0.21$; $$p \leq 0.0106$$, sedentary: $r = 0.336$; $$p \leq 0.0010$$). Working memory also showed a positive correlation with DNAmTL, but only in controls ($r = 0.085$; $$p \leq 0.1956$$, Fig. 3F; masters: r = − 0.042; $$p \leq 0.6193$$, sedentary: $r = 0.264$; $$p \leq 0.0104$$).Fig. 3The relationship between physiological test results and telomere length. A significant relationship was not present between RT-PCR-based telomere length, body mass index (BMI, Panel A), maximal vertical jump height (Jump max, Panel B), and working memory (Working memory score, Panel D) $$n = 241$.$ Panel E, ($$n = 241$$), F ($$n = 95$$) show DNAm-based results The maximal oxygen uptake (VO2max) which was calculated from the Chester step test, was higher in athletes (masters 44.7 ml/kg/min vs. sedentary: 33.7 ml/kg/min). The results showed no relationship with RT-PCR-based TL (r = − 0.039; $$p \leq 0.5484$$ Fig. 4A; masters: r = − 0.1; $$p \leq 0.2286$$, sedentary: $r = 0.12$; $$p \leq 0.2488$$). On the other hand, the DNAm-based TL estimate showed a significant correlation with VO2max in all subject cases ($r = 0.17$; $$p \leq 0.0094$$, Fig. 4B) and in sedentary ($r = 0.29$; $$p \leq 0.0067$$, Fig. 4C), but not in masters ($r = 0.08$; $$p \leq 0.3414$$, Fig. 4D).Fig. 4The relationship between cardiovascular fitness (VO2max) and telomere length. The maximal oxygen uptake was estimated from the step test results and a significant relationship was neither found in the total number of subjects when assessed by RT-PCR gained results (Panel A $$n = 241$$). DNAm-based TL calculation, on the other hand, showed significant relationships in all cases (Panel B $$n = 241$$) and in sedentary (Panel D $$n = 95$$), but not in masters (Panel C $$n = 146$$)
Since telomere length is often to be associated with morbidity (Cheng et al. 2021) and lifestyle-related factors like tobacco smoking (Astuti et al. 2017), we made a supplementary analysis (multiple regression) with suspected confounders variables to check for possible biases. In the multivariable models, boolean variables for gender, smoking, high blood pressure, autoimmune disease, asthma, diabetes, and cardiovascular disease were included. After adding the mentioned variables into the model only DNAmTL showed a significant correlation with exercise physiology variables (VO2max: partial $r = 0.267$, $$p \leq 4.8$$E-6, JumpMax: partial $r = 0.44$, $$p \leq 2.89$$E-12).
When the possible association between RT-PCR measurement-originated data and DNA methylation-based epigenetic aging was examined, it turned out that in all subjects, a case both DNAmPhenoAge and DNAmGrimAge related to TL (r = − 0.21; $$p \leq 0.0012$$; and r = − 0.22; $$p \leq 0.0007$$) respectively (Fig. 5A, B). However, this connection seems to be disappearing due to long-term training (DNAmPhenoAge; masters: r = − 0.11 $$p \leq 0.2014$$, sedentary: r = − 0.34; $$p \leq 0.0007$$, Fig. 5C, D) (DNAmGrimAge masters: − 0.12 $$p \leq 0.1574$$, sedentary: r = − 0.36; $$p \leq 0.0004$$, Fig. 5E, F).Fig. 5The correlation between telomere length and DNAmPhenoAge and DNAmGrimAge measured by RT-PCR. The epigenetic aging was calculated on the DNA methylation pattern based on the description of DNAmPhenoAge and DNAmGrimAge. Both the DNAmPhenoAge (Panel A $$n = 241$$) and DNAmGrimAge (Panel B $$n = 241$$) showed a significant relationship with RT-PCR-based TL. Exercise abolished these correlations (Panel C–F $$n = 241$$) The relationship between DNAm-based TL estimation with DNAmPhenoAge and DNAmGrimAge showed an even more powerful relationship (r = − 0.77; $p \leq 0.0001$; and r = − 0.79; $p \leq 0.0001$, Fig. 6A, B), but exercise habits did not affect this (Table 2).Fig. 6The relationship between DNAm-based TL estimation with DNAmPhenoAge and DNAmGrimAge. DNAm-assessed TL showed an even stronger relationship with DNAmPhenoAge and DNAmGrimAge (Panel A, B). $$n = 241$$Table 2Correlation between DNA methylation-based markersDNAmTLAllMastersSedentaryDNAmPhenoAger = − 0.77; $p \leq 0.0001$r = − 0.75; $p \leq 0.0001$r = − 0.79; $p \leq 0.0001$PhenoAgeAccelr = − 0.21; $$p \leq 0.0012$$r = − 0.16; $$p \leq 0.062$$r = − 0.26; $$p \leq 0.0126$$DNAmGrimAger = − 0.79; $p \leq 0.0001$r = − 0.76; $p \leq 0.0001$r = − 0.82; $p \leq 0.0001$GrimAgeAccelr = − 0.25; $p \leq 0.0001$r = − 0.28; $$p \leq 0.0006$$r = − 0.20; $$p \leq 0.0497$$ When the relationship of DNAmPhenoAge acceleration (r = − 0.21; $$p \leq 0.0012$$) and DNAmGrimAge acceleration (r = − 0.25; $p \leq 0.0001$) with DNAm based TL estimation were evaluated, the results revealed that longer telomeres were related to decelerated aging except for DNAmPhenoAge acceleration (Table 2). However, this at least a part could be due to some possible overlapping of CpGs incorporated in DNAnPhenoAge/GrimAge and DNAmTL.
## Discussion
In the present study, our data revealed that TL has associated with DNA methylation-based epigenetic aging biomarkers. It has been known that a higher level of cardiovascular fitness, the VO2max is associated with longer TL in a wide range of age groups (18–72 years old) (LaRocca et al. 2010). A recent systematic review screened the results of 43 randomized controlled, observational or interventional studies (Schellnegger et al. 2022) and concluded positive effects of exercise on telomere dynamics, however, the contribution of training modalities (intensity, duration, type of exercise, etc.) of the beneficial effects are not known.
It is clear that one of the striking effects of aging is a suppressed physiological function, however, it is also known that the progress of aging depends on environmental and lifestyle factors, including physical fitness (Radak et al. 2019). Indeed, the DNA methylation-based epigenetic biomarkers reflect individual aging more precisely than chronological aging and are related to telomere length (Lee et al. 2019). Based on our results, it cannot be excluded that exercise-induced DNA methylation is associated with longer telomere. However, it has to be noted that since the present study is a cross-sectional study, the possible relationship between the level of physical fitness a TL must consider with caution. Moreover, both DNAmPhenoAge acceleration and DNAmGrimAge acceleration showed that a higher level of physical fitness suppresses the progress of aging. The health-promoting effects of exercise are well documented (Hortobágyi et al. 2022; Quan et al. 2020; Radak et al. 2013), and this study also demonstrates that these changes could be associated with DNA methylation.
Surralles et al. [ 1999] demonstrated that telomere shortening could be affected by histone acetylation, therefore influenced by epigenetics. Moreover, the same study found that longer-lived cell lineages have an active X chromosome with a longer telomere than the inactive X, indicating that telomere maintenance alleles on the X chromosome impact survival. It was also suggested that the male Y chromosome is less protected and more prone to telomere shortening, but the chromosome-dependent telomere shortening is under debate (Genovesi et al. 2021). The results of the current study could not demonstrate that in the given population females have longer telomeres than males. It was suggested that there is a gender difference in the length of telomere, based on the telomerase activity, which could add base pairs to telomere is influenced by estrogen (Kyo et al. 1999), however, the study of Lin et al. reported that postmenopausal women who had longer endogenous estrogen therapy had longer telomere length with lower telomerase activity (Lin et al. 2011). Therefore, the mechanism of telomere shortening with gender bias appears to be complex and requires further investigations (Barrett and Richardson 2011).
The associations between TL and physiological functions are very important (Nordfjäll et al. 2008; Colon et al. 2019; Buttet et al. 2022). There are a few reports on TL and VO2max (LaRocca et al. 2010; Østhus et al. 2012; Brandao et al. 2020; Werner et al. 2019). Cross-sectional and longitudinal studies suggest that endurance exercise health-promoting effects are associated with longer TL. The underlying mechanisms are not known, but they could link to exercise-induced upregulation of antioxidant systems (Radak et al. 2013) since increased oxidative stress is associated with shorter telomeres (D'Mello et al. 2015).
The age-related loss of cognitive and physical performance is very normal, but the degree of loss and values are very much related to the level of physical fitness (Booth and Roberts 2008). Genetics can influence trainability, but regular exercise can greatly improve the level of physical fitness (Radak and Taylor 2022). In the present study, we have shown that DNAm-based estimation is a more sensitive method to examine the relationship between TL and physiological function, especially with VO2max than the RT-PCR-based method. In conclusion, the results of this study further emphasize the importance of the level of physical fitness in the aging process.
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|
---
title: In situ sequence-specific visualization of single methylated cytosine on tissue
sections using ICON probe and rolling-circle amplification
authors:
- Sohei Kitazawa
- Ryuma Haraguchi
- Yuki Takaoka
- Riko Kitazawa
journal: Histochemistry and Cell Biology
year: 2022
pmcid: PMC10006048
doi: 10.1007/s00418-022-02165-2
license: CC BY 4.0
---
# In situ sequence-specific visualization of single methylated cytosine on tissue sections using ICON probe and rolling-circle amplification
## Abstract
Since epigenetic modifications differ from cell to cell, detecting the DNA methylation status of individual cells is requisite. Therefore, it is important to conduct “morphology-based epigenetics research”, in which the sequence-specific DNA methylation status is observed while maintaining tissue architecture. Here we demonstrate a novel histochemical technique that efficiently shows the presence of a single methylated cytosine in a sequence-dependent manner by applying ICON (interstrand complexation with osmium for nucleic acids) probes. By optimizing the concentration and duration of potassium osmate treatment, ICON probes selectively hybridize to methylated cytosine on tissue sections. Since the elongation process by rolling-circle amplification through the padlock probe and synchronous amplification by the hyperbranching reaction at a constant temperature efficiently amplifies the reaction, it is possible to specifically detect the presence of a single methylated cytosine. Since the ICON probe is cross-linked to the nuclear or mitochondrial DNA of the target cell, subsequent elongation and multiplication reactions proceed like a tree growing in soil with its roots firmly planted, thus facilitating the demonstration of methylated cytosine in situ. Using this novel ICON-mediated histochemical method, detection of the methylation of DNA in the regulatory region of the RANK gene in cultured cells and of mitochondrial DNA in paraffin sections of mouse cerebellar tissue was achievable. This combined ICON and rolling-circle amplification method is the first that shows evidence of the presence of a single methylated cytosine in a sequence-specific manner in paraffin sections, and is foreseen as applicable to a wide range of epigenetic studies.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00418-022-02165-2.
## Introduction
Epigenetics is a mechanism that regulates gene expression patterns by changing genome structure and function in response to the environment (Seton-Rogers 2015; Waddington 1959; Fujii et al. 2021; Sato and Sassone-Corsi 2022; Cedar et al. 2022). It is becoming clear that this mechanism is not only closely related to cell differentiation, nuclear reprogramming, and aging signals, but also to several disease processes such as cancer (Skourti and Dhillon 2022; Issa 2000; Toyota and Issa 2000; Esteller 2007), diabetes (Mahajan et al. 2022; Ling et al. 2022), atherosclerosis (Lund and Zaina 2007; Turunen et al. 2009), and lifestyle-related conditions (Riancho 2015). Epigenetics is ultimately interpreted as a control mechanism of gene expression by changing the chromatin structure (Ordog et al. 2012). Three typical epigenetic control mechanisms affect such regulation: [1] histone protein modification (Cedar and Bergman 2009; Zhang et al. 2021), [2] non-coding RNA (Kleaveland et al. 2018; Wei et al. 2017) and [3] DNA methylation (Cedar et al. 2022). The interaction among these three mechanisms aggregates or relaxes the chromatin structure to reduce, stop, enhance, or start gene expression (Goldberg et al. 2007). Among these three mechanisms, DNA methylation is regarded as a major epigenetic landmark (Meier and Recillas-Targa 2017). Indeed, when cells divide and DNA is replicated, the methylated or unmethylated state is immediately maintained by DNA methyltransferase as cell memory (Jones and Liang 2009; Petryk et al. 2021). This maintenance methylation process is, however, more unstable than conventional nucleotide duplication at cell division, and therefore facilitates the epigenetic diversity of cell types from a single fertilized egg (Petryk et al. 2021).
Histochemical techniques have been developed to demonstrate epigenetic alterations at tissue or cell level. Various specific antibodies against modified histone proteins have been developed (Hayashi-Takanaka et al. 2020), and are already being applied to the histopathological diagnosis of giant cell tumors of bone (Behjati et al. 2013; Ishihara et al. 2022), chondroblastoma (Cleven et al. 2015) and some brain tumors (El-Hashash 2021). For the DNA methylation status, immunohistochemistry using an antibody that recognizes methylated cytosine has been developed to demonstrate the presence of methylated cytosine (Frediani et al. 1986). Nonetheless, the antibody simply provides a ubiquitous nuclear staining pattern and, therefore, the biological information obtained is limited. The method using microdissection is also available (Kitazawa et al. 2018), but since the DNA methylation status may differ between neighboring cells, it requires isolation of single cells from tissues, which limits epigenome analysis with the use of this method.
Here, we demonstrate a novel histochemical technique that efficiently displays the presence of a single methylated cytosine in a sequence-dependent manner by applying an ICON (interstrand complexation with osmium for nucleic acids) probe (Okamoto and Tainaka 2005).
## Designing ICON, padlock, and branching probes (Fig. 1)
**Fig. 1:** *Designing ICON, padlock, and branching probes. The objective sequence is located 5'-upstream of the murine RANK gene (upper panel). This region contains numerous CpG sites (cg in red) forming typical CpG islands. The target sequence gcgccgctgctggcgctctgcgtgct (yellow highlighted part) was set to detect the methylation of cytosine (arrows). The ICON probe, 5-agc acX cag agc gcc agc agc ggc gta gtg agt cgt atc gct gga gga ccg ctc aat gcg ggc gtt g-3, is designed complementary to the target sequence of murine RANK gene (underlined), where X is the modified nucleotide to cross-link to the corresponding methylated cytosine. The customized ICON probes were synthesized at GeneDesign Co. Ltd. (Ajinomoto Bio-Pharma Services, Tokyo, Japan, https://www.ajioligos.com/en/products/nucleic-acid-medicine-oligos/). The ICON probe has a bending sequence followed by the padlock probe ligation site (green) to which the padlock probe binds during the subsequent rolling-circle amplification reaction. The sequence of the padlock probe is set to be placed at both ends with a free phosphoric acid group at its 5’-end to form a circular structure when hybridized to the padlock probe ligation site of the ICON probe. The primers used for the hyperbranching amplification reaction were prepared by synthesis of biotin-labeled 5'-side primers for both forward (F) and branching (B). F: ttagtggagcctcttctttac (yellow), B: cataggtcagtaatcatagag (purple) are synthesized and used during the hyperbranching reaction. Each primer is designed to bind to each colored part of the padlock probe. The negative control is prepared by replacing the target-specific sequence of each ICON probe with a shuffled non-specific one*
The objective sequence selected is located in the 5'-upstream region of the murine receptor for the osteoclast differentiation factor, the receptor activator of nuclear factor κB (RANK, also known as the TRANCE receptor, TNFRSF11A) gene (Ishii et al. 2008). This region contains numerous CpG sites (red cg in Fig. 1) forming typical CpG islands (part of the sequence is as follows: cg sites are in bold face; 5-acc gcc ggt cca cag agg ccg cgc gcc cag ccc gcc cgc acc gcg cca tgg ccc cgc gcg ccc ggc ggc gcc gcc agc tgc ccg cgc cgc tgc tgg cgc tct gcg tgc tgc tcg ttc cac tgc agg taa gga tcc gca ccg ctg tcg cct gtg gtc ccc cag ggt cga ggt cct ctg gcg cgc ccc agc ctc acg agc acc agg tga aac tcg ggg tat ccg gat gga gac ccc agc tgg-3). From these CpG islands, the ICON probe was designed to hybridize to gcgccgctgctggcgctctgcgtgct (underlined in the above sequence, yellow high-lighted part in Fig. 1) and to detect the methylation of cytosine (arrows, Fig. 1). The designed ICON probe has the following sequence: 5-agc acX cag agc gcc agc agc ggc gta gtg agt cgt atc gct gga gga ccg ctc aat gcg ggc gtt g-3. The underlined part is the complementary sequence that binds to part of the murine RANK gene, and the X is a modified nucleotide to cross-link to the corresponding methylated cytosine. The customized ICON probes were synthesized at GeneDesign Co. Ltd. (Ajinomoto Bio-Pharma Services, Tokyo, Japan (https://www.ajioligos.com/en/products/nucleic-acid-medicine-oligos/)). The sequence shown in green in Fig. 1 is the one to which the padlock probe (Landegren et al. 1996) binds during the subsequent rolling-circle amplification reaction (Zhang et al. 1998; Watanabe et al. 2011). This sequence can be designed arbitrarily as long as there is no homology to the DNA sequence of the organism to be examined. By changing the individual padlock probe ligation sequence of the ICON probe, multicolored staining is possible in the subsequent amplification reaction (supplementary Fig. 1). The sequence of the padlock probe is set to be placed at both ends with a free phosphoric acid group at its 5’-end to form a circular structure when hybridized to the padlock probe ligation site of the ICON probe. Using the circular form of the padlock probe as a template, the tail part on the 3'-side of the ICON probe then expands while repeating the same base sequence just like a “roller-type stamp” (Watanabe et al. 2011). The primers used for the hyperbranching amplification reaction were prepared by the synthesis of biotin-labeled 5'-side primers for both forward (F) and branching (B) primers. F: ttagtggagcctcttctttac (yellow, Fig. 1) and B: cataggtcagtaatcatagag (purple, Fig. 1) are synthesized and used during the hyperbranching reaction. Each primer is designed to bind to each colored part (in Fig. 1) of the padlock probe. The same primer combination is used repeatedly for each padlock probe sequence. Fluorescent labeling is achieved by using various fluorescent labeling primers instead of biotin labeling (Alexa Fluor 488 for green and Alexa Fluor 594 for red). All customized oligo DNA were purchased from ThermoFisher Scientific. Similarly, the ICON probe for detecting methylated cytosine at murine mitochondrial DNA D-loop (Stoccoro et al. 2022; Bianchessi et al. 2016) was designed as follows: 5- cgtatgggcXataacgcatttgatggccctgtagtgagtcgtatcgctggaggaccgctcaatgcgggcgttg-3 (the underlined part is specific to the mitochondrial DNA sequence). The negative control is prepared by replacing the target-specific sequence of each ICON probe with a shuffled non-specific one.
## Specificity of ICON probe binding to the target methylated sequence on nylon membrane (Fig. 2a)
**Fig. 2:** *The ICON probe differentially detects single methylated cytosine in a sequence-dependent manner. Dot-blot hybridization (a) demonstrates that without stripping the membrane, the radiolabeled oligo DNA probe hybridizes both the unmethylated and methylated plasmids with target DNAs (lanes 2 and 4). After stripping, while the un-crosslinked probe was stripped off by boiling, the hybridized probe crosslinked by potassium osmate treatment remained in the methylated plasmid with target DNA (lane 4). On the glass slides (b), conventional hybridization followed by potassium osmate reaction, and washing without denaturing by NaOH, the ICON probe binds to both the methylated and unmethylated target DNA (b, left, Nos. 2 and 4). On the other hand, after washing with NaOH and removing the un-crosslinked probe, only the ICON probe remained in the methylated target on glass slides (b, No. 4)*
The 5’-flanking region of the mouse RANK gene was cloned as described (Ishii et al. 2008), and the 1 kb upstream from the transcription start site was ligated onto a pGL3 Basic vector (Promega, Madison, WI, USA) yielding a pGL3-RANK-1. The plasmid vector with or without the RANK gene promoter region was treated with SssI methylase (New England Biolabs, Ipswich, MA, USA, cat# M0226S) to yield a construct with methylation at all CpG sites. One pg of four types of plasmid constructs: [1] vector without methylation, [2] vector + RANK gene promoter without methylation, [3] vector with methylation, and [4] vector + RANK gene promoter with methylation, was spotted onto the Hybond N + nylon membrane (Amersham Biosciences Corp., Piscataway, NJ, USA) and immobilized by UV cross-linking. The blotted membrane was prehybridized with prehybridization buffer in a sealed bag at 50 °C in a water bath for 2 h. The prehybridization buffer was replaced with hybridization solution (hybridization buffer containing 100 ng/ml of ICON probe for RANK gene promoter end-labeled with γP32-ATP with T4 polynucleotide kinase (Takara, Tokyo, Japan) to a specific activity of 2 × 108 cpm/ µg. The membranes were hybridized at 50 °C for 2 h, washed in 2 × SSP containing $0.1\%$ SDS, and in 1 × SSP containing $0.1\%$ SDS, then in a 0.1 × SSP containing $0.1\%$ SDS. For the potassium osmate reaction, the washed membrane was treated with freshly prepared K3Fe(CN)6 (Wako, Tokyo, Japan) in 50 mM Tris–Cl, pH 7.7, 0.5 mM EDTA, 1 M sodium chloride at 55 °C for 10 min, and then with a freshly prepared potassium osmate solution containing 0.1 M K3Fe(CN)6, 5 mM K2OsO4 (Sigma-Aldrich, Japan) in 50 mM Tris–Cl, pH 7.7, 0.5 mM EDTA, 1 M sodium chloride at 55 °C for 1 h basically, according to the protocol described by Tainaka and Okamoto [2011]. After cross-linking by potassium osmate, the membrane was rinsed three times with 0.1 × SSP containing $0.1\%$ SDS at 37 °C, then analyzed with image analyzer BAS2000 (FUJIX, Tokyo, Japan) to confirm the conventional dot-blot hybridization reaction. After the first imaging, the membrane was treated for 5 min with boiling H2O containing $0.1\%$ SDS for stripping the hybridized probe, and reanalyzed with BAS2000 imaging.
## Optimizing specificity of ICON probe binding to target methylated sequence on glass slide (Fig. 2b)
Prior to the histochemical application of the ICON probe, the reaction was optimized for the non-isotopic demonstration of spotted oligonucleotide with or without single methylated cytosine on glass slides. Part of the RANK gene promoter region (tct ggt tct tac ttc agg gcc atc aaa tgc tgg atc* gcc cat acg) and its shuffled sequence (gca agt cct aac gtc tgt gtt atc tga ggc gtc tac* gaa ctc cgc) were synthesized with 5’-amino modification (Hokkaido System Science, Co., Ltd., Sapporo, Japan) with or without methylation at * site, and with 0.1 pg each of four types of oligonucleotide: [1] shuffled RANK without methylation, [2] RANK without methylation, [3] shuffled RANK with methylation, and [4] RANK with methylation, spotted onto a SDA0011 microarray of glass slides (Matsunami Glass Ltd, Osaka, Japan) for covalent immobilization of amino-modified oligoDNA by using the VP478A DNA manual arrayer (V & P Scientific, CA, USA). The slides were then kept overnight in a humidified box with saturated NaCl solution at 37 °C. After soaking in distilled water, the spotted slides were air-dried and stored in a desiccator until use. To optimize specificity and sensitivity of in situ non-radioactive detection of methylated cytosine on glass slides, a series of conditions (concentration and duration of K2OsO4 treatment with or without K3Fe(CN)6 solution for cross-linking, and stripping solutions at various temperatures) were tested.
## Padlock ligation, hyperbranching-rolling-circle amplification (H-RCA) reaction
The composition of the padlock ligation reaction solution was adjusted to a total volume of 50 µl (padlock probe (1 pmol/µl) 5 µl, 10 × Taq-DNA buffer 5 µl, Taq DNA ligase (New England Biolabs, cat# M0208S) 2.5 µl, sterilized water 37.5 µl). The reaction solution was then layered on the section, treated at 94 °C for 10 min, and allowed to stand in a closed container preheated at 60 °C for 60 min to complete the ligation reaction. After washing three times with PBS for 5 min at 37 °C, the specimen was washed thoroughly by dipping the slide several times in distilled water. The composition of the H-RCA reaction solution was adjusted to 50 µl: (forward primer (10 pmol/µl) 1 µl, backward primer (10 pmol/µl) 1 µl, 2 × Reaction Mix 25 µl, Bst DNA polymerase (Eiken Chemical Co., Ltd., Tokyo, Japan, cat# LMP204) 2 µl, sterilized water 21 µl). The detection primers used for the amplification reaction were prepared by customized synthesis with 5’-biotin-labeled (Fig. 1) to incorporate biotin into the amplified DNA together with the hyperbranching reaction. The reaction solution was layered on the section and allowed to stand for 30 min in a closed container preheated at 65 °C to complete the amplification reaction. After washing three times in PBS for 5 min at 37 °C, the specimen was thoroughly washed by dipping the slide several times in distilled water. Strept-Avidin-HRP complex (Bio-Techne, MN, USA, cat# 4800-30-06) diluted 50-fold with PBS was added to cover the specimen in a closed container preheated at 37 °C for 10 min. After washing 3 times with PBS by gentle shaking for 5 min at 37 °C, the colorimetric reaction was completed by adding DAB under a microscope until a round dot-like spot was observed (usually within 3–5 min). The specimen was then washed with water for 1 min to stop color development, and again briefly washed with water. The specimen was then dehydrated with series of 70, 80, 90, and $100\%$ graded alcohol, and finally immersed in xylene and covered with a glass cover.
## Cell and tissue preparation (Fig. 3)
**Fig. 3:** *Identification of RANK gene promoter methylation by ICON histochemistry. By epigenomic analyses (upper panel), the CpG island located at the 5’ transcription start site is hypomethylated (blue) in RAW264.7 cells and hypermethylated (red) in ST2 cells. Because ICON histochemistry requires, unlike conventional fluorescent ISH (FISH) study, repeated denature, hybridization and development process, preservation of cell structure is impaired at higher magnification as shown in g and h. In spite of this drawbacks, histochemical demonstration targeting these two cell lines (a and b, HE) shows that ICON histochemistry differentiates these two cells; while red signals by Alexa Fluor 594 are scarcely observed among CpG-hypomethylated RAW264.7 cells (e and g), those of CpG-hypermethylated ST2 cells are seen, mostly as one or two red spots at the marginal area in the nuclei (f and h). Negative controls prepared by shuffled sequence do not show any significant signals (b and d). Each scale bar indicates 20 µm*
With the use of optimized hybridization, washing and denaturing conditions on glass slides, as described in the above-mentioned studies, the study was extended to detecting single methylated cytosine at cell and tissue levels. Basically, the following protocol was used for cell and tissue studies.
RAW264.7 cells (RIKEN, Tsukuba, Japan) were cultured and maintained in α-MEM (Sigma-Aldrich), supplemented with $10\%$ FBS (Sigma-Aldrich), 50 I.U./ml-50 µg/ml penicillin/ streptomycin (ICN Biomedicals Inc., Aurora, OH, USA) and 2 mM of l-glutamine (ICN Biomedicals Inc.). Cells of mouse bone marrow stromal cell line ST2 (Riken, Tsukuba, Japan) were cultured in phenol red-free α-MEM (Sigma-Aldrich) supplemented with $2\%$ charcoal-stripped fetal bovine serum, collected by centrifugation (1500 rpm, 5 min) and fixed in $4\%$ paraformaldehyde (PFA)/PBS for 2 h at 4 °C. At the same time, DNA samples were purified and subjected to epigenome analyses (Bisulfite Sequencing Services, Active Motif, Carlsbad, CA, USA). Brain tissues from 16-week-old mice, under anesthesia, were dissected and maintained in $2.5\%$ isoflurane and fixed in $4\%$ PFA/PBS for 2 days at 4 °C, dehydrated through ethanol, and embedded in paraffin; 4-µm serial sections were then prepared for histological analysis. DNA was purified from part of the brain tissue samples, and subjected to bisulfite conversion. After PCR amplification of the mitochondrial D-loop with a set of primers (sense: 5’-GGTTTTTATTTTAGGGTTATTAAATG-3’ and antisense: 5’-CCAAATACATAACACCACAATTATATTAATC-3’), PCR products (121 bps) were ligated to TA-vector (Promega); 12 independent colonies were then selected and sequenced. Mouse brains were dissected, and the tissue fixed in $4\%$ PFA/PBS for 2 days at 4 °C was dehydrated through ethanol and embedded in paraffin; 4-μm serial sections were then prepared for histological analysis. Routine hematoxylin and eosin (HE) staining and immunohistochemical analysis with the use of the primary anti-MTCO1 antibodies (1:1000, cat. no. ab203912, Abcam Plc.) were carried out. Signal detection was carried out with 3,3'-diaminobenzidine (DAB, cat. no. K3468, Dako North America Inc.). All animal experimental procedures and protocols were approved by the Committee on Animal Research at Kobe and Ehime Universities (Permit No. 05-KU-36–16, 9 July 2018).[1] Tissue sample deparaffinization, hydrophilization, and pretreatment.[1] After deparaffinizing with xylene and alcohol hydrophilic series, the specimen is immersed in distilled water.[2] Microwave treatment in 0.01 M citric acid buffer (pH 6.0) at 97 °C for 20 min; let it stand and gradually cool to 23 °C to loosen methylene bridge generated by the fixative.[3] Proteinase treatment with pepsin solution ($0.3\%$ pepsin/0.01 N HCl, pre-warmed to 37 °C) in a water bath at 37 °C for 10 min. After the treatment, wash the specimen with distilled water twice for 3 min each, then dip several times in PBS solution.[4] Place the $4\%$ PFA solution on slides, and let them stand for 10 min at 23 °C for refixation. Next, wash again three times with PBS for 3 min to thoroughly wash off the PFA fixative. Then dip the slides in distilled water.[5] Dip the specimen in $0.1\%$ Tween$\frac{20}{2}$ × SSC, pre-warmed at 37 °C, for 30 min to induce aging.[6] After washing with distilled water, dip the specimen in dehydration series (70, 80, 90, and $100\%$) of ethanol solutions ten times, and finally air-dry the specimen.[7] Layer $70\%$ formamide/2 × SSC preheated at 75 °C on the sample and place it in a humid incubator at 75 °C for 5 min to heat and denature the nucleic acid in the tissue. At the same time, treat the hybridization buffer containing the ICON probe at 75 °C for 5 min and keep it warm at 42 °C.[8] After washing with distilled water, the specimen is dipped in an alcohol dehydration series (70, 80, 90, $100\%$ ethanol solution) ten times and finally air dried.[2] Hybridization and conventional washing[1] Add the ICON probe to the hybridization buffer (RNAscope blank probe dilutant-C1, 300041, Advanced Cell Diagnostics, Inc., CA, USA) to a final concentration of 1 pmol/µl. After covering the hybridization solution with a cover slip to prevent evaporation, place it in a closed humified container at 42 °C overnight.[2] After incubation, remove the cover slip, wash the specimen first, with 2 × SSC, $0.5\%$ SDS, for 5 min three times at 23 °C, second, with 0.2 × SSC, $0.1\%$ SDS for 5 min two times at 42 °C, third with 0.2 × SSC, $0.1\%$ SDS, at 23 °C for 5 min, and finally with 0.2 × SSC at 23 °C while dipping the slide in the solution about 5–10 times.[3] Osmium reaction and washing to strip off un-crosslinked ICON probe[1] For the potassium osmate solution, adjust the buffer (50 mM Tris–HCl, 1 M NaCl, 0.5 mM EDTA) by excluding potassium osmate from the stock at the time of use, and adjust the final concentration of potassium osmate to 1 mM immediately before use.[2] Overlay the washed specimen with the buffer, excluding potassium osmate, for 2 min at 23 °C. Replace the overlayed buffer with the buffer with potassium osmate, and keep at 37 °C for 15 min. Note that this operating temperature and duration may need minor adjustment according to the fixative condition of the specimen.[3] Remove the potassium osmate and wash the specimen with 0.5 N NaOH solution at 23 °C for 20 min to dissociate the ICON probe, for which a strong bond was not obtained with potassium osmate, from the double-stranded state.[4] Remove the 0.5 N NaOH solution and wash the specimen with $60\%$ DMSO. This DMSO treatment prevents the detached ICON probe from rehybridization.[5] Repeat washing the specimen with PBS for 5 min at 23 °C. Since specimens treated with 0.5 N NaOH are easily peeled off, this PBS washing needs to be done carefully.[6] After soaking in distilled water, the specimen is thoroughly air-dried.[4] Padlock ligation and H-RCA reaction[1] Adjust the composition of the Padlock Ligation reaction solution to a total volume of 50 µl (Padlock probe, 1 pmol/µl, 5 µl, 10 × Taq-DNA Buffer 5 µl, Taq DNA ligase 2.5 µl, sterilized water 37.5 µl) according to the manufacturer’s protocol. The reaction solution is layered on the section, treated at 94 °C for 10 min, and then allowed to stand in a closed container preheated at 60 °C for 60 min to complete the ligation reaction.[2] Repeat washing with PBS for 5 min at 23 °C three times, and then replace the PBS with distilled water. Wash thoroughly by dipping the slide several times in the solution and shaking it.[3] Adjust the composition of the H-RCA reaction solution (primer F (10 pmol/µl) 1 µl, primer B (10 pmol/µl) 1 µl, 2 × reaction mix 25 µl, Bst DNA polymerase (Eiken Chemical Co., cat # LMP204) 2 µl, sterilized water 21 µl to a total volume of 50 µl. The reaction solution is layered onto the section and allowed to stand for 30 min in a closed container preheated at 65 °C to complete the amplification reaction.[4] Repeat PBS washing for 5 min × 3 times at 23 °C, and then replace PBS with distilled water. Wash thoroughly by dipping the slide several times in the solution and shaking it.[5] Add strept-avidin-HRP (TREVIGEN, cat# 4800-30-06) diluted 50-fold with PBS to the specimen and let react for 10 min in a closed container preheated at 37 °C.[6] Repeat washing the specimen thoroughly with the PBS three times, while shaking, for 5 min at 23 °C.[7] Observe color development of the DAB under a microscope. Usually, a round dot-like color appears in about 3–5 min. Wash the specimen with water to stop color development, stain with a light green stain for 1 min, and then lightly wash with water. After the alcohol dehydration series, replace with xylene and seal the specimen with a glass cover.
## Image acquisition
All the sections were photographed under a Nikon Eclipse Ci microscope (Nikon, Japan), equipped with a 5.9-megapixel Nikon DS-Fi3 digital camera (Nikon, Japan) using Nikon NIS-Elements D software version 5.11 (Nikon, Japan).
## ICON probe differentially detects single methylated cytosine in a sequence-dependent manner (Fig. 2)
As shown in Fig. 2a, dot-blot hybridization demonstrated that without stripping the membrane, the radiolabeled oligo DNA probe hybridized both unmethylated and methylated plasmid with target DNAs (Fig. 2a, lanes 2 and 4). After stripping, while the uncross-linked probe was stripped off by boiling, the hybridized probe cross-linked with potassium osmate treatment remained linked to the methylated plasmid with target DNA (Fig. 2a, lane 4). On the glass slides, conventional hybridization followed by potassium osmate reaction, and washing without denaturing by NaOH, the ICON probe bound to both methylated and unmethylated target DNA (Fig. 2b, left, No. 2 and 4). On the other hand, since the potassium osmate reaction was used as for radioactive probes resulted in low selectivity for the methylated target after the NaOH stripping reaction, the K2OsO4 concentration was serially reduced with and without the K3Fe(CN)6 solution. The optimal condition for selective demonstration of the methylated target on the glass slide was achieved by adding 1 mM of K2OsO4 without K3Fe(CN)6 solution (Fig. 1b, right). This optimized condition was used for the following histochemical applications.
## Identification of RANK gene promoter methylation by ICON histochemistry (Fig. 3)
By epigenomic analyses (Fig. 3 upper panel), the CpG island located at 5’ transcription start site was hypomethylated (blue) in RAW264.7 cells, constitutively expressing the RANK gene (Fig. 3a, HE), and hypermethylated in ST2 cells (Fig. 3c, HE). Histochemical demonstration targeting these two cell lines showed that ICON histochemistry clearly differentiated these two cells, while red signals by Alexa Fluor 594 were scarcely observed among CpG-hypomethylated RAW264.7 cells (Fig. 3e and g); those of CpG-hypermethylated ST2 cells were clearly seen mostly at the marginal area in the nuclei (Fig. 3f and h). Negative controls prepared by shuffled sequence did not show any significant signals (Fig. 3b and d).
## Demonstration of methylated cytosine in mitochondrial DNA (Fig. 4)
**Fig. 4:** *Demonstration of methylated cytosine in mitochondrial DNA. The upper panel shows the ICON sequence complementary to the corresponding mitochondrial D-loop area rich in CpG sites (D-loop, bold face). Bisulfite sequencing of mouse brain tissue sample demonstrates that 25% of colonies show methylation at CpG sites in the mitochondrial D-loop (middle panel, arrows, M), while the rest show an unmethylated pattern (middle panel, U). In mouse cerebellum, (lower panel, a, HE), mitochondrial localization by anti-MTCO-1 antibody appears, albeit tends to be abundant in the Purkinje cells, rather ubiquitous mitochondria distribution (lower panel, b, MT-1). By ICON histochemistry, accumulation of mitochondrial DNA methylation is observed in Purkinje cells in the cerebellum (lower panel, c, white arrows). No significant signals are observed by negative control (lower panel, c, right lower insert, white arrows). Each scale bar indicates 50 µm*
By bisulfite sequencing, three of 12 colonies in mouse brain tissue showed methylation at CpG sites in mitochondrial D-loop (Fig. 4 upper panel, arrows, M), while the rest showed an unmethylated pattern (Fig. 4, upper panel, U). In the mouse cerebellum, (Fig. 4a, HE), mitochondrial localization examined by immunohistochemistry using an MT-1 antibody, showed that, albeit they tended to be abundant in the Purkinje cells and molecular cell layers of the cerebellar cortex, mitochondria were present in virtually all cell types, including the granular layer (Fig. 4b, MT-1). By ICON histochemistry, accumulation of mitochondrial DNA methylation was observed in Purkinje cells in the cerebellum (Fig. 4c, white arrows). No significant signals were observed by negative control (Fig. 4c, right lower insert, white arrows).
## Discussion
Epigenetics, especially DNA methylation, should be analyzed on a cell-by-cell basis because cells may show an epigenetic pattern different from the original cell when they divide to form daughter cells (Kitazawa et al. 2022). Therefore, genome analysis using a mix of multiple cell types may not always correctly reflect the characteristics of the target cell (Kitazawa et al. 2018). On the other hand, there are technical limitations in obtaining epigenomic information from a single cell collected by microdissection (Raine et al. 2022). Therefore, it is very important to conduct “morphology-based epigenetics research” (Kitazawa et al. 2022), in which the region-specific DNA methylation status is observed while maintaining tissue architecture. Since the function of cytosine methylation differs greatly between CpG-islands located near the gene promoter, exons, and introns, the information obtained by simply observing the presence or absence of methylated cytosine by immunohistochemical techniques, with the use of an anti-methylated cytosine antibody, is limited (Frediani et al. 1986). Recently, a tissue-enzyme-chemistry method using methylation-sensitive and -insensitive enzymes has also been developed for the histo-endonuclease-linked detection of methylation sites of the DNA (HELMET) method (Koji et al. 2008). By this HELMET method, methylation dynamics of DNA in germlines is measured on a cell-by-cell basis by digesting the DNA sequence CCGG with methylation-sensitive and methylation-resistant restriction enzymes, while detecting differences in cleavage patterns on tissue sections (Koji et al. 2008). Nevertheless, even with this advanced method, it is not possible to detect DNA methylation specific to sequences involved in specific gene expression. In case of targeting specific gene expression, however, sequence-specific analyses of the methylation status of cytosine on a tissue section are requisite. It is also possible to detect the presence of sequence-specific methylated cytosines by applying a methylation-specific PCR method to methylated and unmethylated cytosines in tissue sections after their treatment with sodium bisulfite (Kitazawa et al. 2018). We have previously demonstrated that this sodium bisulfite-mediated technique shows the distribution of methylation near the RANK gene transcription start site in mouse testis tissue in a sequence-specific manner with the use of a padlock probe specific to the converted base sequence on the tissue section (Kitazawa et al. 2018). Nonetheless, this method has limited applicability because the conversion of cytosine by sodium bisulfite may not be complete due to the strong DNA–protein cross-linking caused by formalin fixation, and it is sometimes difficult to ensure the specificity of probes that recognize DNA sequences after bisulfite conversion. More recently, a single-cell epigenetic visualization assay (EVA) (Kint et al. 2021) has been introduced by combining exonuclease and an antibody bound to the epigenetic mark of interest. Although this is an excellent method for visualizing epigenetic changes in a single cell, it may require the addition of more reaction steps for sensitive visualization of methylated cytosine at a single location, but has not yet been applied to detection in paraffin sections (Kint et al. 2021).
In this study, we have developed a method for in situ detection of the methylated state of cytosine at the single nucleotide level, specifically for the base sequence by the use of a probe that forms a complex with high affinity for methylated cytosine. The probe is termed an ICON probe (Okamoto and Tainaka 2005), and is custom-synthesized by GeneDesign, Inc. After forming a molecular hybrid with the target complementary DNA, the ICON probe forms a strong cross-link by complex formation with the target DNA when cytosine is methylated by treatment with a reaction reagent containing potassium osmate (Okamoto and Tainaka 2005). Using this property, a method for visualizing the presence of methylated cytosine in a specific sequence in a chromosome by fluorescent in situ hybridization (MeFISH) has already been described (Shiura et al. 2014; Li et al. 2013), in which a labelled ICON probe has been used directly to visualize repeated DNA sequences at a chromosome or nuclear level. In our current study, as shown in Fig. 1, when creating an ICON probe, a region not involved in molecular hybrids (intermediate bending region of about 12–15 bases followed by padlock probe recognition sequence of about 30 bases) is added to the tail part on the 3' side. With a padlock probe that recognizes the tail part, rolling-circle amplification elongates the 3’ tail of the hybridized ICON probe with the repeated sequence. Following the hyperbranching reaction coupled with biotin labeling, the sensitivity of visualizing the methylation of one base in situ, compared with isotopic labeling, is achieved. Indeed, in the current study, when using purified DNA samples on nylon membranes (Fig. 2a) or on glass slides (Fig. 2b), the methylated and unmethylated states were clearly distinguishable under strong reproving conditions by boiling after the ICON probe was hybridized with the DNA sequence of interest and complexed with potassium osmate treatment, as previously described (Okamoto and Tainaka 2005). In contrast to the use of purified DNA, however, detection of the methylation status in tissues and cells requires accounting for the influence of surrounding nucleoproteins and other factors, as well as the state of fixation. In the present study, examining numerous reaction conditions to determine fairly strict-condition settings was requisite because nonspecific reactions occur even at the cultured-cell level. In particular, the potassium-osmate reaction stage is critical, as has been reported in previous studies (Shiura et al. 2014; Li et al. 2013), in that the authors omitted the ferrate solution and used only potassium osmate to reduce nonspecific binding. After getting the apparent difference by spot hybridization study by the use of radiolabeled-probe (Fig. 2a), with confidence, we searched for the optimal condition for the following hybridization study. In our study, the concentration of potassium osmate treatment was lowered and the duration of the reaction shortened to ensure specificity. Furthermore, when stripping the unbound ICON probe, NaOH was used to prevent the ICON probe from rehybridization. Condition-setting started at the stage of whether it was possible to distinguish between cells that were definitely methylation-positive and those that were negative in a specific region of the gene. The entire procedure is shown as a schema in Fig. 5.Fig. 5Illustrative histochemical procedure for sequence-specific demonstration of single methylated cytosine on tissue section. After proteinase treatment and denaturation, tissue sections are hybridized with the ICON probe and washed as conventional histo-in situ hybridization with the use of the oligo-DNA probe. By osmium treatment, the ICON probe forms a tight cross-link against methylated cytosine, which remained after the NaOH renaturation process. Padlock probe hybridization and the subsequent rolling-circle amplification elongates the 3’-tail of the hybridized ICON probe with repeated sequences. The hyperbranching reaction by the biotin-labeled probe amplifies this reaction and facilitates signal detection under a light microscope. Because the ICON probe is strongly cross-linked to the nuclear DNA of the target cell, subsequent elongation and multiplication reactions proceed like a tree growing in soil with its roots firmly planted, enabling the demonstration of a single methylated cytosine in situ Next, our study was extended to formalin-fixed and paraffin-embedded specimens. The mitochondrial DNA in mouse brain tissue was examined to determine [1] whether DNA methylation is present and [2] in which cells it is distributed. From paraffin sections, and the use of PCR-mediated amplification from bisulfite converted DNA, a fraction of amplified DNA was identified as indeed containing methylation at CpG sites (black arrows in Fig. 4, bisulfite mapping data marked M) at the D-loop of the mitochondria DNA. Although this sodium bisulfite mapping method clearly showed that methylation was present in mitochondrial DNA, it did not provide information on which cells mitochondria were methylated. The ICON method targeting DNA methylation in the D-loop region (Fig. 4 upper panel) showed that most of the signal was localized in Purkinje cells in the cerebellum (Fig. 4, lower panel c).
Immunohistochemistry using specific antibodies for proteins and in situ hybridization using complementary bases for RNA and DNA have been developed and have greatly contributed to the advancement of morphology-based research. It has been challenging, however, to develop an efficient histochemical technique for epigenetics targeting DNA methylation, albeit it being an important issue in the post-genomic era.
In conclusion, our method of combining ICON and rolling-circle amplification is the first that visualizes the presence of a single methylated cytosine in a sequence-specific manner on paraffin sections, and is expected to be applicable to a wide range of future studies. The merit of this method is that the ICON probe is cross-linked to the nuclear DNA of the target cell at first, and the subsequent elongation and multiplication reactions proceed like a tree growing in soil with its roots firmly planted, so the presence of methylated cytosine can be confirmed in situ.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 13 KB)Supplementary file2 (TIFF 6775 KB)
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|
---
title: Molecular and neural roles of sodium-glucose cotransporter 2 inhibitors in
alleviating neurocognitive impairment in diabetic mice
authors:
- Iwona Piątkowska-Chmiel
- Mariola Herbet
- Monika Gawrońska-Grzywacz
- Kamil Pawłowski
- Marta Ostrowska-Leśko
- Jarosław Dudka
journal: Psychopharmacology
year: 2023
pmcid: PMC10006050
doi: 10.1007/s00213-023-06341-7
license: CC BY 4.0
---
# Molecular and neural roles of sodium-glucose cotransporter 2 inhibitors in alleviating neurocognitive impairment in diabetic mice
## Abstract
Diabetes causes a variety of molecular changes in the brain, making it a real risk factor for the development of cognitive dysfunction. Complex pathogenesis and clinical heterogeneity of cognitive impairment makes the efficacy of current drugs limited. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) gained our attention as drugs with potential beneficial effects on the CNS. In the present study, these drugs ameliorated the cognitive impairment associated with diabetes. Moreover, we verified whether SGLT2i can mediate the degradation of amyloid precursor protein (APP) and modulation of gene expression (Bdnf, Snca, App) involved in the control of neuronal proliferation and memory. The results of our research proved the participation of SGLT2i in the multifactorial process of neuroprotection. SGLT2i attenuate the neurocognitive impairment through the restoration of neurotrophin levels, modulation of neuroinflammatory signaling, and gene expression of Snca, Bdnf, and App in the brain of diabetic mice. The targeting of the above-mentioned genes is currently seen as one of the most promising and developed therapeutic strategies for diseases associated with cognitive dysfunction. The results of this work could form the basis of a future administration of SGLT2i in diabetics with neurocognitive impairment.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00213-023-06341-7.
## Introduction
Disturbances in cognitive functions and memory appear not only in the course of brain injury, mental illness, and neurological diseases such as Alzheimer’s disease (AD) or Parkinson’s disease (PD) but also in the course of metabolic impairment like diabetes mellitus (DM) (Roberts et al.2014; Teixeira et al. 2020; Biessels and Whitmer 2020; Li et al. 2016). Despite highly developed diagnostic tools, the molecular mechanisms underlying the development of cognitive dysfunctions in diabetics are still not fully understood (Sandhir and Gupta 2015). However, it is believed that altering the structure and neurophysiological functions of the brain is a consequence of chronic glucose metabolism impairment, increased inflammation, disrupted blood–brain barrier integrity, intracellular oxidative stress, and mitochondrial dysfunction (Moran et al.2013; Moran et al.2019; Avogaro et al.2010; Etchegoyen et al. 2018; Banks and Rhea 2021; Fournet et al. 2018). Deterioration of cognitive abilities and difficulties in processing and restoring remembered information are also associated with the site and the size of changes in the brain (Cukierman et al. 2005; Biessels et al. 2008; Wei et al. 2016). Studies showed that in diabetic patients, the highest degree of gray matter changes is observed in the medial temporal lobe, anterior cingulate gyrus, and medial frontal lobe, whereas white matter in the frontal and temporal regions (Moretti et al. 2012; Ramanoël et al. 2018; Callisaya et al. 2019). It is worth noting here that a similar distribution of cortical atrophy has also been described in the early stages of Alzheimer’s disease (Pini et al. 2016; Thompson et al. 2003; Infante-Garcia et al. 2016; Manschot et al. 2006; de la Monte and Wands 2008). It is increasingly evident that there is an interplay between the metabolic dysfunction associated with type 2 diabetes mellitus (T2DM) and patient susceptibility to the common development of dementia, specifically Alzheimer’s disease (AD) (Arvanitakis et al. 2004; Biessels et al. 2006; Kopf & Frolich 2009; Zilliox et al. 2016).
Some research showed that T2DM and AD have common pathogenetic mechanisms and hence common clinical features. It was proven that metabolic impairment in the course of diabetes (hyper- and hypoglycemia or insulin resistance) leads to chronic inflammation and pathological protein changes (Götz et al. 2009; Yun et al. 2020). Multiple studies have indicated that patients with T2DM have an increased incidence of alpha-synuclein (SNCA) accumulation, aggregation, and phosphorylation in both the pancreatic β cells and the brain (Martinez-Valbuena et al. 2018; Bassil et al. 2017) which favors by disturbed insulin signaling (Gao et al. 2015) and the fibrilization of amyloid-β and tau, two key proteins in Alzheimer’s disease (Twohig and Nielsen 2019). Research shows that the brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT3), and neurotrophin-$\frac{4}{5}$ (NT$\frac{4}{5}$) are known for their respective roles in neuroprotection and neuronal death (Eggert et al. 2021; Vilar and Mira 2016; Nordvall et al. 2022) and can promote the processing of APP by upregulation of α-secretase, thus protecting the brain from Aβ toxicity (Nigam et al.2017; Eggert et al. 2018; Jiao et al. 2016; Mitroshina et al. 2020).
On the other hand, it should not be forgotten that generalized inflammation associated with diabetes modulates neurotrophin functions by reducing their production and limiting their neuroprotective action (Chen et al. 2016; Sastre et al. 2008). Studies also indicate a significant contribution of hypoxia-inducible factor 1α (HIF1α) in modulating the APP (Zhang et al. 2016; Catrina and Zheng 2021; Gunton 2020).
However, the interrelationship between APP, BDNF, NT4, SNCA, or HIF1a signaling and cognitive function in diabetic patients is not fully understood. Taking into account the dynamically increasing incidence of diabetes (Saeedi et al. 2019; Steward 2019; Chiang et al.2014) as well as its relationship with patients’ susceptibility to cognitive disorders (Saedi et al. 2016; Srikanth et al. 2020; Pandey and Tamrakar 2019), there is an urgency for new and more effective forms of therapy to reduce the development of cognitive deficits in diabetics. A number of studies have shown that anti-diabetic drugs like metformin, thiazolidinediones, and DPP4 inhibitors are capable of entering the brain after systemic administration and promoting neurogenesis which translates into a clinical improvement of cognitive functions of treated patients (Wium-Andersen et al.2019; Zhou et al.2020; Patil et al.2014; Darsalia et al. 2019; Athauda et al.2017). The study by Wium-Andersen et al. [ 2019] demonstrated that patients with diabetes who used metformin, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) agonists, or sodium-glucose cotransporter 2 (SGLT2) inhibitors had lower odds for developing dementia. In addition, a meta-analysis by Zhou et al. [ 2020] showed that diabetic patients treated with DPP-4 inhibitors had a lower risk of dementia than patients treated with metformin or thiazolidinedione. Mui et al. [ 2021] reported that diabetic patients taking SGLT2 inhibitors for 5 years had a lower incidence of dementia than those using DPP-4 inhibitors during the same period. Whereas, Athauda et al. [ 2017] confirmed the beneficial effect of therapy with exenatide-glucagon-like peptide-1 (GLP-1) receptor agonist on motor and cognitive activity in patients with Parkinson’s disease.
Sodium-glucose cotransporter 2 inhibitors (SGLT2i, also called gliflozins or flozins) are one of the newly developed oral anti-hyperglycemic agents used to treat the type 2 diabetes mellitus (Chao and Henry 2010; Heise et al. 2013). Currently, there are four SGLT2 inhibitors marketed in Europe (dapagliflozin (Plosker 2012; Kasichayanula et al. 2014), canagliflozin (Lamos et al. 2013), empagliflozin (Scheen 2014; Scott 2014), and ertugliflozin (Nauck 2014)). This group of drugs is distinguished from other antihyperglycemics in a unique mechanism of action that is based on an increasing glucose excretion in the urine through a reduction in renal reabsorption (Ndefo et al. 2015). This group of drugs is characterized by a high safety profile because their mechanism of action is independent of the function of beta-cells and insulin pathways, which reduces the risk of hypoglycemia in patients (Lin et al. 2014). SGLT2 inhibitors show in addition to beneficial metabolic actions, such as improvements in fasting plasma glucose and lowering blood pressure or body weight; they also have a positive effect on the cardiovascular system and central nervous system (CNS) (Abdelgadir et al. 2018; Al Hamed and Elewa [2020]; McMurray et al. 2019; Sa-Nguanmoo et al. 2017). Studies show that sodium-glucose co-transport 2 inhibitors (SGLT2i) have a surprising advantage over other anti-diabetic drugs due to their anti-inflammatory effects within the coronary and cerebral vessels (Liu et al. 2021; Paolisso et al. 2022; Heimke et al.2022). Heimke et al. [ 2022] showed that empagliflozin can reduce LPS-activated inflammation, which is characterized by reduced expression of the pro-inflammatory mediators Nos2, IL6, TNF, and Il1b, by inhibiting the ERK$\frac{1}{2}$-MAP-kinase pathway in microglia. In addition, SGLT2i prevents cognitive decline and protects synaptic plasticity in the hippocampus (Sa-Nguanmoo et al. 2017). Furthermore, these inhibitors may limit the maturation and secretion of the pro-inflammatory cytokines IL-1β and IL-18 by modulating in microglia the NLRP3 inflammasome, a key pathway in the development of AD (Sim et al. 2021; Kim et al.2020).
There is an increasing number of reports suggesting that inhibition of SGLT2 may prevent or even ameliorate impairment of CNS (Enerson and Drewes 2006; Patrone et al. 2014). Even though the initial data indicated that SGLT2 is not expressed in the brain and the therapeutic benefit on cognitive function may be due to the peripheral action of these drugs, recent studies have confirmed SGLT-2 expression in many areas of the brain (hippocampus, cerebellum, etc.), responsible for learning processes, food intake, or glucose homeostasis (Nguyen et al. 2020; Shah et al. 2012; Hummel et al. 2022; Koepsell 2020; Pawlos et al.2021; Oerter et al.2019). Furthermore, Cinti et al. [ 2017] showed that ertugliflozin as well as empagliflozin and dapagliflozin studied in this work are characterized by the most selective SGLT2 receptor inhibition potential among all drugs in this group. A case–control study by Wium-Andersen et al. [ 2019] showed that SGLT2 inhibitors were associated with a lower risk of dementia in treated patients. Also, Siao et al. [ 2022] noted that patients with T2DM treated with SGLT2 inhibitors had a low rate of diabetic complications as well as an $11\%$ lower risk of dementia development compared with patients not using this group of drugs. Moreover, Mui et al. [ 2021] reported that diabetic patients taking SGLT2 inhibitors for 5 years had a lower incidence of dementia than those using DPP-4i during the same period. Beneficial mechanisms of SGLT2i also include reduction of pro-inflammatory cytokines, reduction of oxidative stress, reduction of glomerular hyperfiltration, inhibition of advanced glycation end-product signaling (McMurray et al. 2019; Nauck 2014; Yaribeygi et al. 2020; Lee et al. 2019; Brauer et al. 2020). SGLT2i has been shown to improve brain insulin sensitivity in obese rats by reducing inflammation, reducing oxidative stress, with the end result being a strong increase in hippocampal synaptic plasticity (Sa-Nguanmoo et al.2017). Amin et al. [ 2020] supported this, demonstrating that empagliflozin decreased cerebral infarct volume, suppressed neuroinflammation and oxidative stress, as well as reduced neuronal apoptosis in brain tissues of hyperglycemic I/R-injured rats.
In addition to the direct mechanisms of SGLT2i action on CNS, there is increasing evidence pointing to the participation of this group of compounds in the improvement of cognitive functions by the inhibition of acetylcholinesterase activity and increasing the acetylcholine levels (Pawlos et al. 2021; Shaikh et al. 2016; Tahara et al. 2016; Rizvi et al. 2014), or impact on the accumulation of beta-amyloid and neurofibrillary tangles (Shaikh et al.2016; Hierro-Bujalance et al. 2020; Wiciński et al.2020; Esterline et al. 2020).
Esterline et al. [ 2020] showed that SGLT inhibition can modulate APP level and production of Aβ, having a key role not only in AD development but also in T2DM. Wiciński et al. [ 2020] showed that SGLT2 inhibitors reduced the accumulation of Aβ in the cortical region of AD-T2DM mice (APP/PS1xdb/db mice) and brain atrophy. Hierro-Bujalance et al.[2020] showed that empagliflozin (EMP) reduced senile plaque density and the levels of soluble and insoluble amyloid β (Aβ) in the cortex and hippocampus of treated mice in APP/PS1xd/db model (model resembling actual AD pathology). In addition, recent reports also indicate the participation of SGLT2i in the restoration of mTOR signaling, which can be very important in preventing or even reducing the progress of neurodegenerative diseases (Rizzo et al. 2022; Stanciu et al. 2021; Pawlos et al.2021).
Moreover, last studies show that treatment with SGLT2i (especially empagliflozin) may have a beneficial effect on cerebral BDNF, a key protein promoting memory and survival of neurons, consequently inhibiting the progression of cognitive disorders (Abdelgadir et al. 2018; Lin et al. 2014, Pawlos et al.2021).
Considering the aforementioned reports, we aimed to check whether empagliflozin and dapagliflozin affect the brain cytokine profile (IL1, IL6, and TNFα) and the levels of proteins such as BDNF, NT3, APP, and HIF1α. We also assessed the expression of genes (App, Bdnf, Snca) involved in the control of neuronal proliferation, plasticity, and memory in the prefrontal cortex and hippocampus of treated mice. In the present study, we intended to examine whether the investigated drugs would be able to ameliorate cognitive impairment by modulating neurochemical parameters and mRNA levels of Snca, Bdnf, and App in the brain. Moreover, we checked whether SGLT2i can mediate the degradation of APP involved in dementia that occurs in T2DM, as well as in AD. This is an intriguing assumption since most of the research on the effects of the SGLT2 inhibitor to date has focused on the effects on the kidneys.
## Animals
CD-1 male mice (seven weeks old, 22–25 g) were obtained from a licensed breeder’s animal facility, Experimental Medicine Centre (EMC), Medical University of Lublin, Poland (077—EMC number in Lublin in the Breeders’ Register kept by the Minister of Science and Higher Education, Poland). The animals were housed at 4 individuals per cage with free access to water and food and were kept under constant temperature (20–21 °C ± 1 °C) and humidity (60 ± $10\%$) and a 12-h light/dark cycle. The number of animals was 8 per group. Animal maintenance and treatments were performed in accordance with binding European standards related to the experimental studies on animal models (Act from January 15, 2015, on the Protection of Animals Used for Scientific or Educational Purposes; Directive $\frac{2010}{63}$/EU of the European Parliament and of the council of 22 September 2010 on the protection of animals used for scientific purposes). Procedures were also approved by the Local Ethics Committee at the University of Life Science in Lublin (No. $\frac{43}{2018}$, Lublin, Poland). All activities were carried out by qualified staff; the animals were under the constant supervision of the veterinarian; all efforts were made to minimize the anxiety and suffering of mice. The total number of animals was estimated in accordance with the requirements of statistical analyses, the Three Rs (3Rs), and the ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments).
## Drugs and chemicals
In the experiment, we used streptozotocin (≥ $98\%$ HPLC, Sigma-Aldrich, Munich, Germany) which was freshly prepared in citrate buffer (0.01 M, pH = 4.5), crystalline fructose (Biomus, Lublin, Poland); empagliflozin (Jardiance, Boehringer Ingelheim International GmbH, Germany), and dapagliflozin (Forxiga, AstraZeneca AB, Södertälje, Sweden) dissolved in saline (aqua pro iniectione, Baxter, Lublin, Poland). Sodium citrate was supplied by Biomus Company (Lublin, Poland).
## Experimental design – mouse model of diabetes and drug administration
A mouse model of diabetes was used in the experiment. This model was designed and described in detail in our previous article (Piątkowska-Chmiel et al. 2021). In brief, diabetes was induced for 4 weeks by ad libitum administration of $20\%$ aqueous fructose solution to mice, followed by injection of freshly prepared STZ solution (40 mg/kg body weight, ip) for 5 consecutive days (1 × daily). Animals with blood glucose ≥ 11 mmol/L were considered diabetic, whereas control mice received only citrate buffer (group I: CTL, $$n = 8$$).
In the next stage, randomly selected animals with induced diabetes were assigned to three groups, each containing 8 mice. They were group II (DM): mice with confirmed diabetes; group III (DM-EMP); and group IV (DM-DAP): mice with confirmed diabetes treated with empagliflozin (EMP) or dapagliflozin (DAP) (10 mg/kg/day, po) for 14 days. In this phase of the experiment, animals in the control group (CTL) and those with induced diabetes (DM) received equal volumes of physiologic saline (Scheme 1).Scheme 1Experimental design of mouse model of diabetes and drug administration
## Passive avoidance test
The short-term and long-term memory of the mice was assessed by a passive avoidance (PA) test. On the first day, one hour after the administration of the last dose of drugs, the mice were placed in a two-compartment step-through passive avoidance apparatus. Both parts were separated by a wall with an 8-cm wide passage. The floor of the dark compartment was composed of 2-mm stainless steel rods spaced 1-cm apart and connected to a constant voltage power source. The animal was placed in the bright area, and next, the researcher waited for it to pass into the darkened area of the apparatus. Then, when the hind legs of the mice entered the dark chamber, the guillotine door was closed, and an electrical foot shock (0.6 mA) was delivered through the grid floor for 2 s. This impulse was a negative training stimulus. After 1 h and after 24 h, the second step of the test was performed. The mice were placed back in the bright compartment to check whether they avoided entering the dark chamber. This time was defined as the latency time, which was recorded for up to 180 s.
## The novel object recognition test
The novel object recognition (NOR) test is an efficient tool for testing learning and different types of memory in mice through manipulation of the retention interval, i.e., the amount of time elapsed between the training and the testing phase. This test allows the examination of the visual and tactile properties of the explored objects rather than their spatial. The test was carried out based on the method previously described (Piątkowska-Chmiel et al. 2021). A novel object recognition test was conducted in a 40 × 40 × 40 cm white-colored wood box. The test consisted of 4 stages, 5 min each. On the first day, the mice were kept in the empty box to familiarize themselves with the environment (habituation phase). On the second experimental day, mice were allowed to examine two identical objects for 5 min (training phase). Wooden cubes of various shapes (oval, rectangular, or triangular pyramids) were used as objects. After the 1-h break, the testing phase begins. One of the objects was replaced with a new one, and then the time spent by mice in contact with the new object was measured for the different experimental groups to compare the cognitive performance. After 24 h, the animals were placed again in the wood box for 5 min with two objects (familiar and new objects) – second testing phase. As before, the time for animals’ interactions with individual blocks was measured.
The obtained data were calculated and presented as recognition index (%) = (time of exploring the novel object/total exploration time) × 100 (1st, 2nd testing day).
## Neurochemical analysis
One day following the behavioral tests, mice were killed by decapitation. On the day of decapitation, the brain from each animal was removed, and immediately, the prefrontal cortex and hippocampus were isolated for determining the levels of interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor α (TNF-α), brain-derived neurotrophic factor (BDNF) protein, neurotrophin-4 (NT4), hypoxia-inducible factor 1α (HIF1α), β-amyloid precursor protein (APP), as well as gene expression of App, Snca, and Bdnf.
## Brain samples preparation
Briefly, after isolation, the tissues were rinsed in ice-cold PBS to remove excess blood thoroughly and weighed. Then, the tissues were homogenized in fresh lysis buffer (w:$v = 1$:50) on ice. The resulting suspension was sonicated with an ultrasonic cell disrupter until the clear solution. Next, homogenates were centrifuged at 10,000 g for 5 min at 4 °C to obtain supernatants, which were stored at − 20 °C until use. Total protein concentrations for all homogenates were assayed using the Bradford method (Bradford 1976).
## Measurement of cytokines’ and proteins’ (BDNF, NT4, APP, HIF1α) levels
The concentrations of cytokines in supernatants were assessed by enzyme-linked immunosorbent assay (ELISA kits for mice: interleukin IL-1β, IL-6, TNFα, BDNF, NT4, APP, and HIF1α; Cloud-Clone Corp., Houston, TX, USA). Each parameter was determined individually in all samples according to the manufacturer’s protocols. The concentrations of cytokines and proteins such as BDNF, NT4, APP, and HIF1α were determined by comparing the optical density of the samples to the standard curve. Cytokines’ concentrations and the levels of BDNF, NT4, APP, and HIF1α in the prefrontal cortex were expressed in picograms per ml/mg protein.
## RNA extraction and real-time PCR
The analysis of mRNA expression was determined as previously described (Piątkowska-Chmiel et al. 2021). Briefly, the RNA was isolated from both the prefrontal cerebral cortex and hippocampus of mice using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA). The concentration and purity of RNA were measured spectrophotometrically with a NanoDrop MaestroNano spectrophotometer (Maestrogen, Hsinchu, Taiwan). RNA with A$\frac{260}{280}$ ratio ranging between 1.8 and 2.0 was used for further investigations. cDNA was synthesized with a High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, California, USA) according to the manufacturer’s instructions. The mRNA expression levels of App, Bdnf, and *Snca* genes involved in the inflammatory response were measured by a real-time PCR reaction and ΔΔCt method, using Hprt and Tbp as endogenous controls (for details, see Table 1). The reaction was carried out in triplicates using the 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, California, USA) and Fast Probe qPCR Master Mix (2 ×), plus ROX Solution (EURx, Poland) according to the manufacturer’s protocol. The data quality screen based on amplification, Tm, and Ct values was performed to remove any outlier data before ΔΔCt calculations and to determine fold change in mRNA levels. The data were presented as RQ values. Table 1Data on the primers used: gene symbols, assay IDs, gene names, GenBank references sequence accession numbers, and amplicon lengths (bp)Symbol of the geneAssay IDName of the geneRef seqThe length of amplicon (bp)AppAB ID: Mm01344172_m1Amyloid beta (A4) precursor proteinNM_001198823.1NM_001198824.1NM_001198825.1NM_001198826.1NM_007471.3111BdnfAB ID: Mm01334047_m1Brain-derived neurotrophic factorNM_007540.4105HprtAB ID: Mm00446968_m1Hypoxanthine guanine phosphoribosyl transferaseNM_013556.265SncaAB ID: Mm01188700_m1Synuclein, alphaNM_001042451.2NM_009221.267TbpAB ID: Mm00446974_m1TATA box binding proteinNM_013684.3105AB ID, applied biosystems TaqMan GENE EXPRESSION ASSAY ID
## Statistical analysis
All statistical parameters were calculated using GraphPad Prism version 8.0.1 (GraphPad Prism, San Diego, CA, USA). All of the data were analyzed by a one-way ANOVA, followed by Tukey’s post hoc test for analysis of significance. Differences with a p-value less than 0.05 were considered statistically significant. No outliers were removed from the dataset. Data are expressed as mean ± standard error of the mean (SEM).
## Protective effects of empagliflozin and dapagliflozin on diabetes-induced cognitive deficits
Analysis of data showed that the investigated drugs had a moderate impact on short-term memory deficits at levels to be detected by the selected behavioral tests (Fig. 1a–d). As can be seen in Fig. 1a, there was not any significant difference in latency time (the time to enter the dark compartment of the apparatus) determined in the passive avoidance test in the groups of sodium-glucose cotransporter 2 inhibitors-treated mice when compared with diabetic mice ($p \leq 0.05$). The slightly longer latency time was observed in empagliflozin-treated animals (DM-EMP) compared to diabetic mice (DM) in the PA test performed after an hour, but it was not statistically significant (Fig. 1a, $p \leq 0.05$). Whereas the NOR test revealed that empagliflozin (EMP) led to a significant improvement in the short-term memory of treated animals (Fig. 1c, $p \leq 0.05$, F(4.34) = 2.291) as well as long-term memory (Fig. 1d, $p \leq 0.05$, F(4.34) = 3.638). The recognition index (RI) in the DM-EMP group was $59.8\%$ in the case of tests performed after 1 h and $60.7\%$ after 24 h which indicated a significant preference of animals for the novel object as opposed to untreated mice (Fig. 1c,d). For comparison, the recognition index (%) in diabetic mice was significantly below 48.2–$44.5\%$, suggesting that the group of animals had recognition memory deficits. Fig. 1Effect of empagliflozin and dapagliflozin on diabetes-induced neurobehavioral deficits. Cognitive function was determined using a passive avoidance (PA) task (a, b). Novel object recognition (NOR) test was performed to evaluate the memory function (c, d). CTL: control group; DM: mice with confirmed diabetes; DM-EMP: mice with confirmed diabetes treated with empagliflozin (EMP) (10 mg/kg/day, per os) for 14 days; DM-DAP: mice with confirmed diabetes treated with dapagliflozin (DAP) 10 mg/kg/day, per os) for 14 days. * $p \leq 0.05$, **$p \leq 0.01$, as compared with the control group (CTL). # $p \leq 0.05$, ##$p \leq 0.01$ as compared with the untreated diabetic group (DM) (one-way ANOVA, followed by Tukey’s post hoc test). Bars represent means ± SEM, $$n = 8$$/group In turn, treatment with dapagliflozin (DAP) significantly retrieved the long-term memory functions in treated mice which was evidenced by an increase in latency time during the PA test performed after 24 h (Fig. 1b; $p \leq 0.01$, F(4.34) = 5.001; a 1.5-fold increase of the latency) and by a level of recognition index (RI) in NOR test (Fig. 1d, $p \leq 0.05$, F(4.34) = 3.638) in comparison to untreated diabetic mice. The value of the recognition index (%) in treated mice was significantly above $61\%$, which indicates that they were able to distinguish between presented objects, as opposed to untreated mice. For comparison, RI in diabetic mice was significantly below $50\%$ (Fig. 1d, $p \leq 0.05$, F(4.34) = 3.638), suggesting that this group of animals had recognition memory deficits.
It should be highlighted that there was no significant difference in recognition index in the training phase between any of the groups (mean ± SEM).
## Effect of empagliflozin and dapagliflozin on the levels of inflammatory cytokines in the brain of diabetic mice
As shown in Fig. 2a–c, in the diabetic group (DM), the statistically significant changes in the levels of pro-inflammatory cytokines were not recorded with the exception of TNFα compared to healthy animals (CTL).Fig. 2Effect of empagliflozin and dapagliflozin on the brain cytokines profile of diabetic mice. The levels of cytokines, IL-1β (a), IL6 (b), and TNFα (c), in prefrontal cortex homogenates were assessed by using commercially available mouse enzyme-linked immunosorbent assays (ELISA). CTL: control group; DM: mice with confirmed diabetes; DM-EMP: mice with confirmed diabetes treated with empagliflozin (EMP) (10 mg/kg/day, per os) for 14 days; DM-DAP: mice with confirmed diabetes treated with dapagliflozin (DAP) 10 mg/kg/day, per os) for 14 days. Comparisons between groups were made by a one-way ANOVA, followed by Tukey’s post hoc test. Bars in the figures present the means ± SEM, $$n = 8$$/group. # $p \leq 0.05$, ##$p \leq 0.01$ as compared with the untreated diabetic group (DM) Among the tested drugs, the 14-day treatment with dapagliflozin (DAP) affected the concentrations of pro-inflammatory cytokines in the prefrontal cortex of diabetic mice. As shown in Fig. 2a,b, DAP significantly decreased (by about $20\%$) the level of IL-1β and IL6 ($p \leq 0.01$, F(2.94) = 1.878; $p \leq 0.001$, F(2.94) = 2.865, respectively) in comparison to diabetic mice group (DM). In addition, 14-day therapy with EMP and DAP failed to reverse the increased TNFα level in the prefrontal cortex of diabetic mice (Fig. 2c; $p \leq 0.05$).
## Attenuation of cognitive deficits through the restoration of neurotrophins’ levels
As can be seen in Fig. 3a, the mice receiving empagliflozin and dapagliflozin at a dose of 10 mg/day had significantly increased levels of brain BDNF compared with the untreated diabetic mice ($p \leq 0.001$, F(2.94) = 11.437; $p \leq 0.01$, F(2.94) = 11.437, respectively). In addition, a one-way ANOVA with Tukey’s post hoc test, as seen in Fig. 3b, revealed that the fourteen-day therapy with empagliflozin led to an increase in the level of the NT4 protein in the prefrontal cortex of treated mice compared with diabetic animals ($p \leq 0.05$, F(2.94) = 4.382). As can be seen in Fig. 3c, the APP protein level for the EMP-or DAP-treated group was not statistically significant when compared with the untreated diabetic group ($p \leq 0.05$).Fig. 3Effect of empagliflozin and dapagliflozin on the levels of neurotrophins (BDNF, NT4), APP, and HIF1α in brain of diabetic mice. BDNF (a), NT4 (b), APP (c), and HIF1α (d) protein levels in prefrontal cortex homogenates were measured using commercially available mouse enzyme-linked immunosorbent assays (ELISA). DM-EMP: mice with confirmed diabetes treated with empagliflozin (EMP) (10 mg/kg/day, per os) for 14 days; DM-DAP: mice with confirmed diabetes treated with dapagliflozin (DAP) 10 mg/kg/day, per os) for 14 days. Comparisons between groups were made by a one-way ANOVA, followed by Tukey’s post hoc test. ** $p \leq 0.01$, as compared with the control group (CTL). ## $p \leq 0.01$, ###$p \leq 0.001$ as compared with the untreated diabetic group (DM) The results of this study also indicate a significant impact of dapagliflozin on the level of HIF1α in the brain of treated mice compared with the untreated animals (Fig. 3d, $p \leq 0.01$, F(2.94) = 5.405). As can be seen in Fig. 3c,d, fourteen-day therapy with empagliflozin did not show a significant impact on either the APP protein level or the level of HIF1α in the brain of treated mice ($p \leq 0.05$) when compared with diabetic mice.
## Empagliflozin and dapagliflozin significantly improved the mRNA expression levels of neurotrophic factor and neuronal proteins
To examine the effect of empagliflozin and dapagliflozin on the gene expression of App, Bdnf, and Snca, mRNA levels were analyzed by quantitative real-time PCR (qRT-PCR) in the samples of the prefrontal cortex and hippocampus of diabetic mice. As shown in Fig. 4a,b, the administration of empagliflozin and dapagliflozin caused the increased expression of Bdnf in the prefrontal cortex ($p \leq 0.01$, F(2.94) = 17.323; $p \leq 0.001$, F(2.94) = 17.323, respectively) and hippocampus ($p \leq 0.01$, F(2.94) = 30.509; $p \leq 0.001$, F(2.94) = 30.509, respectively). The expression levels of the studied genes were generally lower in the prefrontal cortex and hippocampus of diabetic mice (Fig. 4a–f). The expression of App mRNA, analyzed in the prefrontal cortex by qRT-PCR, was significantly increased in the empagliflozin- or dapagliflozin-treated group in comparison to the diabetic group (Fig. 4c; $p \leq 0.001$, F(2.29) = 42.579; $p \leq 0.05$, F(2.29) = 42.579, respectively). Whereas *App* gene expression level in the hippocampus significantly increased only in the animal group treated with dapagliflozin (Fig. 4d, $p \leq 0.01$, F(2.94) = 4.809) in comparison to diabetic mice. Moreover, we observed a significant increase in *Snca* gene expression level in hippocampal structure in mice treated with dapagliflozin compared with the untreated group (Fig. 4f; $p \leq 0.001$, F(2.94) = 18.633). Whereas empagliflozin had no significant effect on *Snca* gene expression in the above-mentioned mouse brain structure ($p \leq 0.05$), but significantly up-regulated it in the prefrontal cortex. As can be seen in Fig. 4e, the mRNA level of Snca was significantly higher in animals treated with empagliflozin than in the group of diabetes-induced mice ($p \leq 0.001$, F(2.94) = 24.643). In turn, dapagliflozin had no significant effect on the *Snca* gene expression in the prefrontal cortex of treated mice ($p \leq 0.05$).Fig. 4The effect of empagliflozin and dapagliflozin on the mRNA expression of Bdnf, App, and Snca in the brain of diabetic mice. Bdnf (a, b), App (c, d), and Snca (e, f) in the prefrontal cortex (PFC) and hippocampus (HPC) were analyzed by quantitative real-time PCR (qRT-PCR). Data are expressed as the means ± SEM, $$n = 8$$/group. CTL: control group; DM: mice with confirmed diabetes; DM-EMP: mice with confirmed diabetes treated with empagliflozin (EMP) (10 mg/kg/day, per os) for 14 days; DM-DAP: mice with confirmed diabetes treated with dapagliflozin (DAP) 10 mg/kg/day, per os) for 14 days. Comparisons between groups were made by a one-way ANOVA, followed by Tukey’s post hoc test. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ as compared with the control group (CTL). # $p \leq 0.05$, ##$p \leq 0.01$, ###$p \leq 0.001$ as compared with the untreated diabetic group (DM)
## Discussion
Our previous research (Piatkowska-Chmiel et al. 2021; Piątkowska-Chmiel et al. 2022a, 2022b) as well as the results of other scientists (Exalto et al. 2012; Chatterje et al. 2016; Li et al. 2015; Schuh et al. 2011; Jayaraj et al. 2020) confirm the relationship between diabetes and increased risk of cognitive dysfunction, dementia, and even Alzheimer’s disease. The presented study also proves this dependence. Diabetic mice showed significant memory and learning impairment in behavioral tests. Additionally, they were characterized by decreased levels of neurotrophins, the down-regulation of Snca, App, and *Bdnf* gene expression, and the increased concentration of pro-inflammatory TNFα in the brain.
However, after 2 weeks of administration of empagliflozin or dapagliflozin, learning, and memory dysfunctions have been improved in mice with diabetes. Our results are consistent with the observations of other scientists. Studies have proven that the SGLT2 protein is present in some regions of the brain including the cerebellum and hippocampus with complex functions in the CNS (Yu et al.2010, 2013). Both empagliflozin and dapagliflozin revealed an overall improvement in learning and memory in animal models of dementia and T2DM (Sa-Nguanmoo et al. 2017; Lin et al.2014; Hierro-Bujalance et al. 2020; Mousa et al.2023). The studies on neuroimaging and neuropathology showed a reduced risk of developing dementia in EMP-treated patients which may be due to the protective effect of the drug on cerebrovascular endothelial cells (Hayden et al. 2019; Lin et al. 2014; Bello-Chavolla et al.2019). Animals treated with SGLT2i showed histological improvement in neurovascular restructuring which is associated with cognitive decline (Hayden et al. 2019). The data demonstrated that combination therapy of dapagliflozin and liraglutide improved the cognitive function in dietary-induced diabetic mice by the increased number of neurons in the dentate gyrus and synaptophysin (Millar et al.2017). Furthermore, diabetic patients treated with SGLT2 inhibitors had a lower rate of diabetic complications as well as a lower risk of development of dementia compared with patients using other groups of anti-diabetic drugs (Siao et al. 2022; Mui et al. 2021). SGLT2i seem to be more effective in improving hippocampal synaptic plasticity than other drug classes such as dipeptidyl peptidase-4 (DPP4) inhibitors, through reduced oxidative stress, better insulin signaling, and increased synaptic activity in the hippocampus (Sa-Nguanmoo et al. 2017; Hierro-Bujalance et al. 2020). Moreover, Sa-Nguanmoo et al. [ 2017] showed that dapagliflozin had greater efficacy in improving insulin signaling and hippocampal synaptic plasticity than vildagliptin. Significant improvements in learning and memory processes were noted in the treated animals. Arab et al. [ 2021] noted that dapagliflozin counteracted neuronal apoptosis and up-regulated glial cell-derived neurotrophic factor (GDNF) by lowering lipid peroxidation.
The role of neurotrophins in normal neural development or response to traumatic brain injuries is widely described in the literature (Srikanth et al. 2020; Jiao et al. 2016). Therapies that improve neurotrophic factors’ levels could efficiently neutralize the effects of oxidative damage, microglia activation, and apoptosis while promoting neuron regeneration and synaptogenesis. All these actions translate into the improvement of learning and memory formation (Vilar and Mira 2016; Nordvall et al. 2022; Rex et al. 2007). Mice with diabetes given empagliflozin or dapagliflozin showed a significant increase in BDNF and NT4 protein levels in the prefrontal cortex. Although the mechanisms of the pro-cognitive action of SGLT2i have not been fully explained, based on the collected data, it can be hypothesized that restoration of the brain’s neurotrophin levels and gene expression involved in neural plasticity attenuated behavioral deficits observed in diabetic mice in our research. The results of our observations are consistent with those of other scientists. Lin et al. [ 2014] observed attenuation of cerebral oxidative stress and an increase in BDNF in EMP-treatment diabetic mice; these effects were also accompanied by an improvement in cognitive function. Furthermore, at the moment, we know that the observed improvement in cognitive functions in EMP- or DAP-treatment mice is not due to the modulation of the level of molecules such as IL-1beta or IL-6.
Multiple studies have shown that SGLT2i (particularly empagliflozin and luseogliflosin) can improve cognitive functions by reversing cerebrovascular dysfunction in animals and diabetic subjects (Wang and Fan 2019; Wang et al. 2022). It cannot be ruled out that the improvement in the cognitive function we observed in the treated mice is the result of a more complex mechanism of action of these antidiabetic drugs. Some research shows that the neuroprotective effect of SGLT-2is may be related to the increased native GLP-1 concentration which is involved in the control of synaptic plasticity and the course of signaling pathways related to learning and memory (Millar et al. 2017; Yildirim Simsir et al. 2018).
Moreover, in our study, we noted up-regulated gene expression involved in neural plasticity in animals that were EMP- or DAP-treated. In animals treated with EMP, changes in the expression of Bdnf, App, and *Snca* genes were observed mainly in the prefrontal cortex. Whereas in the group of animals treated with DAP, changes in gene expression were noted in both examined brain structures. Even though we cannot pinpoint a specific mechanism involved in the modulation of expression of the above-mentioned genes, we can hypothesize that the differences in pharmacological effects between the tested drugs may be associated with differences in their chemical structure, duration of action, and distribution level (Tahara et al. 2016). On the other hand, we cannot ignore information that SGLT2i can be dual inhibitors of SGLT2 and acetylcholinesterase (AChE) (Shaikh et al. 2016; Arafa et al. 2017). They may be as effective in inhibiting AChE as galanthamine (Panchal et al. 2018). Mice receiving SGLT2i showed significantly lower levels of AChE, which obviously correlated with greater acetylcholine availability and improved cognitive function (Arafa et al. 2017).
Clinical and preclinical studies suggest that diabetes and Alzheimer’s disease may share many biochemical features and signaling pathways. It has been shown that in the course of these diseases may appear some pathologic forms of proteins both on the periphery and in the brain (Götz et al. 2009; Yun et al. 2020; Martinez-Valbuena et al. 2018; Bassil et al. 2017; de Nazareth 2017; Shinohara and Sato 2017; Matsuzaki et al. 2010; Oskarsson et al. 2015). This phenomenon is favored by impaired glucose metabolism and insulin signaling, resistance to insulin-like growth factor (IGF-1), oxidative stress, neuroinflammation, as well as cerebrovascular dysfunction (Gao et al. 2015; Luchsinger et al. 2001; Ou et al.2018). Our data show that the expression of amyloid precursor protein may be reduced in specific brain areas that are known to degenerate in AD. Since APP plays an important role in synapse formation and synaptic plasticity, it is easy to predict that the decrease in expression in diabetic mice has a significant impact on cognitive function. Furthermore, the altered expression of the *App* gene in diabetic animals may affect glucose and insulin homeostasis and thus the functions of the CNS (Unno et al.2020). Recent studies have suggested that antidiabetic drugs may reduce amyloid pathology (Infante-Garcia et al. 2016; Holscher 2014; Michailidis et al. 2022; Ou et al. 2018). Interestingly, the studied group of anti-diabetic drugs increased the level of APP mRNA expression without significant influence on the level of protein in the brain of treated mice. The potential reason for the lack of a correlation between mRNA expression and protein level may be delays in their synthesis. Protein synthesis takes time, so transcript changes affect protein levels but with a time lag, which is also seen in our research (Maier et al.2009; Liu et al.2016). We already know that the control of amyloid precursor protein processing is important not only in the course of Alzheimer’s disease (Eggert et al. 2018) but also in diabetes mellitus (Eggert et al. 2021; Catrina and Zheng 2021). Hierro-Bujalance et al. [ 2020] reported that empagliflozin reduced senile plaque density, with an overall reduction in soluble and insoluble amyloid β levels in the cortex and hippocampus of mice APP/PS1xdb/db. Therefore, APP processing control by sodium-glucose cotransporter 2 inhibitors may play a pivotal role in disease-modifying therapy for Alzheimer’s disease but also diabetes mellitus. We suppose that, as observed by us, increasing brain expression of App in SGLTi-treated mice affected the reduction of the APP turnover rate. Thus, our study partly revealed molecular mechanisms underlying the potential of these drugs for reducing the incidence of dementia and/or AD in people with T2DM.
Research shows that alpha-synuclein (Snca) can also be involved in the pathophysiology of neurodegenerative disorders (Twohig and Nielsen 2019; Dawson and Dawson 2003; Yu et al. 2007) as well as pathological processes in the brain of diabetics. Studies showed that observed in the course of diabetes, the decreased tissue glucose uptake, increased insulin resistance, and accelerated neuronal dysfunction are associated with the strong decline of Snca, Arc, and *Npas4* genes expression levels (Micheli et al. 2021; Piatkowska-Chmiel et al. 2021; Hong et al. 2020). The increase of Snca expression slowed down the development of cognitive decline in diabetic mice, indicating that the brain Snca level could be an important marker of cognitive impairment progression. The studies confirm that the reduced proliferation of neurogenic niches cells, observed physiologically also during aging, is accompanied by low SNCA expression (Micheli et al. 2021). In our research, a decrease of Snca mRNA levels in the hippocampus and prefrontal cortex of diabetic mice was noted in comparison to healthy mice, which was consistent with the results of behavioral tests. Interestingly, after 14 days of treatment with sodium-glucose cotransporter 2 inhibitors, a significant increase in *Snca* gene expression level in brain structures of treated mice was observed when compared with the untreated group. Moreover, the mice characterized by high Snca mRNA expression levels demonstrated improved cognitive function in the PA test and NOR test. Arab et al. [ 2021] showed that dapagliflozin attenuated ROS production, enhanced glial cell lineage-derived neurotrophic factor, preserved dopaminergic neurons, and reduced alpha-synuclein accumulation.
Accumulating evidence suggests that the high glucose levels in diabetes may disrupt the regulation of HIF-1 signaling in tissues, possibly causing complications in the functioning of the nervous system (Catrina et al. 2004), retina (Catrina and Zheng 2021), heart (Marfella et al. 2004), blood vessels (Katavetin et al. 2006), as well as kidney (Gunton 2020). More and more studies show that pharmacological induction of HIF-1 is beneficial for the prevention of the progression of complications in the course of metabolic diseases such as diabetes (Sugahara et al. 2020; Dodd et al. 2018; Rojas et al.2018; Zhu et al. 2019; Zeinivand et al. 2020). The results of our study indicate that dapagliflozin is able to modulate hypoxia-inducible factor 1α (HIF1α) level in the brain of treated mice when compared with the untreated animals. Research proves that the enhanced HIF1α expression facilitates glucose metabolism, counters oxidative stress, and improves cerebral blood flow which ultimately contributes to neuronal cell protection and improved cognitive function (Soucek et al. 2003; Silva et al. 2013). Furthermore, HIF1α can also downregulate the receptors for inflammatory cytokines in the hippocampus, reducing neuroinflammation (Xing and Lu 2016). Fine et al. [ 2012] and Sorond et al. [ 2015] proved that the upregulation of HIF1α and its target genes can improve memory by the increase of cerebral blood flow.
In conclusion, our data suggest that diabetes may negatively affect neurons by modulating the neurotrophin levels as well as by the down-regulation of Snca, Bdnf, and *App* genes expression involved in the control of neuronal functions. Thus, the studies reported here proved a crucial and previously undocumented feature of diabetes pathophysiology; 14-day treatment with empagliflozin or dapagliflozin positively influenced neurochemical parameters including neurotrophin levels and neuronal gene expression (Supplementary Table 2). The results of this study are the first step in the evaluation of the role of SGLT2i in the multifactorial process of neuroprotection. Despite these interesting new discoveries, more research is needed to fully understand the molecular mechanisms underlying the beneficial impact of cognitive functions of this group of drugs in the human population. One limitation of this study is that we have no direct evidence that the modulation of the above-mentioned molecules’ levels by EMP and DAP contributes to the amelioration of cognition. In addition, it would be appropriate to investigate whether the achieved concentrations of drugs in the brain are sufficient to suppress SGLT2. We cannot rule out that the observed improvement of cognitive functions in treated animals is the result of synergistic and complex mechanisms of SGLT2i action. They can act by inhibiting SGLT2, inhibiting the acetylcholinesterase enzyme, reducing the level of oxidative stress and inflammation, or limiting the remodeling of brain vessels. In our opinion, SGLT2i are promising candidates in the treatment of neurocognitive disorders.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 21 KB)
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|
---
title: Cocaine-induced plasticity, motivation, and cue responsivity do not differ
in obesity-prone vs obesity-resistant rats; implications for food addiction
authors:
- Anish A. Saraswat
- Lauren G. Longyear
- Alex B. Kawa
- Carrie R. Ferrario
journal: Psychopharmacology
year: 2023
pmcid: PMC10006066
doi: 10.1007/s00213-023-06327-5
license: CC BY 4.0
---
# Cocaine-induced plasticity, motivation, and cue responsivity do not differ in obesity-prone vs obesity-resistant rats; implications for food addiction
## Abstract
### Rationale
Compared to obesity-resistant rats, obesity-prone rats consume more food, work harder to obtain food, show greater motivational responses to food-cues, and show greater striatal plasticity in response to eating sugary/fatty foods. Therefore, it is possible that obesity-prone rats may also be more sensitive to the motivational properties of cocaine and cocaine-paired cues, and to plasticity induced by cocaine.
### Objective
To examine baseline differences in motivation for cocaine and effects of intermittent access (IntA) cocaine self-administration on cocaine motivation, neurobehavioral responsivity to cocaine-paired cues, and locomotor sensitization in male obesity-prone vs obesity-resistant rats.
### Methods
Intravenous cocaine self-administration was used to examine drug-taking and drug-seeking in males. Motivation for cocaine was measured using a within session threshold procedure. Cue-induced c-Fos expression in mesocorticolimbic regions was measured.
### Results
Drug-taking and drug-seeking, cue-induced c-Fos, locomotor sensitization, and preferred level of cocaine consumption (Q0) were similar between obesity-prone and obesity-resistant groups. Maximal responding during demand testing (Rmax) was lower in obesity-prone rats. IntA experience enhanced motivation for cocaine (Pmax) in obesity-prone rats.
### Conclusions
The results do not support robust inherent differences in motivation for cocaine, cue-induced cocaine seeking, or neurobehavioral plasticity induced by IntA in obesity-prone vs obesity-resistant rats. This contrasts with previously established differences seen for food and food cues in these populations and shows that inherent enhancements in motivation for food and food-paired cues do not necessarily transfer to drugs and drug-paired cues.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00213-023-06327-5.
## Introduction
The motivation to seek out and consume potentially addictive substances like cocaine relies on activity of mesocorticolimbic circuits that evolved to direct behavior towards essential reinforcers like food. Thus, it is not surprising that foods, addictive drugs and cues associated with these reinforcers share the ability to enhance activity within mesolimbic regions, including the nucleus accumbens (NAc; Tang et al. 2012). In addition, both food- and drug-seeking behaviors are powerfully influenced by cues associated with them. For example, food cues can elicit approach, spur on food-seeking behavior, and support instrumental responding in and of themselves; the same is true of drug-paired cues (Parkinson et al. 2000; Cardinal et al. 2002; Robinson and Flagel 2009; Derman et al. 2020; Robinson et al. 2015).
Cue-triggered urges to seek out food are often described as an adaptive component of successfully identifying and finding food in the environment, whereas urges triggered by drug-paired cues are often considered inherently aberrant; driving relapse and continued consumption that characterize drug addiction (Kawa et al. 2019a, b; Piazza and Deroche-Gamonet 2013). However, cue-triggered urges to seek out and consume calorie dense foods are stronger in people with obesity, occur in the absence of explicit caloric need, and predict future difficulty maintaining successful weight loss (Martin et al. 2010; Yokum et al. 2011; Murdaugh et al. 2012; Boswell and Kober 2016). This has led to complex and challenging questions about the degree to which neural and behavioral concepts of drug addiction can, or should, be applied to food and common obesity (Dagher 2009; Volkow et al. 2013; Ferrario 2017; Vainik et al. 2020).
Not all individuals are equally susceptible to weight gain and obesity (Albuquerque et al. 2015; Loos and Yeo 2022). This resulted in the development of rodent models that capture susceptibility and resistance to diet-induced obesity, including selectively bred obesity-prone and obesity-resistant rat lines (Giles et al. 2016; Levin et al. 1997; Alonso-Caraballo et al. 2018). Obesity-prone rats eat more than their obesity-resistant counterparts, and work more for food when the cost to obtain food is relatively low (Vollbrecht et al. 2015). In addition, prior to the development of obesity, motivational responses to food cues are stronger in obesity-prone compared to obesity-resistant rats (see Ferrario 2020 for review). Specifically, they show greater Pavlovian conditioned approach, stronger sign- and goal-tracking behavior, and enhanced Pavlovian-to-instrumental transfer in response to food cues compared to obesity-resistant rats (Robinson et al. 2015; Derman and Ferrario 2018; Alonso-Caraballo and Ferrario 2019). This is associated with increased intrinsic excitability of principle neurons in the NAc in obesity-prone vs obesity-resistant rats that reduces the firing threshold of these neurons (Oginsky et al. 2015; Alonso-Caraballo and Ferrario 2019). Furthermore, obesity-prone rats also show enhancements in experience-induced plasticity within the NAc compared to obesity-resistant rats. For example, eating sugary, fatty “junk-foods” leads to persistent increases in calcium-permeable AMPA receptor (CP-AMPAR) transmission and expression within the NAc of obesity-prone but not -resistant rats (Oginsky and Ferrario 2019; Alonso-Caraballo et al. 2021; Nieto et al. 2022), and activity of these receptors has been tightly linked to cue-induced food- and cocaine-seeking behaviors (Crombag et al. 2008; Loweth et al. 2014; Derman and Ferrario 2018).
The pattern of behavior described above, and the overlap between motivational processes governing food- and drug-seeking, suggest that obesity-prone rats may also be more sensitive to the motivational properties of cocaine and cocaine-paired cues, and to plasticity induced by cocaine self-administration. To address these possibilities, we used intravenous cocaine self-administration procedures to examine drug-taking and drug-seeking behavior, and motivation for cocaine before and after intermittent access (IntA) self-administration experience in obesity-prone and obesity-resistant male rats. An IntA procedure was used because it has been shown to be particularly good at inducing addiction-like behaviors and associated neuroplasticity (Algallal et al. 2020; Allain et al. 2021; Calipari et al. 2013, 2014, 2015; Carr et al. 2020; Samaha et al. 2021; Kawa et al. 2016; Kawa and Robinson 2019; Kawa et al. 2019a, b). Motivation in the current study was measured using a within session threshold procedure that captures demand for cocaine (i.e., how consumption changes as a function of price) and the preferred level of consumption when price is negligible (Bentzley et al. 2013). In addition, we examined cue-induced c-Fos protein expression in the medial prefrontal cortex, amygdala, and NAc after IntA, and the induction of locomotor sensitization across IntA in obesity-prone and obesity-resistant groups.
## Methods
General methods are given first, followed by details of each experiment including Ns for each group. See also timelines shown in panel A of Figs. 1, 5, and 7.Fig. 1Experiment 1; acquisition of cocaine self-administration is similar across groups. A Experimental timeline. B Average number of infusions across each session in obesity-prone (OP, $$n = 12$$) and obesity-resistant (OR, $$n = 14$$) groups. C Average active and inactive responding across each session. All data shown as mean ± SEM
## Subjects
Male selectively bred obesity-prone (OP) and obesity-resistant (OR) rats, originally developed by Barry Levin from outbred Sprague Dawley rats were used for all studies (OP $$n = 36$$, OR $$n = 39$$; Levin et al. 1997; see also Bouret et al. 2015, section VI, C2 and Ferrario 2020 for additional information about this model). Rats were bred in house (colonies maintained by the UM Breeding Core), and were group housed on a reverse light–dark cycle (12 h lights off at 08:$\frac{00}{12}$ h lights off at 20:00) in a climate-controlled colony room from weaning through adulthood (~ 50 days old). One week prior to intravenous catheter surgery, rats were handled daily and singly housed for the remainder of the experiment. Given the established sex differences in the acquisition and maintenance of cocaine self-administration and in the effects of intermittent access on behavior (Quigley et al. 2021; Algallal et al. 2020; Kawa and Robinson 2019) only one sex (males) was used in the current study. Future studies designed to assess potential differences in females should be conducted. All procedures were approved by the University of Michigan Committee on the Use and Care of Animals in accordance with AAALAC and AVMA guidelines.
## Intravenous catheter surgery
Rats underwent intravenous catheter surgery as described previously (Crombag et al. 2000; Ferrario et al. 2011). Briefly, rats were anesthetized using isoflurane (2–$5\%$ i.h. experiments 1 and 3) or a combination of ketamine (90 mg/kg, i.p.) and xylazine (10 mg/kg, i.p.; experiment 2). A silastic catheter was then inserted into the right jugular vein and passed beneath the skin over the right shoulder and placed in the mid-scapular region. Rats were given the analgesic carprofen immediately prior to surgery and for two days following surgery (5 mg/kg, s.c., one injection per day; Zoetis, NJ). Rats were allowed to recover for at least 5 days before training began. For the first 10 days following surgery, catheters were flushed daily with 0.2 ml sterile saline (Baxter, IL) containing 5 mg/ml gentamicin sulfate (Henry Schein, NY). For the remainder of the study, catheters were flushed with 0.2 ml sterile saline (Baxter, IL; experiments 1 and 2) or 0.2 mL of saline containing heparin (20 IU/mL; Millipore, MO; experiment 3) to prevent occlusions in the catheter. At the end of the experiment, catheters were tested for patency via intravenous infusion of 0.1 mL of methohexital sodium (10 mg/mL; JHP Pharmaceuticals, NJ). Rats that did not become ataxic immediately following the infusion were considered to have non-patent catheters and were removed from analyses as appropriate.
## Cocaine self-administration
Cocaine HCL was provided by the NIDA drug supply program. All training and testing were conducted in standard operant chambers (22 × 18 × 13 cm) located within sound-attenuating cabinets, with a ventilating fan (Med Associates, St Albans, VT, USA). Each chamber contained 2 nose poke ports located on the left and right side of one wall. One port was designated active and the other inactive (left/right counter-balanced across chambers). A red house light was located at the top center of the wall opposite the nose pokes, and a cue light was located above the active nose port. The house light was illuminated when rats were placed in the operant chambers and was turned off at the start of each session. During acquisition and IntA self-administration sessions, a fixed ratio 1 (FR1) schedule was used in which each response in the active port resulted in the delivery of an intravenous infusion of cocaine. Each infusion was paired with the illumination of a cue light for 5 s. During acquisition, there was a time-out period during which additional active responses were not reinforced for the duration the cue light was illuminated and for 15 s after (20 s in total). During IntA and threshold testing, another infusion could not be triggered until the cue light turned off (5 s). During initial training and IntA sessions, cocaine (0.4 mg/kg/infusion) was delivered in 50 μL over 2.6 s. Responses in the inactive port were recorded but had no consequence. The number of responses in the active and inactive ports, and infusions was recorded throughout each session using the Med Associates software. For all studies, rats were given one session per day, 7 days per week.
## Within session threshold procedure
Motivation for cocaine and preferred levels of cocaine intake were assessed using a within-session threshold procedure (Kawa et al. 2016; Carr et al. 2020). Briefly, during each session a response in the active port resulted in the delivery of an intravenous infusion of cocaine on an FR1 schedule. The dose of cocaine available decreased by a ¼ log every 10 min for a total of 120 min as follows (experiment 1, mg/kg/infusion: 1.28 0.72, 0.4, 0.23, 0.13, 0.072, 0.04, 0.023, 0.013, 0.007, 0.004, 0.002; experiment 2, μg/infusion: 383.5, 215.6, 121.3, 68.2, 38.3, 21.6, 12.1, 6.8, 3.8, 2.2, and 1.2). This effectively increases the price (i.e., work) needed to maintain the preferred level of cocaine consumption. Cocaine was given at specific concentrations, rather than adjusting to body weight, in the second experiment in an attempt to improve control and the potential to detect group differences. During these sessions, each infusion was paired with the illumination of the cue light for 5 s. Rats were tested in this way for at least 5 sessions and no more than 10 sessions, depending on the quality of curve fitting that resulted from these sessions (see demand analysis below).
## Intermittent access
Procedures here were similar to those described previously (Zimmer et al. 2012; Kawa et al. 2016; Carr et al. 2020). Each IntA session is comprised of 5-min drug available (DA) periods interspersed with 25-min no drug available (NDA) periods. This results in a binge-like pattern of cocaine intake, where brain concentrations of cocaine spike to a peak during DA periods and fall to baseline during the NDA periods prior to the start of the next DA period (Zimmer et al. 2012; Kawa et al. 2016; Kawa et al. 2019a, b). DA periods were signaled by turning off the house light, whereas NDA periods were signaled by illumination of the house light. During IntA sessions, each infusion was paired with the illumination of a cue light (5 s), and rats could take another infusion after the cue light turned off. After the 5-min DA period ended, the house light turned on, signaling the onset of a 25-min NDA period, during which active and inactive nose pokes were recorded but had no consequences. After the NDA period, the house light was again extinguished, signaling the onset of the next DA period. In experiments 1 and 2, each session consisted of 8 DA periods and 8 NDA periods (4 h total). In experiment 3, each session consisted of 7 DA periods and 7 NDA periods followed by an 8th DA period in which a single self-administered infusion was followed by the final NDA period. This was done to assess locomotor activity, see below for additional details.
## Cocaine-seeking test
Fourteen days after the last IntA self-administration session (WD14) were given a single “cocaine-seeking” test (60 min). Rats were placed into the operant chambers with the house light illuminated (as during the NDA period of IntA), and the start of the test was signaled by extinguishing the house light. During this test, no cocaine was given but responses in the active nose port resulted in presentation of the previously drug-paired cue light (5 s) alone. Responses in the inactive nose port were recorded but had no consequences.
Figure 4 shows active and inactive nose poke responding during the WD14 cocaine-seeking testing. No cocaine was given, but responses in the active nose port resulted in presentation of the previously cocaine paired cue. Figure 4B shows the time course of active responding during this test. Both OP and OR groups maintained active vs inactive discrimination, responding more on the active nose poke than the inactive but no group differences were found (Fig. 4A; two-way RM ANOVA; main effect of nose poke port; F[1, 22] = 149.7, $p \leq 0.01$; no main effect of strain; F[1, 22] = 0.67, $$p \leq 0.42$$, no significant port × strain interaction; F[1, 22] = 0.02, $$p \leq 0.88$$).Fig. 4Cocaine-seeking when responding was reinforced by the cue alone was similar between groups. A Total active and inactive responses in obesity-prone ($$n = 12$$) and obesity-resistant ($$n = 12$$) groups. B Time course of active responding in both groups. * = significant main effect of nose poke port
## c-Fos induction and immunohistochemistry
c-Fos expression induced by re-exposure to the previously cocaine-paired cue was assessed 14 days after the last IntA self-administration session. After 14 days of abstinence, rats were placed into the operant chambers with the house light illuminated, and the start of the test was signaled by turning off the house light. The session consisted of 10 presentations of the drug-paired cue once every minute for 5 s. Responses on the previously active and inactive nose poke were recorded but had no consequences. An additional set of drug-naïve rats were given 10 sessions in which the cue was presented as described above. The 10th session was conducted side-by-side with testing of the IntA group and brains were collected from all rats 90 min after the start of the session. Note, separate cohorts of rats were used for the cocaine-seeking test described above, and c-Fos induction (see also experiments 1 and 2 below).
Procedures for processing brains for immunohistochemistry (IHC) were similar to those described previously (Derman et al. 2020). Ninety minutes after the onset of the c-Fos induction test described above, rats were given sodium pentobarbital (Henry Schein, NY, 3.9 mg/mL, i.p.) and transcardially perfused with phosphate-buffered saline (PBS) followed by $4\%$ paraformaldehyde (PFA, Sigma-Aldrich, MO) in PBS. Brains were extracted, placed into a $\frac{50}{50}$ mix of $4\%$ PFA and $30\%$ sucrose solution, then stored at 4 °C. Twenty-four to forty-eight hours later, brains were transferred to a $30\%$ sucrose solution and stored at 4 °C for 2–3 days. Next, coronal sections containing prefrontal cortex, striatum, and amygdala (30 μm) were made using a cryostat (Leica, Wetzlar, Germany) and stored at − 20 °C in cryoprotectant ($50\%$ 0.1 M phosphate buffer, $30\%$ ethylene glycol, $30\%$ sucrose) until they were processed for IHC.
All IHC was conducted at room temperature in free-floating sections placed on an orbital shaker (Talboys, NJ). Expression of c-Fos protein was determined using a Rabbit anti c-Fos primary antibody (CST, MA, #2250S) and an Alexa Fluor 555 Goat-anti-Rabbit secondary antibody (Invitrogen, MA, A32732), both at a 1:2000 dilution. All steps were conducted at room temperature. Briefly, sections were washed (12 × 10 min in 1X PBS), blocked (1.5 h, 1X PBS with $5\%$ normal goat serum [MP Biomedicals, CA], $0.04\%$ Triton-X [Sigma Aldrich, MO]), and incubated with primary antibody in blocking solution overnight (15–20 h). Slices were washed again (5 × 5 min in 1X PBS) and then incubated with secondary antibody in blocking solution (1.5 h). After a final set of washes (5 × 5 min in 1X PBS), tissue was mounted on Superfrost Plus microscope slides (Fisherbrand, MA) and cover-slipped with Prolong Gold and the nuclear fluorescent stain DAPI in the mounting medium (Invitrogen, MA, P36931) so that c-Fos counts could be normalized to total cell counts. An upright epifluorescence manual system microscope (Olympus, Tokyo, Japan, BX43) was used to image sections with an XM10 camera. Images were taken at 2 × and 10 × using cellSens software. Finally, c-Fos expression was quantified using standard background subtraction and thresholding procedures in ImageJ. The total number of cells was determined by counting the number of DAPI stained cells. The number of c-Fos positive cells were determined within the prelimbic and infralimbic regions of the medial prefrontal cortex (mPFC; AP: + 3.00 mm from bregma) and the NAc core and shell (AP: + 1.92 mm from bregma). Within the amygdala, c-Fos was measured in the anterior and posterior portions of the basolateral nucleus and the lateral amygdaloid nucleus (AP: − 2.5 mm from bregma). The number of c-Fos positive cells were normalized to the total number of cells within the sample region. Quantification was performed blind to group (OP vs OR) and treatment (naïve vs drug-experienced).
## Locomotor activity
Locomotor activity was examined using automated beam breaks (Med Associates, VT) within the self-administration chambers. Locomotor activity in response to a single self-administered infusion of cocaine (1.2 mg/kg, i.v.) was assessed after completion of the last NDA period of each IntA session (house light off during the infusion, as in DA periods), and in separate dedicated locomotor test sessions that were not immediately preceded by IntA. These separate test sessions took place within the self-administration chambers the day before IntA began and the day after IntA concluded. During these dedicated locomotor tests, rats were placed into the operant chamber with the house light illuminated. After 30 min of habituation, the house light was turned off and animals were allowed to self-administer a single infusion of cocaine. Following the single infusion, the house light was again illuminated and locomotor activity was measured via beam breaks for 25 min. For both procedures, cocaine infusion was accompanied by the illumination of the cue light (5 s).
## Statistics and analyses
Behavioral experiments were designed for within-subject and between-subject comparisons. Data were processed and organized with Microsoft Excel (Version 16.16.16) and statistical analyses were performed using GraphPad Prism (Version 9.0.0). Data were then analyzed using Student’s t-tests, one-way ANOVAs, repeated measures ANOVAs, GLMs, and mixed-effects models followed by Holm-Sidak’s and Tukey’s multiple comparisons tests as appropriate. Depending on the model selected, the degrees of freedom may have been adjusted to a non-integer value.
Data from within session threshold testing were examined using established curve fitting approaches (Newman and Ferrario 2020) to extract three main metrics of cocaine demand: Q0, the preferred level of consumption when the price of cocaine is negligible; Pmax, the price at which maximum work is performed; and Rmax, the maximum work performed to obtain cocaine regardless of price. Fundamental to the assessment of cocaine demand (i.e., the consumption of cocaine as a function of the price to obtain it) is that cocaine consumption must be relatively flat when cost is low, and then decline to zero as cost increases (Bentzley et al. 2013; Hursh and Silberberg 2008). For demand analysis, data from each within threshold test session were plotted for individual rats and only sessions that followed the pattern described above were included in demand analysis (see also Fig. 3A for examples). If an animal had no sessions that followed the pattern, they were excluded from the demand analysis; this was the case for 1 out of 12 rats in the OP group and 2 out of 12 rats in the OR group at the Post IntA time point only; experiment 1. Data from sessions were fit using the model described by Newman and Ferrario [2020] and fits were then confirmed visually. Pmax, Rmax, and Q0 values obtained from these fits were then averaged across several sessions for each rat.
## Experiment 1
The first set of studies set out to evaluate potential inherent differences in cocaine demand between OP and OR groups, the effect of IntA cocaine self-administration on cocaine demand in these groups, and potential differences in cocaine-seeking following withdrawal. For these studies, OP ($$n = 12$$) and OR ($$n = 14$$) groups were trained to self-administer cocaine as described above (6 sessions; 1 h or a maximum of 20 infusions/session). Next, baseline cocaine demand was assessed using within session threshold procedure described above (5–10 sessions). This was followed by IntA self-administration (12 sessions) and post cocaine demand assessment (5–10 sessions). All rats then underwent a 14-day period of forced abstinence in which they remained in their home cages followed by a single cocaine-seeking test.
## Experiment 2
The goal of experiment 2 was to further evaluate demand for cocaine before and after IntA, and to examine potential differences in cue-induced c-Fos induction in obesity-prone ($$n = 8$$) vs obesity-resistant ($$n = 8$$) rats. To avoid potential differences in drug exposure and number of CS-US pairings during acquisition, an infusion criterion (IC) procedure was used (Saunders and Robinson 2010; Kawa et al. 2016). Briefly, these sessions terminated once the rat obtained a set number of infusions, or after one hour elapsed. All rats received 2 sessions at IC10, 3 at IC20, and 3 at IC40. Next, baseline cocaine demand was assessed using a within session threshold procedure (5–10 sessions), followed by IntA self-administration (8 sessions), and post IntA threshold testing (2 sessions). Finally, rats underwent a 14-day period of forced abstinence followed by cue-induced c-Fos induction. Additional drug naïve controls were included (OP $$n = 5$$ OR $$n = 5$$), as described above.
## Experiment 3
The goal of this final experiment was to evaluate cocaine-induced locomotor sensitization in obesity-prone ($$n = 11$$) vs obesity-resistant ($$n = 12$$) rats across IntA. Rats here were trained to self-administer cocaine as described in experiment 1. Following acquisition, the acute locomotor response to a single self-administered infusion of cocaine was assessed (Baseline). Next, locomotor activity to a single self-administered infusion of cocaine was assessed at the end of each IntA session (12 sessions total). Finally, locomotor activity was assessed 24 h after the last IntA session (post).
## Self-administration acquisition
Figure 1A shows the timeline for experiment 1 followed by the average number of infusions (Fig. 1B) and active vs inactive responses (Fig. 1C) during initial acquisition. The number of infusions increased similarly in OP and OR groups across the 6 training sessions (Fig. 1B: two-way RM ANOVA; main effect of session: F(2.96, 73.99) = 15.06, $p \leq 0.01$; no main effect of strain: F[1, 25] = 2.43, $$p \leq 0.13$$; no strain × session interaction; F[5, 125] = 1.83, $$p \leq 0.11$$). In addition, both groups learned to discriminate between the active and inactive nose poke ports, with responding on the active port increasing across sessions, and inactive responses remaining low and stable (Fig. 1C: three-way RM ANOVA; main effect of port; F[1, 26] = 40.11, $p \leq 0.01$; main effect of session; F(2.04, 53.11) = 4.86, $$p \leq 0.01$$; significant session × port interaction; F[5, 115] = 3.74, $p \leq 0.01$). No differences in responding or number of infusions were observed between groups.
Figure 5A shows the timeline for experiment 2. To control for initial drug and cue experience during acquisition, an infusion criterion procedure was used. Thus, there were no group differences in initial cocaine intake (data not shown). In addition, both groups learned to discriminate between the active and inactive nose poke ports with responding on the active port increasing across sessions and inactive responding remaining low (data not shown; three-way RM ANOVA; main effect of nose poke port; F[1, 14] = 43.8, $p \leq 0.01$; significant session × nose poke port interaction; F[7,96] = 2.13, $$p \leq 0.04$$) and no differences between groups (three-way RM ANOVA; no main effect of strain; F[1, 14] = 0.76, $$p \leq 0.40$$). Fig. 5Experiment 2; IntA cocaine self-administration is again similar between groups. A Timeline for experiment 2. B Active responses across DA and NDA periods during the first IntA session in obesity-prone ($$n = 8$$) and obesity-resistant ($$n = 8$$) groups. Neither group showed discrimination between the DA and NDA periods. C Active responses across DA and NDA periods during the last IntA session. By the 8th session, both groups showed strong discrimination, making more active responses during the DA than the NDA period. D Infusions taken across each IntA session was similar between groups. E Active responses during the first minute of the DA period increased across IntA in both groups. F Active responses during each min of the 5-min DA period during session 1. G Active responses during each min of the 5-min DA period during session 12. While both groups escalated their intake from the first to last session, no group differences were found Figure 7A shows the timeline for experiment 3 followed by the average number of infusions (Fig. 7B) and active vs inactive responses (Fig. 7C) during initial acquisition. The number of infusions increased similarly in OP and OR groups across the 7 training sessions (Fig. 7B: two-way RM ANOVA; main effect of session; F(3.37, 73.66) = 15.59, $p \leq 0.01$; no main effect of strain; F[1, 22] = 0.01, $$p \leq 0.97$$). In addition, both groups learned to discriminate between the active nose and inactive nose poke ports (Fig. 7C: three-way RM ANOVA; main effect of nose poke port; F[1, 22] = 11.5, $p \leq 0.01$), with no group differences in active or inactive responding (Fig. 7C: two-way RM ANOVA; active: no main effect of strain; F[1, 22] = 0.01, $$p \leq 0.97$$; inactive: no main effect of strain; F[1, 22] = 1.13, $$p \leq 0.30$$).Fig. 7Experiment 3; acquisition of cocaine self-administration was similar between groups. A Timeline for experiment 3. B The number of infusions across each session in obesity-prone (OP, $$n = 12$$) and obesity-resistant (OR, $$n = 12$$) groups. C Average active and inactive responding across each session
## IntA self-administration
Figure 2A outlines the pattern of drug available (DA) and no drug available (NDA) periods during an IntA session. During IntA, responses in the inactive port were low and stable throughout all sessions and were similar between OP and OR groups (Supplemental Fig. 1A: three-way RM ANOVA; no main effect of strain; F[1, 24] = 1.81, $$p \leq 0.19$$; no strain × session interaction; F[11, 264] = 0.98, $$p \leq 0.46$$; no strain × port interaction; F[1, 24] = 0.10, $$p \leq 0.76$$; Avg inactive response over 12 sessions = 2–23 responses ± 1.50 SEM). Active responses across each DA and NDA period for the first and last IntA session are shown in Fig. 2B and C, respectively. During the first IntA session, neither group showed strong discrimination between alternating DA and NDA periods (Fig. 2B: two-way RM ANOVA; no main effect of period; F(1.53, 36.6) = 2.19, $$p \leq 0.14$$). However, by session 12, both groups showed strong discrimination, responding more during the DA period than the NDA period (Fig. 2C: two-way RM ANOVA; main effect of period; F(4.31, 103.5) = 20.87, $p \leq 0.01$). In addition, active responding was similar in OP and OR groups, and there were no group differences in the number of infusions taken across IntA sessions (Fig. 2D: two-way RM ANOVA; no main effect of strain; F[1, 24] = 0.04, $$p \leq 0.84$$).Fig. 2IntA cocaine self-administration is similar between groups and results in escalation of drug intake. A Schematic of alternating drug available (DA) and no drug available (NDA) periods during each IntA session. B Active responses across DA and NDA periods during the first IntA session in obesity-prone and obesity-resistant groups. Neither group showed discrimination between the DA and NDA periods. C Active responses across DA and NDA periods during the last IntA session. By the 12th session, both groups showed strong discrimination, making more active responses during the DA than the NDA period. D Average infusions taken across each IntA session was similar between groups. E Active responses during the first minute of the DA period increased across IntA in both groups. F Active responses during each min of the 5-min DA period during session 1. G Active responses during each min of the 5-min DA period during session 12. While both groups escalated their intake from the first to last session, no group differences were found Figure 2E shows the average number of active responses during the first minute of the DA period for each session. Active responses escalated across IntA sessions in both groups (Fig. 2E: two-way RM ANOVA; main effect of session; F(2.44, 58.63) = 10.53, $p \leq 0.01$), and no group differences were found (Fig. 2E: two-way RM ANOVA; no main effect of strain; F[1, 24] = 2.38, $$p \leq 0.14$$; no significant session × strain interaction; F[3, 72] = 1.26, $$p \leq 0.30$$). In addition, active responding was greatest during the first minute (M1) of the DA period compared to the remaining 4 min during both session 1 and session 12 for both groups (Fig. 2E, F). Overall, IntA produced escalation and binge-like patterns of cocaine intake that was similar in OP and OR groups.
During IntA, responses in the inactive port remained low and stable throughout all 8 sessions and there were no differences between OP and OR groups (Supplemental Fig. 1B; three-way RM ANOVA; no main effect of strain; F[1, 97] = 0.63, $$p \leq 0.43$$; no strain × session interaction; F[17, 97] = 1.46, $$p \leq 0.19$$; no strain × port interaction; F[1, 97] = 0.15, $$p \leq 0.23$$; Avg inactive response across 12 sessions = 0.25–11.13 responses ± 1 SEM). As in Exp. 1, neither group discriminated between the DA and NDA periods during the first IntA session (Fig. 5B: two-way RM ANOVA; no main effect of period; F(2.86, 39.99) = 1.52, $$p \leq 0.23$$), but showed discrimination between the two periods during the last session, responding more during the DA versus the NDA period (Fig. 5C: two-way RM ANOVA; main effect of period; F(2.12, 29.61) = 12.07, $p \leq 0.01$). There were no group differences in the number of infusions taken across IntA sessions (Fig. 5D: two-way RM ANOVA; no main effect of strain; F[1, 14] = 1.90, $$p \leq 0.19$$; no session × strain interaction; F[7, 97] = 1.59, $$p \leq 0.15$$).
Figure 5E shows the average number of active responses during the first minute of the DA period for each session. Active responses escalated across IntA sessions in both groups, and no group differences were found (Fig. 5E: two-way RM ANOVA; main effect of session; F(1.60, 22.38) = 6.56, $p \leq 0.01$; no main effect of strain; F[1, 14] = 0.32, $$p \leq 0.58$$; no group × session interaction; F[2, 23] = 0.48, $$p \leq 0.62$$). In addition, active responding was the greatest during the first minute (M1) of the DA period compared to the remaining 4 min during both the first and last IntA session for both groups (Fig. 5F, G). Overall, the pattern of drug intake and active responding was similar to that seen in experiment 1, with rats in both groups escalating their drug intake and consuming the majority of cocaine within the first minute of each DA period.
During IntA, responses in the inactive port were low and stable throughout all 12 sessions and there were no differences in responding between OP and OR groups (Supplemental Fig. 1C: three-way RM ANOVA; no main effect of strain; F[1, 21] = 0.82, $$p \leq 0.38$$; no strain × session interaction; F[11, 231] = 0.99, $$p \leq 0.45$$; no strain × nose poke port interaction; F[1, 21] = 0.21, $$p \leq 0.65$$; Average inactive response across 12 sessions = 2–28 responses ± 1.55 SEM). Neither group discriminated between the DA and the NDA period during the first IntA session (Fig. 8A: two-way RM ANOVA; no main effect of period; F(1.81, 36.27) = 1.19, $$p \leq 0.31$$). However, by session 12, both groups showed clear discrimination, responding more on the active nose poke during the DA than the NDA period (Fig. 8B: two-way RM ANOVA; main effect of period; F(3.12, 65.33) = 3.96, $$p \leq 0.01$$). In addition, there were no group differences in the number of infusions taken during IntA (Fig. 8C: two-way RM ANOVA; no main effect of strain; F[1, 22] = 2.17, $$p \leq 0.16$$). Figure 8D shows the average number of active responses during the first minute of the DA period for each session. As above, both groups escalated their drug intake during the first minute of the DA period across the 12 sessions (Fig. 8D: two-way RM ANOVA; main effect of session; F(2.52, 50.29) = 8.92, $p \leq 0.01$; no main effect of strain; F[1, 21] = 0.01, $$p \leq 0.97$$). Furthermore, active responding was greatest during the first minute (M1) of the DA period compared to the remaining 4 min during both session 1 and session 12 for both groups (Fig. 8E, F).Fig. 8IntA cocaine self-administration results in locomotor sensitization that is similar between groups. A Active responses across DA and NDA periods during the first IntA session in obesity-prone ($$n = 11$$) and obesity-resistant ($$n = 12$$) groups. B Active responses across DA and NDA periods during the last IntA session. Again, both groups showed strong discrimination, making more active responses during the DA than the NDA period. C Infusions taken across each IntA session was similar between groups. D Active responses during the first minute of the DA period increased across IntA in both groups. E Active responses during each min of the 5-min DA period during session 1. F Active responses during each min of the 5-min DA period during session 12. While both groups escalated their intake from the first to last session, no group differences were found. G Locomotor activity in response to a single self-administered infusion of cocaine taken at the end of each IntA session. H Total locomotor activity in response to a single self-administered infusion of cocaine at the end of the first and last Int A session. IntA experience produces progressive increase in locomotor activity in response to the same dose of cocaine (1.2 mg/kg, i.v.). * = significant main effect of session
## Demand for cocaine
Figure 3A shows cocaine intake (closed circles) and the corresponding fitted curve (blue line) from one demand session in two separate rats. In the upper panel, cocaine consumption was relatively flat at low cost and declined to zero as cost increased. Thus, curve fitting can provide a reliable measure of Q0 and Pmax. The lower panel shows data from a demand session that did not follow this pattern, and thus cannot be used to determine these metrics. The majority of rats tested showed a typical demand curve for 3 or 4 out of 5 demand sessions. This is expected, as there is some learning that takes place when the session parameters change (e.g., no NDA period, changes in dose available). There were a small minority of rats ($\frac{1}{12}$ OP and $\frac{2}{12}$ OR rats) that never showed the expected demand pattern despite additional testing (up to 10 sessions). Data from these animals was not included in analyses (see also methods).Fig. 3Cocaine demand testing using the within session threshold procedure. A Example cocaine intake (closed circles) and the corresponding fitted curve (green line) from one demand session in two separate rats. In the upper panel, cocaine consumption was relatively flat at low cost and declined to zero as cost increased and thus curve fitting can provide a reliable measure of Q0, Pmax, and Rmax. The lower panel shows intake from a demand session that did not follow this pattern, and thus cannot be used to determine these metrics. B Motivation to take cocaine (Pmax) at baseline (BL; OP $$n = 12$$; OR $$n = 14$$) and after 12 sessions of IntA cocaine self-administration (post; OP $$n = 11$$; OR $$n = 10$$). Pmax was increased following IntA in the obesity-prone, but not obesity-resistant group. C The preferred level of cocaine intake (Q0) did not differ between groups but increased in both groups following IntA. D Rmax was greater in the obesity-resistant vs obesity-prone group at both timepoints and was not affected by IntA. * = Sidak’s post-test $p \leq 0.05$; ** = significant main effect of test; *** = significant main effect of strain Figure 3B shows Pmax before (baseline; BL) and after IntA (post). Motivation to take cocaine (Pmax) did not differ between groups at baseline, but was significantly increased following IntA in OP, but not OR groups (Fig. 3B: two-way RM ANOVA; significant test × strain interaction; F[1, 18] = 9.19, $p \leq 0.01$, Sidak’s post-test; OP vs OR baseline, $$p \leq 0.42$$; OP baseline vs post IntA, $p \leq 0.01$, OR baseline vs post IntA, $$p \leq 0.80$$). Consistent with this, Pmax was also greater in OP vs OR groups after IntA (Fig. 3b: Sidak’s post-test; OP vs OR post, $$p \leq 0.04$$).
Figure 3C shows the preferred level of consumption (Q0). No group differences in Q0 were found, but IntA significantly increased Q0 in both groups (Fig. 3C: two-way RM ANOVA; no main effect of strain; F[1, 25] = 0.12, $$p \leq 0.73$$; significant main effect of test; F[1, 19] = 24.81, $p \leq 0.01$, no significant test × strain interaction; F[1, 19] = 0.78, $$p \leq 0.39$$).
Figure 3D shows the maximum work performed to obtain cocaine regardless of price (Rmax). Rmax was significantly greater in OR vs OP groups at both timepoints (Fig. 3D: two-way RM ANOVA; main effect of strain; F[1, 25] = 4.57, $$p \leq 0.04$$), but did not change as a function of IntA in either group (Fig. 3D: two-way RM ANOVA; no main effect of test; F[1, 18] = 3.49, $$p \leq 0.08$$, no significant test × strain interaction; F[1,18] = 0.40, $$p \leq 0.54$$). In sum, demand testing revealed enhancements in motivation for cocaine in OP, but not OR groups following IntA; this was not related to the preferred level of cocaine consumption or maximal active nose poke responding.
Rats in Exp. 2 also underwent demand testing before and after IntA experience. For this set of studies cocaine concentration was not adjusted for body weight, and instead was given at decreasing concentration to vary price (see methods). In addition, rats only received two sessions of demand testing after IntA. Unfortunately, this approach resulted in behavior that did not show the expected consumption pattern (flat consumption at low price that declines to zero as price increases). Thus, these data could not be used to evaluate motivation as planned and are therefore not included here. It is important to note though that cocaine intake was similar across groups during these sessions.
## Cue-induced c-Fos expression
Fourteen days after the last cocaine self-administration session, cue-induced c-Fos expression was assessed in the NAc, amygdala and mPFC following non-contingent exposure to the previously cocaine-paired cue. Controls were drug-naive and exposed to non-contingent cue presentations (see methods). Rats were free to respond in the nosepoke ports during this session, but responses in the previously active port had no consequences (see also Supplemental Fig. 2). Effects on c-Fos expression were similar in sub-regions of the mPFC and amygdala and were therefore collapsed to simplify data presentation. Figure 6 shows c-Fos expression normalized to total cell number in the NAc core (A), NAc shell (B), amygdala (C) and mPFC (D). No differences between OP and OR groups were found for any measure. However, cue presentation resulted in greater c-Fos expression in IntA vs control groups in the NAc core (Fig. 6A: two-way RM ANOVA; main effect of treatment; F[1, 19] = 14.61, $p \leq 0.01$), NAc shell (Fig. 6B: two-way RM ANOVA; main effect of treatment; F[1, 19] = 5.30, $$p \leq 0.03$$), and the amygdala (Fig. 6C: two-way RM ANOVA; main effect of treatment; F[1, 18] = 13.64, $p \leq 0.01$). In contrast, there was no difference in mPFC c-Fos expression between control and IntA groups (Fig. 6C: two-way RM ANOVA; no main effect of treatment; F[1, 20] = 0.30, $$p \leq 0.59$$). Thus, the previously drug-paired cue produced robust activation in the NAc and amygdala but not mPFC that was comparable between groups. Fig. 6Cue-induced c-Fos expression in the NAc core (A), NAc shell (B), amgydala (C), and mPFC (D). Representative c-Fos (red) and DAPI (blue) images are shown to the right of each bar graph; scale bar = 100 microns. No differences between obesity-prone and obesity-resistant groups were found for any measure. However, cue presentation resulted in greater c-Fos expression in IntA vs control groups in the NAc core, NAc shell and amygdala. * = significant main effect of treatment
## Locomotor sensitization
Figure 8G shows locomotor activity in response to self-administered cocaine taken in the last DA period of each IntA session. Locomotor activity increased significantly across IntA sessions with a similar magnitude of increase in both OP and OR groups (Fig. 8G: two-way RM ANOVA; main effect of session; F(4.90, 106.4) = 9.19, $p \leq 0.01$; no main effect of strain; F[1, 22] = 0.26, $$p \leq 0.61$$). Summary data comparing locomotor activity during session 1 and 12 are shown in Fig. 8H (two-way RM ANOVA; main effect of session; F[1, 21] = 24.69, $p \leq 0.01$; no main effect of strain; F[1, 22] = 0.16, $$p \leq 0.70$$; no strain × session interaction; F[11, 239] = 0.78, $$p \leq 0.67$$).
Figure 9 shows locomotor activity in response to self-administered cocaine assessed at baseline (after acquisition but prior to IntA) and the day after the last IntA session (post), with time course data shown in panel A and summary data shown in panel B. IntA resulted in a sensitized locomotor response to cocaine in both groups, with a greater number of beam breaks during post vs baseline testing (Fig. 9B: two-way RM ANOVA; main effect of test; F[1, 21] = 12.85, $p \leq 0.01$; no main effect of group; F[1, 22] = 0.31, $$p \leq 0.58$$).Fig. 9Cocaine-induced locomotor activity before and after IntA experience is similar between groups and sensitizes across time. A Time course of locomotor activity across habituation and after a single self-administered infusion of cocaine (1.2 mg/kg, i.v.) after acquisition but prior to IntA (baseline, BL) and 24 h after the last IntA session (post). B Summary of cocaine-induced locomotor activity. IntA experience produced a similar degree of locomotor sensitization in both groups. * = significant main effect of test
## Discussion
Obesity-prone rats show greater responsivity to food cues and greater sensitivity to neural plasticity induced by sugary, fatty food than do obesity-resistant rats (see Ferrario 2020 for review). Given the overlap between neural circuits that regulate motivation for food and addictive drugs like cocaine, the current study examined whether obesity-prone rats may also be more sensitive to cocaine-induced neurobehavioral plasticity and more responsive to cocaine-paired cues than obesity-resistant rats. We found that obesity-prone and obesity-resistant groups did not differ in their initial acquisition of cocaine self-administration, baseline demand for cocaine or their locomotor response to self-administered cocaine. Furthermore, after IntA self-administration, responding in the absence of cocaine but the presence of a previously cocaine-paired cue was similar in both groups. In addition, there was robust cue-induced c-Fos expression in the NAc and amygdala after IntA, and locomotor sensitization across IntA sessions. However, the magnitude of these effects was similar across obesity-prone and obesity-resistant groups. Overall data here are in striking contrast to established inherent behavioral differences in motivation for food and responsivity to food cues between obesity-prone and obesity-resistant lines (discussed further below).
## Acquisition and baseline demand for cocaine
We did not find any differences in acquisition of cocaine self-administration between obesity-prone and obesity-resistant rats (Figs. 1, 7). Specifically, in all studies, discrimination between the active and inactive nose poke ports emerged similarly across the groups during initial training, and drug intake was comparable between groups. In addition, during baseline demand testing the preferred level of cocaine consumption at low price (Q0) was similar between groups. The absence of group differences in learning to self-administer cocaine is consistent with prior studies of instrumental responding for food in these lines. However, when the effort needed to obtain food is low (FR1, FR3), obesity-prone rats show enhanced responding compared to obesity-resistant rats that results in greater food intake (Vollbrecht et al. 2015). Thus, although consumption of food and effort to obtain it are greater in obesity-prone vs obesity-resistant rats, no such pattern was found for intravenous cocaine. It is possible that differences in acquisition of cocaine self-administration were masked by the dose of drug used here. However, baseline demand data suggest that this is not the case, as Q0 was similar between groups (Fig. 3C). The only group difference found at baseline was in Rmax, which was greater in obesity-resistant vs obesity-prone rats (Fig. 3D). This difference was maintained after IntA. Although Rmax was greater in obesity-resistant groups, measures that rely on rates of responding must be interpreted cautiously given the stimulant properties of cocaine.
## Effects of intermittent access
We used an IntA procedure here because this induces robust neural and behavioral plasticity associated with drug addiction (Kawa et al. 2016; Samaha et al. 2021). No differences in behavior during IntA were found between groups across all three studies (Figs. 2, 5, and 8). However, as expected, rats did show good discrimination between drug available, and no drug available periods. In addition, effects of IntA on behavior were overall similar to previous reports, with rats consuming the majority of their infusions during the first minute of the drug available period, and this intake escalating across sessions (Kawa et al. 2016; Allain et al. 2018; Kawa and Robinson 2019).
The main point of divergence between the groups was in the effect of IntA on motivation for cocaine, where IntA experience increased Pmax in obesity-prone, but not obesity-resistant groups (Fig. 3B). This effect of IntA in obesity-prone rats is consistent with prior studies showing that IntA increases Pmax and other measures of motivation in outbred Sprague Dawley rats (Calipari et al. 2013; Kawa et al. 2019a, b; James et al. 2019), the same strain from which rats in the current study were originally derived. Thus, it is the absence of effects in obesity-resistant rats that deviates from established effects of IntA on cocaine motivation. It is possible that obesity-resistant rats are less sensitive to cocaine-induced plasticity that underlies these enhancements in motivation, and/or that neuroplasticity occurs in obesity-resistant rats that opposes the effects of cocaine on motivation.
Importantly, differences in motivation for drug between obesity-prone and obesity-resistant groups was not caused by differences in IntA experience, as there were no group differences in the number of infusions taken or escalation of intake over the 12 IntA sessions (Fig. 2). In addition, while Pmax increased only in the obesity-prone group, Q0 increased similarly in both groups following IntA. To our knowledge, this is the first time that an increase in Q0 has been observed following IntA; this provides additional evidence for dissociations between hedonic vs motivational properties of cocaine.
In regard to potential differences in response to drug-paired cues, there were no differences in the ability of a previously drug-paired cue to maintain drug-seeking behavior in the absence of cocaine between groups (Fig. 4). Furthermore, while passive exposure to the cocaine-cue induced robust c-Fos expression in the NAc and amygdala compared to controls, the magnitude of these effects was again similar across groups (Fig. 6). Thus, there is no evidence for enhanced sensitivity to cocaine-paired cues in obesity-prone vs –resistant rats after IntA experience. The conditions used here are typical of many studies in terms of number of sessions, cocaine intake and cue exposures. However, it is possible that differences could emerge after longer periods of withdrawal or in response to different self-administration conditions (e.g., more sessions, or long access sessions). Nonetheless, these results are in stark contrast to enhanced responsivity to food cues in obesity-susceptible compared to obesity-resistant rats (Robinson et al. 2015; Derman and Ferrario 2018; Alonso-Caraballo and Ferrario 2019). Finally, the pattern of cocaine cue-induced c-Fos expression found here is consistent with previous reports (Brown et al. 1992; Neisewander et al. 2000; Hotsenpiller et al. 2002; Kufahl et al. 2009). One caveat to this is the absence of cue-induced c-Fos in the mPFC here (Fig. 6D). Interestingly, this was due to relatively high c-Fos expression in drug-naïve obesity-prone controls.
Another feature associated with IntA and plasticity of mesolimbic systems is the development of robust psychomotor sensitization (Carr et al. 2020; Allain et al. 2017, 2021; Wolf and Ferrario 2010). Here, we measured locomotor activity induced by self-administration of a single bolus of cocaine (1.2 mg/kg) at the end of each IntA session (Fig. 8) and prior to vs after IntA (Fig. 9). In both cases, locomotor sensitization developed across the 12 sessions of IntA, and the magnitude of this increase was similar in obesity-prone and obesity-resistant groups. Furthermore, cocaine-induced locomotor activity before IntA experience was also similar between groups (Fig. 9a). While consistent with the data discussed above, these results differ from previous studies showing enhanced acute locomotor activity in response to cocaine, and stronger expression of cocaine-induced locomotor sensitization in obesity-prone vs obesity-resistant rats (Oginsky et al. 2015; Vollbrecht et al. 2015, 2016). There are many potential explanations for this difference, most notably route of administration (i.v. here vs i.p. in prior reports) and the doses used. Additionally, the use of the IntA procedure, which produces robust sensitization, may have produced a ceiling effect thereby limiting our ability to detect group differences. However, our goal was not to identify conditions that do or do not produce behavioral differences between our groups. Rather, our aim was to use a procedure that induces neural and behavioral plasticity specifically related to the development of addiction and ask whether or not under those conditions we observe differences between obesity-prone and -resistant groups.
Overall, the pattern of neural and behavioral results here do not support the idea that there are robust differences in neurobehavioral responses to cocaine or cocaine-paired cues in obesity-prone vs obesity-resistant rats. Thus, behavioral differences that exist between obesity-prone and -resistant animals for food and food cues do not necessarily transfer to drugs and drug-paired cues. Furthermore, neural and behavioral plasticity induced by IntA were either similar in both groups, or commensurate with previous reports in “standard” outbred rats. These data highlight the need to carefully consider potential differences between effects of foods vs directly acting pharmacological agents like cocaine in order to refine concepts and models of “food addiction.”
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (PDF 181 KB)Supplementary file2 (PDF 157 KB)
## References
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|
---
title: Digital therapeutics from bench to bedside
authors:
- Changwon Wang
- Chungkeun Lee
- Hangsik Shin
journal: NPJ Digital Medicine
year: 2023
pmcid: PMC10006069
doi: 10.1038/s41746-023-00777-z
license: CC BY 4.0
---
# Digital therapeutics from bench to bedside
## Abstract
As a new therapeutic technique based on digital technology, the commercialization and clinical application of digital therapeutics (DTx) are increasing, and the demand for expansion to new clinical fields is remarkably high. However, the use of DTx as a general medical component is still ambiguous, and this ambiguity may be owing to a lack of consensus on a definition, in addition to insufficiencies in research and development, clinical trials, standardization of regulatory frameworks, and technological maturity. In this study, we conduct an in-depth investigation and analysis of definitions, clinical trials, commercial products, and the regulatory status related to DTx using published literature, ClinicalTrials.gov, and web pages of regulatory and private organizations in several countries. Subsequently, we suggest the necessity and considerations for international agreements on the definition and characteristics of DTx, focusing on the commercialization characteristics. In addition, we discuss the status and considerations of clinical research, key technology factors, and the direction of regulatory developments. In conclusion, for the successful settlement of DTx, real-world evidence-based validation should be strengthened by establishing a cooperative system between researchers, manufacturers, and governments, and there should be effective technologies and regulatory systems for overcoming engagement barriers of DTx.
## Introduction
With the development of IT, numerous healthcare technologies using personal smart devices and information have been proposed, and are expected to introduce a paradigm shift in medicine and healthcare. These new healthcare technologies have been provided in the form of health apps and is primarily used to support major medical practices or to promote wellness in prevention and prognosis management. However, as the amount and value of health-related data increase, the demands of consumers and medical service providers for digital medical technology transform from wellness products that have not proven clinically effective to products that have proven clinical effectiveness for the prevention, management, and treatment of diseases. This means digital technology can supplement and replace medical treatment beyond simple bio-signal measurement or health management. From this perspective, digital therapeutics (DTx), treatment techniques based on digital technology, have been defined and studied in earnest since the establishment of Digital Therapeutics Alliance (DTA), which consists of healthcare companies and stakeholders in 20171. DTx is considered as a subcategory of digital medicine, part of a broader category called digital health. Digital health, digital medicine, and DTx products have different requirements for clinical evidence and regulatory oversight because of different claim levels and therefore have different risk levels. DTx with prescription-only characteristics are classified as prescription DTx (PDTx). The primary difference between DTx and wellness applications is that DTx applications are developed with clinical evidence to target specific medical conditions, particularly major chronic diseases2.
Commercial DTx that have been released thus far was primarily developed for indications such as chronic and neuropsychiatric diseases which can be treated through behavioral change. DTx are rapidly being commercialized in the United States and Europe3,4. However, even for commercialized products, they have not been fully implemented into clinical practice owing to insufficient evidence to prove their effectiveness.
The objective of this study is to propose challenges and recommendations for DTx to be used in clinical settings beyond research and development. We explore trends and characteristics in the major DTx domains through literature and case studies on definitions, intended indications, commercial products, clinical trial studies, and regulations. In addition, we derive major challenges by analyzing the interconnections between research and development, clinical trials, and regulatory domains by comprehensively considering commercial or under-researched DTx. Furthermore, from the results of the investigation and analysis, we suggest essential considerations for each DTx domains and present the discourse required for DTx to be effectively established as a general medical practice in the future.
## Definition of digital therapeutics
In 2015, Sepah et al. first mentioned the term “digital therapeutics” and expressed that DTx are “evidence-based behavioral treatments delivered online” that can increase healthcare accessibility and effectiveness5. The DTA, one of the most active organizations in defining and disseminating DTx, defined DTx as “evidence-based therapeutic interventions that are driven by high-quality software programs to treat, manage, or prevent a disease or disorder”6. However, no clear international agreement on the definition exists, and it is being interpreted or applied differently in countries and research institutes. When examining the definitions and responses to DTx in each country, South *Korea is* the only country that has defined DTx: “software as a medical device that provides evidence-based therapeutic intervention to patients to prevent, manage, or treat a medical disorder or disease”. In other countries, such as the US, Germany, the UK, Japan, Australia, China, and France, DTx are not defined at the government level and are treated as general medical devices, as summarized in Table 1.Table 1Definition of digital therapeutics by country and institution. InstituteDefinition of Digital therapeuticsEuropean data protection supervisorDigital therapeutics (DTx) are evidence-based therapeutic interventions driven by software to prevent, manage, or treat a medical disorder or disease. In other words, DTx are patient-facing software applications that help patients treat, prevent, or manage a disease and that have a proven clinical benefit59.Digital therapeutics allianceDigital therapeutics deliver medical interventions directly to patients using evidence-based, clinically evaluated software to treat, manage, and prevent a broad spectrum of diseases and disorders6.Ministry of Food and Drug Safety, South KoreaSoftware as a medical device that provides evidence-based therapeutic intervention to patients for prevention, control, or treatment of medical disabilities and/or diseases26.※ The use of digital therapeutics is for patients who require therapeutic intervention The term DTx is similar to software as a medical device (SaMD) in that they both represent software used for medical purposes. While SaMD is a generic term for software intended to be used for one or more medical purposes, the term DTx is limited to software that intervenes with treatment based on clinical evidence for the treatment, management, or prevention of diseases or disorders. Therefore, most public, or regulatory authorities treat DTx as a sub-concept of SaMD without a special regulatory category.
## Characteristics of digital therapeutics
Table 2 shows a comparison of the characteristics of pharmacotherapy and DTx. A common characteristic of conventional pharmacotherapy and DTx is that the therapeutic effect for a specific disease must be clinically verified, and a prescription is required; however, they have differences in all stages from development, clinical trial, regulatory approval, distribution, clinical application, and post-marketing management. DTx require minimal development cost compared with pharmaceuticals (<$1\%$), and the development period is approximately half7. In addition, because they are not consumed due to use, manufacturing facilities and material costs for additional production after initial development are not required. Even for the distribution channels, unlike pharmaceuticals delivered to patients through manufacturers, wholesalers, retailers, and medical suppliers, DTx simplify the delivery path to patients through developers and platform providers. Table 2Comparison between conventional pharmacotherapy and digital therapeutics. CategoryPharmaceuticalDigital therapeuticsCommonalitiesClinically proven therapeutic effect and prescription for specific diseasesDifferencesDevelopment costHigh (about $1.8B)60Relatively lowDevelopment period7Average 12 yearsAverage 3 to 7 yearsManufacturingContinuous production required through manufacturing facilitiesNo additional manufacturing required after initial developmentDistribution channelManufacturer – wholesaler – retailer – medical service supplier (hospital, clinic, pharmacy, etc.) - PatientDeveloper – Platform provider – PatientPhases of clinical trialsHuman pharmacology study (Phase 1)Exploratory study (Phase 2)Confirmatory study (Phase 3)8Exploratory study (Pilot, optional)Confirmatory study (Pivotal)9RegulationPharmaceutical lawMedical device lawMedication monitoringManual monitoringReal-time automatic monitoring12Side effects1Drug toxicityMinor side effects owing to the use of mobile devicesMedication adherence$50\%$$1180\%$10MaintenanceUnavailableAvailable through updateWithdrawal and disposalUserProviderExpiration dateThe final day that the manufacturer guaranteesDepending on the efficacy changing over timeClassificationOTC drugDTxETC drugPDTxEfficacy influencing factors12,13Primarily physiological and demographic factorsIn addition to demographic factors, it is also affected by sociocultural and cognitive abilities. Accessibility by patientAccessible without educationNeed prior educationPrerequisitesNot requiredDigital device, Appropriate level of cognitive ability,PrescriptionMandatory (ETC only)Mandatory (PDTx only)Data securityNot applicableNeed cyber security solutionClinical challenges14 (i.e., psychopharmacology)Blood–brain barrier (Neurology)Patient engagementSaMD software as medical device, ETC ethical the counter, OTC over the counter, DTx digital therapeutics, PDTx prescription DTx.
For clinical trials, unlike pharmaceuticals that require phase-3 clinical trials that include drug safety and pharmacokinetic evaluation, DTx can obtain marketing approval through piloting (optional) and pivotal clinical trials through the medical device regulatory pathway in most countries8,9. Additionally, DTx only provide software-based interventions; thus, unlike existing pharmacotherapy that has side effects due to drug toxicity, they are considered to have only minor side effects owing to the use of mobile devices1. Medication adherence of DTx is known to be approximately $80\%$10, which is higher than that of pharmacotherapy ($50\%$)11. In post-marketing management, the expiration date of pharmaceuticals is determined according to the denaturation of substances, but the expiration date of DTx depends on the efficacy that changes over time. In classification, pharmaceutical drugs are classified as over the counter or ethical the counter drugs depending on whether a prescription is required. Similarly, some of DTx are classified as a PDTx if it requires a prescription. Moreover, DTx can improve their function (efficacy) or discard it through updates. In pharmacotherapy, individual physiological characteristics are the most important influencing factors in determining drug efficacy. However, the important difference is that the efficacy of DTx can be affected not only by demographic factors but also by sociocultural and cognitive abilities12,13. The main disadvantages of DTx compared to pharmaceuticals are low patient accessibility from a digital barrier, need for prerequisites, and data security issues. Since DTx operate on a digital platform, it is essential to understand digital devices; therefore, education prior to use is required, and it can be applied only to patients who have digital devices and some level of cognitive ability. In addition, since DTx store user data in digital form, there is a risk of leakage of sensitive personal data; therefore, additional preventative cyber security is required. The clinical challenge of pharmacotherapy is considered as a blood–brain barrier in drugs for neurological disease; however, for DTx, patient engagement is considered a major factor in determining the success of treatment in the future as it presupposes active participation of the patient14.
## Clinical trials of digital therapeutics
After screening 50,872 investigated clinical literature based on duplication, year of publication, non-journal article, and research scope, forty-five clinical trials were finally analyzed, excluding cases without National Clinical Trial (NCT) number or meta-analysis; of these, thirty-one were registered with clinicaltrials.gov and fourteen were presented in DTA website. A flowchart of the clinical trial search process is shown in Supplementary Figs. 1, 2, 3, and 4, and the NCT-numbered clinical trials related to DTx are summarized in Supplementary Table 1.
Detailed clinical trials related to psychiatric indications showed the largest number at $31.1\%$, neurological at $22.2\%$, endocrine at $20\%$, respiratory at $11.2\%$, poisoning at $8.9\%$, and cardiovascular disease at $6.7\%$. For clinical studies currently in progress, neuropsychiatric and chronic diseases accounted for the majority of indications; however, it has been shown that the scope of DTx is expanding with indications for neurological diseases as follows. Neurodegenerative diseases such as Parkinson’s disease, mastectomies, cancer diseases such as lump resections, blood diseases such as multiple myeloma, solitary plasmacytoma, plasma cell diseases such as amyloidosis, multiple sclerosis, multiple sclerosis-related depression and anxiety, fibromyalgia, back pain, chronic diseases such as heart failure (different from existing chronic diseases), autism spectrum disorders, schizophrenia, major depressive disorder, heart failure, pain, acute postoperative pain, systemic diseases such as lupus erythematosus, and dysarthria after stroke15,16.
For national clinical trial registration, the United States registered the most with 27 cases ($60\%$), followed by Switzerland with two ($4.4\%$), Finland with three ($6.7\%$), South Korea with two ($4.4\%$), Italy with two ($4.4\%$), Brazil with one ($2.2\%$), Malaysia with one ($2.2\%$), Singapore with one ($2.2\%$), Canada with one ($2.2\%$), Israel with one case ($2.2\%$), and Poland with one case ($2.2\%$). three case ($6.7\%$) had no clinical trial information on the ClinicalTrials.gov website. Clinical research was conducted by various institutions, including 24 companies ($53.3\%$), four pharmaceutical companies ($8.9\%$), seven universities ($15.6\%$), four hospitals ($8.9\%$), three research institutes ($6.7\%$), two medical consortiums ($4.4\%$), and one other ($2.9\%$).
Figure 1 shows a Sankey diagram analyzing clinical studies registered on ClinicalTrials.gov or on DTA website, or clinical studies that are not registered on ClinicalTrials.gov but presented through research papers. This diagram shows the relationship between clinical trial registration or publication timing, indications, sponsor type, country, clinical trial type, study design method, and primary outcome. Figure 1 shows that the types of indications have been diversifying over the years, showing that companies are prioritizing clinical trials more proactively. In addition, more than half of clinical trials were conducted in the US. Most clinical trials were conducted with randomized controlled trials (RCTs). For clinical study design, parallel had the highest proportion. The primary outcome included all of the general medical practice areas such as treatment, diagnosis, and prevention, but the proportion of treatment was the highest, followed by research. The proportion of other primary outcomes was insignificant. Fig. 1Sankey diagram depicting digital therapeutics-related clinical research studies that have been assigned a National Clinical Trial (NCT) number. A Sankey diagram was utilized to analyze trends in indication, institution, country, trial type, intervention, and major outcomes. Between 2010 and 2022, a total of 31 clinical trials related to digital therapeutics were identified on the ClinicalTrials.gov website using the keyword “Digital therapeutics”. The clinical trials were conducted by a variety of institutions, including pharmaceutical companies, hospitals, research institutes, medical consortia, universities, and corporations. These trials were carried out in multiple countries, including the United States, Europe, Switzerland, Finland, South Korea, Italy, Brazil, Malaysia, Singapore, Canada, Israel, and Poland. The clinical trials employed interventions such as parallel, single, crossover, and factorial designs, with primary objectives that included treatment, research, prevention, diagnosis, supportive care, and basic science.
## Commercialization of digital therapeutics
Many countries are promoting the commercialization of DTx through multi-sector cooperation with regulatory authorities, pharmaceutical companies, and medical experts. In the initial stage, DTx are approved and commercialized primarily for chronic diseases such as diabetes, cardiovascular disease, respiratory disease, and chronic pain or neuropsychiatric diseases such as drug addiction, sleep disorder, schizophrenia, chronic pain, and attention deficit hyperactivity disorder (ADHD)3. However, DTx for more diverse indications, such as irritable bowel syndrome17, migraine18, hip and knee replacement surgery, and ear disease, have been launched recently. Most DTx have been released through Food and Drug Administration (FDA) clearance led by the US (see Table 3), and currently, CE marked DTx products are being launched in Europe, primarily in Germany and Belgium1.Table 3FDA cleared DTx products and regulatory status. ProductManufacturerIndicationPathwayNomenclatureRegulatory ClassPrescriptionClinicals outcome in premarket stageApproval yearDarioLabStyle InnovationsType 1,2 diabetes510(k)System, test, blood glucose, over the counterClass IIO2015InsuliaVoluntisType 1,2 diabetes510(k)Calculator, drug doseClass IIO2016reSETPear TherapeuticsSubstance use disorderDe novoComputerized behavioral therapy device for psychiatric disordersClass IIOO2016Natural CyclesNatural CyclesBirth controlDe novoSoftware application for contraceptionClass IIO2017MindMotion GoMindmazeNeurorehabilitation510(k)Measuring exercise equipmentClass IIO2017My Dose CoachSanofiType 1, 2 diabetes510(k)Calculator, drug doseClass IIO2017reSET-OPear TherapeuticsOpioid use disorder510(k)Computerized behavioral therapy device for psychiatric disordersClass IIOO2018FreespiraPalo Alto Health SciencesPost-traumatic stress disorder510(k)Biofeedback DeviceClass IIO2018PropellerRESMED (Propeller Health)Chronic obstructive pulmonary disease510(k)NebulizerClass IIO2018TALi TrainTALI DigitalAttention impairment510(k) exemptComputerized cognitive assessment aidClass II2018levaRenovia, IncUrinary incontinence510(k)PerineometerClass IIO2018d-NavHYGIEIAType 1,2 diabetes510(k)Calculator, drug doseClass IIO2019SomrystPear TherapeuticsChronic Insomnia510(k)Computerized behavioral therapy device for psychiatric disordersClass IIOO2019WellDocBlueStarType 1,2 diabetes510(k)Accessories, pump, infusionClass IIO2020EndeavorRxAkili Interactive LabsPediatric attention deficit hyperactivity disorderDe novoDigital therapy device for attention deficit hyperactivity disorderClass IIOO2020NerivioTheranicaMigraine510(k)Trunk and limb electrical stimulator to treat headacheClass IIO2020NightwareNightWarePost-traumatic stress disorderDe novoDigital therapy device to reduce sleep disturbance for psychiatric conditionsClass IIOO2020ParallelMahana TherapeuticsIrritable bowel syndromeDe novoComputerized behavioral therapy device for treating symptoms of gastrointestinal conditionsClass IIO2020RelieVRxAppliedVRPain reliefDe novoVirtual reality behavioral therapy device for pain reliefClass IO2021MahanaMahana TherapeuticsIrritable bowel syndrome510(k)Computerized behavioral therapy device for treating symptoms of gastrointestinal conditionsClass IIO2021 A representative example of commercialized DTx is reSET (Pear Therapeutics Inc., MA, USA). reSET is the first interactive FDA cleared DTx for cognitive-behavioral therapy of drug and alcohol addiction patients. reSET provides a professional online counseling service and a face-to-face treatment service with medical staff according to the results of a patient self-questionnaire. In addition, EndeavorRx (Akili Interactive Labs Inc., MA, USA), developed as a DTx for pediatric ADHD, demonstrated that it can improve a patient’s attention index (API) by stimulating and activating the prefrontal cortex through video games19, and FDA-510(k) clearance and CE mark were obtained. Teva Pharmaceuticals’ Propeller Health (ResMed (Propeller Health), WI, USA)20, and ProAir Digihaler (Teva Pharmaceuticals Inc., NJ, USA)21, which are medication reminders with an inhaler, have proven to have a $79\%$ reduction in inhaler use when applied to asthma and COPD patients, respectively, and obtained FDA-510(k) certification22. In addition, Sleepio (Big Health, CA, USA), developed for the improvement of sleep disorders, showed that the treatment effect can be improved from 20 to $76\%$ through the sleep management function and online sleep disorder counseling program23.
*Unlike* general medicines, DTx operate by providing treatment content through computer or mobile applications. Because implementation technologies such as web or mobile app development and server construction do not have much differentiation for each indication, the specific treatment effect is determined by the content and application method provided. Therefore, each company’s DTx pipeline will depend on how the company will provide indication-specific content to the DTx platform it has already secured. Development of DTx contents for each indication is primarily performed through the company’s own development or in cooperation with research institutes such as universities or hospitals, and DTx are expanded to various indications by continuing cooperation with existing partnerships or establishing new partnerships with specialized institutions.
Figure 2 shows a Sankey diagram of commercialized DTx products, year of release, indication, regulatory authority, and regulatory class. The number of commercial products released has a repeating pattern of an increase and a decrease every two years since 2015, but shows an overall increase. Many of the products initially released were related to chronic or neurological diseases, and products released after 2020 were observed to be related to psychiatric disorders. By regulatory authority, most products are FDA cleared and CE marked. A total of 51 products received CE mark or FDA clearance, of which the number of CE marked products [26] was approximately double the number of FDA cleared products [14], and 14 products obtained both FDA clearances and CE marks. Most of the FDA cleared products obtained Class II grade certification, whereas most CE marked products were certified as Class I grade (Supplementary Table 2). However, it is unreasonable to generalize that CE legislation is stricter than FDA legislation; therefore, there are many approvals of low-grade medical devices. Recently, the EU has strengthened regulations from MDD to MDR, and one questionnaire to industry found that $89\%$ of respondents now prefer US rather than EU market entry for innovative devices due to the increased predictability of regulatory requirements24. The European DTx approval flow is expected to change after 2021, as the difficulty of obtaining new product approvals has increased, and strict post-marketing surveillance is involved. Fig. 2Sankey diagram depicting the major indications and regulatory status of commercial digital therapeutics. Sankey diagrams were employed to analyze trends in the types of indications, regulatory agency types, and class types for commercial digital therapeutic devices. The investigation focused on products listed on the DTA, DiGA, and mhealthbelgium websites, as well as products that had received FDA approval listed in the FDA medical device database. Regulatory authorities for each product were also investigated. Commercial digital therapeutics are primarily launched for indications related to chronic conditions, psychiatry, and neurology, with additional products available for urinary incontinence, ear disorders, hip and knee arthroplasty, tinnitus, irritable bowel syndrome, and vaginismus. Most commercial products have received approval from the FDA and CE regulatory agencies, while others have been approved by Japan’s Ministry of Health, Labour and Welfare (MHLW) and the Medicines and Healthcare products Regulatory Agency (MHRA).
## Regulation of digital therapeutics
Six guidelines and one policy document were investigated for DTx-related regulations within the International Medical Device Regulators Forum (IMDRF) member jurisdictions. The FDA has stated that it will permit the distribution and use of devices during public health emergencies without filing a premarket notice under section 510(k), as the FDA considers that DTx do not pose undue risk25. Six guidelines were published by the Ministry of Food and Drug Safety (MFDS, South Korea). The guidelines published in 2020 provided guidance on the definition of DTx and documents required to be submitted when obtaining DTx approval in the Korean regulatory system26. Subsequently, five guidelines published from 2021 to 2022 suggest safety and performance evaluation methods for DTx for alcoholism27, depressive disorder28, insomnia29, nicotine use disorder30, and panic disorder31 and provide design examples of clinical trial protocols with primary endpoints, sample size, and hypothesis for each guideline. Although there are no specific guidelines, except in Korea, the continued cases of approval through regulatory authorities in the United States, Europe, and Japan suggest that approval is possible within the current regulatory system for general medical devices. However, researchers are suggesting the establishment of a regulatory system suitable for DTx. Researchers argue that the regulatory system of DTx is considerably vague32 or insufficient33, and Vilardaga et al. indicated the limitation that DTx regulation does not guarantee usability and continuous adoption14. A stakeholder study on the development of the DTx industry reported that it is important to develop guidelines for permits and prepare a simplified regulatory system following research funding, suggesting that the role of the government is important34. In addition, researchers urged the development of an internationally harmonized regulatory model to improve the safety and quality of DTx35–37.
## Discussion
DTx are classified as medical devices representing regulated products worldwide, and refers to a type of medical device, not general medical devices such as artificial intelligence or implantable medical devices. The definition of DTx is dependent on the regulatory decisions of each country’s authorities, and each authority must follow the medical device nomenclature. However, as suggested in the results regulatory agencies have no formal agreement on the type of DTx as a medical device, and it is defined in various ways by country or institution using similar concepts and keywords.
As discussed above, keywords encompassing various definitions of DTx are summarized with application software, evidence-based medical intervention, preventing, managing, or treating diseases. Combining the above keywords, DTx can be defined as “software which provides evidence-based medical interventions for disease or disorder prevention, management, and treatment.” From the above definition, we derived the following requirements for DTx. First, DTx should be able to provide medical interventions for disease or disorder prevention, management, and treatment. Second, DTx should provide accessibility suitable for the user’s situation-appropriate methods such as application software. Third, for evidence-based treatment, the effectiveness of use must be proven through systematic clinical trials.
To determine whether these requirements have been achieved, agreed criteria for medical intervention effects, software availability, and clinical trial conditions that provide evidence are required, but no clear document has been presented yet. For example, as the use of artificial intelligence technology in medical devices increased, the IMDRF developed a technical document on key terms and definitions of artificial intelligence medical devices for regulatory use38. Therefore, regulators should carefully observe the DTx industry, and if the maturity level and terminology between countries are inconsistent, they should consider an agreement on key terms and definitions among international regulators. In addition, if the number of DTx approvals for a specific indication increase, it is necessary to develop standards for safety and efficacy verification through international standards development organizations such as the IEC or ISO, focusing on proven market maturity. Moreover, if the clinical trial protocol for efficacy evaluation for a specific disease group is standardized and the differences by race are small, the clinical trial conditions may be specified within the standard, such as using a medical device with a clearly defined clinical trial method (e.g., pulse oximeter39). Finally, DTx-related nomenclature was added to the official international nomenclature of medical devices referenced by various regulatory authorities, such as the global medical device nomenclature (GMDN); thus, DTx products approved under new nomenclatures in each country, e.g., under the FDA de novo pathway, should be available as per internationally harmonized nomenclatures for use.
Since DTx are primarily provided through smart devices, they can be closely related to the user’s daily life and perform continuous active interventions. Owing to these characteristics, DTx have been developed for the main objective of chronic disease management, drug abuse prevention, sleep management, and psychological and psychiatric disease management and treatment that require continuous interaction4. Chronic diseases include major diseases such as diabetes, asthma, chronic pain, chronic heart disease, and substance abuse disorders cover lifestyle diseases such as alcoholism, smoking and, medical drug abuse such as opioids. In addition, psychiatric disorders include a wide range of indications, from depression or anxiety disorders to post-traumatic stress disorder, schizophrenia, and ADHD. For sleep, they primarily address sleep disorders such as insomnia. However, recent DTx and ongoing clinical research cases show attempts to make DTx more disease-focused in various clinical fields16.
The clinical field application of DTx is expanding to tumors, cranial nerve, obstetrics and gynecological, urinary system, digestive system, orthopedic, respiratory system, immune, otolaryngology, and infection diseases15. It also includes a wide variety of indications such as mastectomy, lumpectomy, amyloidosis, multiple myeloma, solitary plasmacytoma, gynecological pain, urinary disorder, migraines, Parkinson’s disease, irritable bowel syndrome, Lupus erythematosus, multiple sclerosis, dyspnea genitopelvic pain/penetration disorder, and cognitive dysfunction. We observed no studies which clearly defined the commonalities of the new fields under consideration for the application of DTx. However, in the role or usage pattern, if it is possible to directly perform cognitive-behavioral intervention or as an auxiliary/complementary material to aid a doctor’s treatment or follow-up management, it tends to be introduced preferentially. Sim et al. predicted that DTx would be preferentially introduced in fields in which cognitive-behavioral interventions are possible and medical demand exceeds supply40. Supplementary Table 3 categorizes and shows indications of representative DTx that have been commercialized or are in the research stage.
Most of the DTx developed thus far are provided in the form of smartphone or web-based applications, and there are differences in core technology elements according to the type of indication and target end-user type (Supplementary Table 4). For example, diabetes DTx for diabetes provide treatment/guidance through the mobile web, and DTx related to respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) includes measurement functions using Internet-of-Things devices in addition to mobile apps. Furthermore, DTx for pediatric ADHD, eye disease, and psychiatric indications provide virtual reality (VR) devices, game content, and programs for mobile or PC. Therefore, most of the core technologies of DTx developed thus far focus on mobile and web apps. This is because user accessibility to smartphones is very high and monitoring and feedback related to the treatment is easy, which is advantageous for improving medication adherence and maximizing the effect of cognitive-behavioral therapy.
Content is a core technical element of DTx and has various forms such as questionnaires, face-to-face monitoring, games, and virtual reality41,42. Content plays an important role in determining the therapeutic effect of DTx; to induce patients to take continuous medication (participation) and increase the therapeutic effect, the provisional content and method are determined by considering the target indication, patient’s age, and sociocultural characteristics. Content is often developed based on the experience and expertise of clinicians; however, recently, cases of applying artificial intelligence to treatment contents or treatment guides have been reported43,44, thus continuous attention is required.
The important requirements for efficient DTx are digital adequacy aspects such as the user’s ability to use digital devices and cognitive level. Since DTx are mostly provided in the form of content through digital platforms, the higher the user’s ability to use the digital device, the better the understanding and concentration on the provided content, and the higher the efficacy of the manufacturer’s intention. However, these characteristics of DTx can cause a digital health gap due to digital disparities; therefore, a design process considering patient equity is required. Additionally, to secure the therapeutic effect of DTx, patient engagement is key13,14, and can be affected by sociocultural background and demographic characteristics12,13. The fact that DTx are influenced by demographic or sociocultural characteristics is not sufficiently empirically proven. However, from the examples of common digital media, we can infer that users’ influence on digital content varies according to external factors. A study which analyzed the factors affecting digital media user acceptance indicated that a reader’s awareness, interest, and intention to use e-books are affected by the reader’s age, education level, and income45. It has been reported that digital disparities or e-service discrepancies may occur depending on education level or age in the EU46 and income, education level, residence type, and age in Canada47. In addition, it has been reported that the ability to use a mobile health app is also related to age, education level, and e-Health literacy48. Since DTx are a type of digital content, we can easily infer that the above factors directly affect DTx adequacy.
The above suggests that demographic and sociocultural characteristics of individuals such as age, gender, culture, cognitive ability, digital device usability, social status, and even religion and values should be considered when conducting DTx clinical trials or applying regulations. Akili Interactive Labs, inc. ’s DTx for pediatric ADHD is an example of this. Akili’s pediatric ADHD treatment platform, Selective Stimulus Management Engine (SSME), is divided into 8–12, 13–17, and 18 years or older products in the United States, and clinical trials and approval procedures are in progress for each product. However, it has already entered the market without age restrictions in Europe, and clinical trials are being conducted in Japan for 6–17-year-old patients. Although Akili’s pediatric ADHD DTx have a similar platform and configuration, the clinical and approving procedures differ for each country because the platform is localized for language and culture. This is because the degree of the friendliness of the digital platform, the user’s cognitive level, and the sociocultural background can affect the therapeutic effect of the DTx. Lastly, changes in values, culture, and customs according to the change in era can change patients’ perception to the same digital content, which can lead to changes in the efficacy of DTx. Although this cannot be standardized, it means that the efficacy of DTx will naturally change over time. This suggests that there is contemporaneity in the efficacy of DTx. Consequently, DTx require periodic verification even after approval due to the change in efficacy over time, which means that DTx have an expiration date.
Nearly $80\%$ ($76.3\%$, 29 of 38) of the DTx-related clinical trials investigated in this study were RCTs. RCTs are still the best type of evidence that scientists and regulators can agree for validating the efficacy and safety of a device14, demonstrating that DTx products are rigorously tested in the clinical phases. A major challenge in current DTx clinical trials is the establishment of appropriate control groups for multinational trials and RCTs. Among the clinical trials investigated in this study, no cases of multinational clinical trials were found, and clinical trials in Asia were also rare. In addition, DTx as software can appear in various forms compared with placebos of pharmaceuticals, and maintaining complete blind conditions is difficult12,49; thus, an RCT framework considering the characteristics of DTx should be designed.
Although DTx are approved through RCTs, they are high-risk medical devices (Class III), and regulatory post-approval studies are not compulsory. Moreover, not all licensed DTx satisfy the statutory criteria for post-market surveillance50. An RCT is important and necessary to understand the efficacy of treatment; however, real-world data can also complement RCTs by evaluating the generalization of interventions and outcomes in real-world implementations51. Therefore, it appears that post-marketing research to observe whether newly approved DTx maintain safety and efficiency in the real-world should be continued through manufacturers and academia.
Although the IMDRF regulatory authority has not yet issued any clear guidelines, DTx are classified as SaMD and approved in the same manner as general medical devices36. In Germany’s DiGA-related Fast-Track Procedures52, DiGA is applied only to products which have obtained approval in accordance with the European Medical Device Directive (MDD)/European Medical Device Act (MDR); therefore, we consider the permission of DTx is sufficiently possible in the current regulatory system. However, DTx manufacturers are not traditional medical device companies familiar with medical device regulations, and regulatory systems and documents are also not familiar to researchers. Therefore, government efforts such as the publication of guidelines or online seminars to aid DTx manufacturers understand the regulatory system are required.
This study had the following major limitations. First, since this study was conducted only with publicly available data, the current development stage research, clinical trials in preparation, and regulations and guidelines under development by countries and institutions, were not included in the analysis. In addition, because published studies, clinical trials, and commercialized products tend to be reported based on the results of successful implementations, case studies or cause analysis of failure were not conducted. The literature on clinical efficacy verification in the real world was also excluded from the scope because of insufficient cases for each product. The real-world evidence (RWE) of DTx is also used as the major evidence data in determining reimbursement through health technology assessment. Not addressing the reimbursement of DTx is another important limitation of our study. Reimbursement is the biggest stumbling block to dissemination of innovative medical devices, and there are few cases such as DiGA in Germany and Improving Access to Psychological Therapies (IAPT) in UK. However, although the Access to Prescription Digital Therapeutics Act has entered the legislative stage in the US53, there have not been sufficient cases to compare and analyze payment schemes because Act has not implemented yet. Therefore, such cases should be considered in future studies.
After successful commercialization in the field of cognitive-behavioral therapy, DTx are expanding their scope of application to various clinical indications and are creating a new ecosystem by linking legacy healthcare and digital platforms. DTx have advantages in terms of not only access to global smart technology, cost-effectiveness, and patient-specific treatment but also response to diseases such as chronic, psychiatric, and neurological diseases that have not been properly managed within the conventional medical system. Therefore, the market value, expectations, and demand for DTx are expected to increase daily. For DTx to progress from the introduction stage and become a successful future treatment technology, barriers such as RWE-based verification should be solved through strengthening the link between research and development, clinical trials, and regulatory domains, establishing strategies to overcome engagement barriers, and preparing efficient regulatory processes. Moreover, infrastructure and clinical adaptability of DTx should be considered for a stable settlement of DTx in clinical areas. Hence, a close cooperation process between researchers, manufacturers, and government should be established. In addition, measures to secure technical and institutional solutions to prevent deepening health disparities and equity in the application owing to digital or medical disparities between countries should be considered from the development stage. Therefore, discussions and agreements centered on various international organizations, including those of medical devices, are required.
## Literatures
Research literature, registered clinical trials, regulatory-approved commercial products, and regulations related to DTx were investigated and analyzed, and background data for each area were collected in the following way. First, for research literature, papers published between 2010 and 2022 were investigated from a total of five databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE), Google Scholar, ScienceDirect, and Web of Science. The search string used was as follows: (Digital therapeutics OR DTx) AND (FDA OR SaMD OR MDD OR MDR) AND (Digital health OR Healthcare) AND (Smartphone OR Application). The literature search was conducted only on articles found through the search string, and reviewed articles that were not published in English were included. For scientific articles, non-journal articles were excluded from the analysis.
## Clinical trials
Clinical trial studies through research articles were investigated using the same search string as the literature search. For clinical trial research through the DTA website (https://dtxalliance.org), the contents of the clinical overview described in the detailed description of the product library among DTA website were referred to. In addition, cases were reviewed by searching for the keywords “digital therapeutics” on the ClinicalTrials.gov website. Finally, cases without an NCT number related to clinical studies investigated were excluded from the analysis. All clinical trials investigated only those registered or presented between 2010 and 2022.
## Commercial products
Commercial products included in the DTx-related literature or products registered in the FDA Medical device database54 in the product library of the DTA website55, as well as products that have received an CE mark in Germany and Belgium56,57, were primarily investigated.
## Regulations
Regulatory areas can be divided into premarket approval, which approves and manages medical devices, quality management systems, and post market control58. It may include reimbursement for DTx use through insurance in the aftermarket. In this study, the search focused on guidance related to premarket and other regulatory areas were excluded. The reason focusing on guidance is that DTx are part of software as medical devices, and regulatory authorities treat certain categories of medical devices different. We selected the Therapeutic Goods Administration (TGA, Australia), Brazilian Health Regulatory Agency (ANVISA, Brazil), National Medical Products Administration (NMPA, China), Health Canada (HC, Canada), European Commission Directorate (EC, EU), Pharmaceutical and Medical Devices Agency (PMDA, Japan), Ministry of Health of Russian Federation (MHRF, Russia), Health Sciences Authority (HSA, Singapore), MFDS (Republic of Korea), Medicines and Healthcare products Regulatory Agency (MHRA, United Kingdom), and US FDA (United States), the regulatory authorities of the IMDRF member countries, as the target of our investigation, and searched related data on the English website of each regulatory authority. Among the EU members, Germany, which established the Digital Health Apps program (DiGA), was additionally investigated, and Korean homepages were also investigated considering the linguistic accessibility of the authors. The research was conducted by searching the website of each institution with the keywords “Digital therapeutics” OR “DTx” OR “Guidance” OR “Guideline” OR “Policy.” In addition, literature and paper research on regulations proposed by researchers for DTx were conducted based on the same investigation conditions (five databases, investigation period) and “Digital therapeutics” OR “DTx” AND “Regulation” was used as the search string. In the regulatory literature search, only cases in which DTx was clearly mentioned in the scope, keywords, or main contents of the document were included, and cases in which it was described only in broad terms (e.g., software as a medical device, SaMD, software or program) were excluded from the analysis. Flowcharts for the search strategy are presented in Supplementary Figs. 1, 2, 3, 4.
## Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
## Supplementary information
Supplementary Information REPORTING SUMMARY The online version contains supplementary material available at 10.1038/s41746-023-00777-z.
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|
---
title: The TINCR ubiquitin-like microprotein is a tumor suppressor in squamous cell
carcinoma
authors:
- Lucia Morgado-Palacin
- Jessie A. Brown
- Thomas F. Martinez
- Juana M. Garcia-Pedrero
- Farhad Forouhar
- S. Aidan Quinn
- Clara Reglero
- Joan Vaughan
- Yasamin Hajy Heydary
- Cynthia Donaldson
- Sandra Rodriguez-Perales
- Eva Allonca
- Rocio Granda-Diaz
- Agustin F. Fernandez
- Mario F. Fraga
- Arianna L. Kim
- Jorge Santos-Juanes
- David M. Owens
- Juan P. Rodrigo
- Alan Saghatelian
- Adolfo A. Ferrando
journal: Nature Communications
year: 2023
pmcid: PMC10006087
doi: 10.1038/s41467-023-36713-8
license: CC BY 4.0
---
# The TINCR ubiquitin-like microprotein is a tumor suppressor in squamous cell carcinoma
## Abstract
The TINCR (Terminal differentiation-Induced Non-Coding RNA) gene is selectively expressed in epithelium tissues and is involved in the control of human epidermal differentiation and wound healing. Despite its initial report as a long non-coding RNA, the TINCR locus codes for a highly conserved ubiquitin-like microprotein associated with keratinocyte differentiation. Here we report the identification of TINCR as a tumor suppressor in squamous cell carcinoma (SCC). TINCR is upregulated by UV-induced DNA damage in a TP53-dependent manner in human keratinocytes. Decreased TINCR protein expression is prevalently found in skin and head and neck squamous cell tumors and TINCR expression suppresses the growth of SCC cells in vitro and in vivo. Consistently, Tincr knockout mice show accelerated tumor development following UVB skin carcinogenesis and increased penetrance of invasive SCCs. Finally, genetic analyses identify loss-of-function mutations and deletions encompassing the TINCR gene in SCC clinical samples supporting a tumor suppressor role in human cancer. Altogether, these results demonstrate a role for TINCR as protein coding tumor suppressor gene recurrently lost in squamous cell carcinomas.
TINCR encodes a p53-regulated ubiquitin-like microprotein expressed in stratified epithelia. Tincr loss promotes UVB-induced skin carcinogenesis in mice and deletions and mutations in human squamous cell carcinoma support a tumor suppressor role.
## Introduction
The discovery of transcriptional units without apparent protein-coding activity in the genome, followed by the functional characterization of many of these long non-coding RNAs (lncRNAs) as important factors involved in chromatin remodeling and in transcriptional and post-transcriptional regulation of gene expression, has brought to the forefront an essential role for these non-coding elements in shaping the architecture of transcriptional networks controlling cell and tissue homeostasis and disease1,2. Disruption of the homeostatic mechanisms controlling epidermal cell proliferation and differentiation is of relevance to the pathogenesis of squamous cell carcinoma, a highly prevalent cancer worldwide, and numerous lncRNAs regulate epidermal development and differentiation3, pointing to a role of these transcriptional units in the pathogenesis of human cancer. Among these, the TINCR gene is prominently expressed in human skin and has a proposed non-coding RNA-mediated role in the control of epidermal cell differentiation4. Moreover, deregulated TINCR expression has been observed across several different epithelial tumor types5. However, a potential protein-coding role for TINCR has been proposed based on global proteomic profiling of cornified epidermal keratinocytes, questioning the lncRNA nature of the TINCR transcript and recent reports have documented and characterized the TINCR locus as a protein-coding gene6–8. Here, we document the activation of TINCR expression downstream of P53 activation following UV-induced DNA damage, show increased incidence of SCC in Tincr knockout mice following UV-induced carcinogenesis and describe the presence of loss of function protein-truncating mutations and deletions encompassing the TINCR locus, all in support of a tumor suppressor role in human SCC. Moreover, we demonstrate that expression of the TINCR protein can impair tumor growth in squamous cell carcinoma cell lines and document that loss of TINCR protein expression is associated with poor prognosis in metastatic cutaneous SCC.
## Results
To explore the potential role of TINCR in tissue homeostasis and diseases we first characterized its expression across tissues. In agreement with previous reports4,7,8, we detected expression of TINCR transcripts in stratified epithelial tissues including the esophagus, trachea, and cervix. TINCR expression is particularly prominent in the skin (Supplementary Fig. 1a–c) and the TINCR protein is readily detected in cutaneous basal keratinocytes with a prominent upregulation in more differentiated spinocellular and granular skin layers (Supplementary Fig. 1d). In addition, and consistent with the proposed role of TINCR RNA in terminal keratinocyte differentiation4,7,8, the TINCR messenger RNA and protein are markedly upregulated upon in vitro calcium-induced differentiation of primary human keratinocytes (Supplementary Fig. 1e–g).
The barrier function of the skin epidermis plays an important protective role against dehydration, infections, and erosion that is challenged by DNA damage from daily sunlight exposure9,10. To explore a potential role for TINCR in the skin response to UV-induced damage we analyzed TINCR mRNA levels in human keratinocytes after UVC (100–280 nm) radiation. These analyses revealed marked transcriptional upregulation of TINCR transcripts upon UVC insult (Fig. 1a), suggesting a functional involvement downstream of the P53-mediated DNA damage response. Fig. 1TINCR is a p53 target gene upregulated in response to UV-induced damage.a RT-PCR of TINCR levels in human keratinocytes from two foreskin samples (060 and 101) at baseline and following UVC radiation (100 mJ/cm2) as average values normalized to ACTB relative to untreated controls. Error bars: standard error of the mean in technical replicates. P values: two-tailed unpaired Student’s t-test. b TINCR RNA levels in wild type and CRISPR TP53 knockout human keratinocytes targeted by gRNAs (g3-1, g4-1) in basal conditions and 4 h following UVC radiation (20 mJ/cm2) as in a. c Localization of the intron 1 TP53 regulatory element in the TINCR locus. d Quantitative PCR analysis of TP53 antibody chromatin immunoprecipitation with primers flanking the TP53 regulatory element in human keratinocytes at baseline and following UVC treatment. IgG chromatin immunoprecipitation is shown as control. P values: two-tailed unpaired Student’s t-test. e Representative images of hematoxylin-eosin-stained back skin from UVB-treated wild type, heterozygous, and homozygous Tincr mutant mice. f Quantification of epidermal thickness following UVB radiation in male ($$n = 3$$) and female ($$n = 3$$) mice for each Tincr genotype as in e as average values of relative skin thickness normalized to non-UVB-treated controls for each sex and genotype. Error bars: standard error of the mean. P values: two-tailed unpaired Student’s t-test. g Hematoxylin-eosin and myeloperoxidase stains showing UVB-induced skin microabscesses, and quantification of the number of these lesions in males and females for each Tincr genotype. Graphs show average values, each dot represents one mouse, and error bars correspond to the standard error of the mean. P values: two-tailed unpaired Student’s t-test. h Kaplan–Meier disease-free survival curves of Xpctm1Ecf/wt Tincrwt/wt ($$n = 17$$) and Xpctm1Ecf/wt Tincrp. R6fs/p.R6fs ($$n = 23$$) mice following long-term exposure to UVB (100 mJ/cm2, 3 times per week for 35 weeks). P values: log-rank Mantel–Cox test. Pie chart graphs show numbers of papillomas (dark blue), squamous cell carcinomas (medium blue) or spindle cell-like carcinomas (light blue) and if lesions were Trp53 wild-type (dark green) or Trp53 mutant (light green) in Xpctm1Ecf/wt Tincrwt/wt and Xpctm1Ecf/wt Tincrp. R6fs/p.R6fs mice. i Haematoxylin-eosin-stained micrographs of SCC tumors developing following chronic UVB exposure in Xpctm1Ec/wt Tincrp. R6fs/p.R6fs mice. Source data are provided as a Source Data file.
Consistent with this hypothesis, CRISPR knockout of TP53 in human keratinocytes abrogated TINCR mRNA induction by UVC radiation (Fig. 1b). In agreement, transcription factor binding motif analyses identified a canonical P53 response element in the first intron of the TINCR gene (Fig. 1c). Moreover, P53 chromatin immunoprecipitation revealed enrichment of sequences encompassing this response element in UVC-treated primary human keratinocytes compared to non-UV-treated controls (Fig. 1d). These results were specific to UV-induced DNA damage, as a genotoxic insult with the alkylating agent cisplatin and the antimetabolite hydroxyurea, did not increase TINCR expression, potentially reflecting limited induction of DNA damage by these agents in non-actively proliferating primary human keratinocytes (Supplementary Fig. 1h). In all, these results characterize TINCR as a direct target of P53 and upregulated in response to UV-induced DNA damage.
UV irradiation-induced damage in the mouse skin triggers an inflammatory response with neutrophil aggregation, epidermal hyperplasia, and desquamation of the terminally differentiated skin layers. To test the functional role of TINCR in response to UV exposure in vivo, we genetically engineered mice harboring a frameshift mutation (c.15_16insT) that proximally alters the mouse Tincr reading frame (p.Arg6Thrfs*33) with minimal alteration of the secondary structure of the Tincr lncRNA (Supplementary Fig. 2a, b). Of note, no alternative downstream translation initiation codons exist in the Tincr canonical open reading frame and the predicted polypeptide encoded by the p.Arg6Thrfs*33 mutant allele (MEELRSRAVPLEALPHQGTPSRRGIATATDRAAPRHTE*) shows no similarity with the Tincr protein after the first 5 amino acids. Tincr mutant mice are viable and fertile with Mendelian segregation of the mutant allele. Given that male and female mice show differential skin structural features and response to UV11,12, we analysed separately the skin phenotypes of Tincr wild type and mutant male and female mice. These analyses revealed preserved epithelial integrity and no differences in epithelial thickness, architecture, or keratinocyte differentiation in Tincr wild-type and mutant animals in homeostatic conditions (Supplementary Fig. 3a–c). However, treatment of Tincr wild type, heterozygous and knockout mice with an erythema-inducing dose of UVB (280–315 nm) radiation revealed a significant reduction ($$P \leq 0.0393$$) of the UVB-induced epidermal thickening at 48 h in Tincr knockout females compared to wild type controls (Fig. 1e, f). In addition, Tincr knockout female mice showed a higher number of UVB-induced neutrophil microabscesses compared to their male counterparts (Fig. 1g).
Impaired resolution of inflammatory responses following UV-induced injury in the skin of Tincr deficient mice, together with our observation of TINCR upregulation by P53 following UV-induced genotoxic stress, suggested a potential tumor suppressor role for TINCR in the skin and stratified epithelia. To test the role of TINCR as a tumor suppressor, we performed a UVB skin carcinogenesis experiment in the UV-induced DNA damage repair defective *Xpc heterozygous* mutant background, which sensitizes mice to UVB carcinogenesis in the skin13. In these experiments, we treated Tincr wild type, *Xpc heterozygous* (Xpctm1Ecf/wt Tincrwt/wt) and Tincr mutant, *Xpc heterozygous* (Xpctm1Ecf/wt Tincrp. R6fs/p.R6fs) mice with UVB (100 mJ/cm2) three times a week for 35 weeks. In this experiment, we observed accelerated papilloma skin lesions on the UVB-exposed back skin of Tincr mutant mice (32-week latency) compared with Tincr wild-type controls (46-week latency) (Fig. 1h). In total, 13 mice developed papillomas and squamous cell carcinomas in Xpctm1Ecf/wt Tincrp. R6fs/p.R6fs mice, while only 4 Xpctm1Ecf/wt Tincrwt/wt mice developed skin lesions by the experimental endpoint of 54 weeks ($$P \leq 0.0307$$) (Fig. 1h and Supplementary Data 1). We also observed increased progression to carcinoma and specific development of invasive tumors with spindle-like features in UV-treated Tincr mutant mice (Fig. 1h, i and Supplementary Data 1). In addition, only tumors from Tincr deficient Xpctm1Ecf/wt Tincrp. R6fs/p.R6fs mice gained Trp53 mutations over the course of these UVB carcinogenesis experiments (Fisher’s exact test $$P \leq 0.04$$) (Fig. 1h and Supplementary Data 2).
To further explore the tumor suppressor role of TINCR in human SCCs, we evaluated the impact of mimicking P53-induced TINCR upregulation by lentivirally expressing the TINCR protein-coding sequence in TP53 mutant CAL-27 (TP53 homozygous c.578 A > T) and FaDu (TP53 c.376-1 G > A, c.743 G > T) head and neck squamous carcinoma (HNSCC) cell lines, which lack the expression of endogenous TINCR (Supplementary Fig. 3d, e). These experiments revealed reduced numbers of colony-forming units, slightly limited colony size, decreased cell growth, and delayed proliferation in cells ectopically expressing TINCR protein compared with controls (Fig. 2a–c). Moreover, TINCR protein expression impaired the growth and tumorigenicity of CAL-27 (Fig. 2d–f) and FaDu (Fig. 2g–i) cell xenografts in vivo. Moreover, and in support of a differentiation role for TINCR as a tumor suppressor, gene expression analysis of HNSCC patient samples from TCGA14 revealed an association of TINCR RNA levels with transcriptional programs linked with epithelial differentiation and keratinization (Fig. 2j–l and Supplementary Data 3, 4,5).Fig. 2TINCR suppresses squamous cell carcinoma cells and tumor growth.a Western blot analysis of exogenous TINCR protein expression in HNSCC cell lines infected with empty or TINCR-HA-FLAG expression vectors. b Representative images of colony-forming assays in CAL-27 and FaDu cells expressing either empty vector or TINCR-HA-FLAG constructs. c Quantification of colony growth as in b. Graphs show average crystal violet signal across technical triplicates. Error bars show the standard error of the mean. Data were representative of two independent experiments. d In vivo intradermal tumor growth of CAL-27 cells expressing either empty vector or TINCR-HA-FLAG constructs. Growth curves show the average volume and standard deviation across ten independent tumors for each condition. Error bars show the standard error of the mean. e Graph shows the average weight of tumors recovered at the endpoint for each condition as in d. Error bars show the standard error of the mean. f Images of tumors recovered at endpoint for each condition as in e. g In vivo intradermal tumor growth of FaDu cells expressing either empty vector or TINCR-HA-FLAG constructs. Growth curves show the average volume and standard deviation across ten independent tumors for each condition. Error bars show the standard error of the mean. h Graph shows the average weight of tumors recovered at the endpoint for each condition as in g. Error bars show the standard error of the mean. i Images of tumors recovered at endpoint for each condition as in g. j Box and whisker plot of TINCR mRNA expression across TCGA HNSCC patients with high ($$n = 31$$, red) and low ($$n = 491$$, blue) TINCR expression. k Volcano plot representation of differential gene expression between HNSCC patients with high and low expression of TINCR. l Dot plot representation of GSEA analysis in HNSCC patients with high TINCR expression compared to HNSCC patients with low TINCR expression. P values in c–e, g, h correspond to two-tailed unpaired Student’s t-test. Source data are provided as a Source Data file.
Following these findings, we explored the presence of genetic alterations involving the TINCR locus in squamous cell carcinoma clinical samples. Genetically, copy number alteration analysis revealed highly prevalent heterozygous chromosomal losses encompassing the TINCR locus across head and neck (~$28\%$), lung (~$39\%$), esophageal (~$38\%$), and cervical (~$32\%$) squamous cell carcinomas in TCGA cohorts (Fig. 3a). Consistently, fluorescence in situ hybridization analysis of the TINCR gene showed ~$27\%$ heterozygous deletions and $7\%$ homozygous loss in an independent series of oropharyngeal squamous cell carcinomas ($$n = 156$$) (Fig. 3b, c). In addition, dideoxynucleotide sequencing of DNA samples from HNSCC tissue biopsies revealed protein-altering mutations in $\frac{9}{55}$ cases (Fig. 3d–g and Supplementary Fig. 4a–c). These included compound heterozygous mutations (p.Val18Met; p.Val49Met) in one tumor sample and homozygous mutations in five additional cases (Fig. 3g and Supplementary Fig. 4b). In two cases a recurrent single nucleotide point mutation (p.Met1Ile) altered the translation initiation codon and two cases showed early truncating mutations (p.Trp11* and p.Gln43Hisfs44*).Fig. 3Prevalence and structural consequence of TINCR deletions in human cancer.a Frequency of TINCR copy number alterations in TCGA HNSCC, lung SCC, esophageal SCC, and cervical SCC tumors. b Representative image of TINCR heterozygous loss detection by fluorescence in situ hybridization (FISH) in HNSCC. c Pie chart representation of frequency TINCR copy number alterations in TCGA HNSCC analyzed by FISH. d Schematic representation of the TINCR protein indicating the position of mutations identified in HNSCC patient tumor samples. Translation-initiating codon mutations are depicted as red circles, truncating mutations as black circles, and single amino acid substitutions as white circles. e Ribbon diagram representation of TINCR protein crystal structure at 2.12 Å resolution. f Electrostatic surface potential of the wild-type TINCR protein. Positively charged regions are shown in blue (kT/e = +5) and negatively-charged regions are shown in red (kT/e = −5). g Electrostatic surface potential of the documented TINCR variants. Positively charged regions are shown in blue (kT/e = +5) and negatively-charged regions are shown in red (kT/e = −5). h Western blot of analysis of documented TINCR variants on TINCR protein expression in 293 cells infected with empty vector, TINCR-1X-Flag, or variant TINCR constructs. Source data are provided as a Source Data file.
To best evaluate the impact of these alterations we analysed the structure of the TINCR protein. Structural characterization of TINCR at 2.12 Å resolution revealed a UBL fold domain composed of four β-strands forming one β-sheet (β1-4) and one α-helix (α2) (Fig. 3e), the lack of a characteristic C-terminal di-Glycine motif required for covalent protein conjugation that is present in all other ubiquitin-like proteins described so far with the exception of UBL515 (Fig. 3e and Supplementary Fig. 4d–f), and a 15-residue long highly conserved positively charged N-terminal extension (herein referred to as “cap”) as the most prominent features (Supplementary Fig. 4d–f and Supplementary Data 6, 7). The “cap” is visible only in protomer A, as it is buttressed by two neighboring TINCR protomers. However, its high B-factor values suggest that it is highly mobile in solution. Projection of TINCR amino acid variants identified in SCC samples on this structure revealed a protein disruptive effect of these non-conservative amino acid substitutions (p.Arg5Trp; p.Gly7Lys; p.Val18Met; p.Thr30Ile; p.Ala42Thr; p.Val49Met) (Fig. 3d–g). The distribution of these point mutations across both the N-terminal cap region and the ubiquitin-like domain, together with recurrent loss of translation-initiating codon mutations, N-terminal proximal truncating lesions, and locus deletions, all support a loss-of-function mechanism. To confirm the damaging effects of these mutations on TINCR protein expression, we generated expression constructs for each of the TINCR variants detected in HNSCC patients. Western blot analysis of TINCR protein in cells transfected with these vectors revealed that, as expected, loss of TINCR protein products from cells transfected with initiating codon altering and truncating mutations (Fig. 3h). Similarly, the introduction of a compound p.V18M, p.V49M mutation resulted in almost complete abrogation of TINCR protein expression (Fig. 3h). Albeit, in-depth functional characterization of the predicted deleterious alleles with retained TINCR expression remains to be addressed, these results argue for a loss of function mechanism.
To further explore the impact of TINCR inactivation in human cancer we performed immunohistochemical analysis across two independent cohorts of cutaneous squamous cell carcinoma (cSCC) samples encompassing a total of 141 patient samples (HUCA $$n = 100$$; cSCC SK801c $$n = 41$$). Across these series, we observed loss of TINCR protein expression in over $40\%$ of these cases (Fig. 4a, Supplementary Fig. 5, and Supplementary Data 8,9). Similarly, loss of TINCR protein was detected in over $30\%$ of patient samples from an HNSCC cohort (HUCA $$n = 306$$) across all tumor differentiation grades. Of note, the most aggressive poorly differentiated tumors presented the highest frequency of negative TINCR expression ($48\%$) in this series, though TINCR expression did not associate with survival (Fig. 4b and Supplementary Fig. 5). TINCR expression in HNSCC showed a trend in positive correlation with CDKN1A (p21) levels, a readout of TP53 activity (Supplementary Data 10). In addition, lymph node metastasis and a tumor thickness of less than 6 mm was correlated with negative expression of TINCR protein (Supplementary Data 10). Across our HNSCC series, HPV was detected in ten cases, five of which showed loss of TINCR expression. Moreover, evaluation of clinical outcomes in a selected cohort of cSCC with nodal metastasis further revealed that patients with TINCR-negative tumors exhibited more frequent metastasis (log-rank test, $$p \leq 0.043$$) and a trend towards lower overall survival (log-rank test, $$p \leq 0.089$$) compared with those harboring either diffuse positive TINCR protein expression or partial expression in differentiated areas of the tumor (Fig. 4c, Supplementary Fig. 5, and Supplementary Data 11), a result that is in line with the tumor suppressive effects of the TINCR protein observed in our UVB skin carcinogenesis and SCC xenograft experiments. Fig. 4Prognostic impact of the loss of TINCR protein expression in human cancer.a Representative images of immunohistochemical analysis of TINCR protein expression in cSCC tumors. Pie chart graphs show the prevalence of different TINCR protein expression patterns in two independent cohorts of cSCC samples. Negative (blue): no detection. Partial (light blue): TINCR expression is limited to differentiated areas of the tumor. Positive (red): diffuse TINCR staining. b Representative images of the different staining patterns of TINCR detected by immunohistochemistry in HNSCC patient tumor samples classified according to their differentiation grade. Pie chart graphs depict the distribution of TINCR protein expression patterns in relation to the tumor differentiation grading in a cohort of 306 HNSCC cases. c Kaplan–Meier curves indicating time to metastasis in patients with metastatic cSCC dichotomized according to TINCR protein expression.
## Discussion
The identification of a highly conserved ubiquitin-like peptide encoded in the TINCR gene6–8, originally described as a long non-coding RNA locus selectively expressed in squamous epithelial tissues4, has prompted renewed interest in the functional characterization of this locus in tissue homeostasis and disease. Ubiquitin-like proteins often serve as substrates for protein conjugation and function as signal tags that are involved in protein degradation, subcellular localization, signal transduction, and epigenetic regulation16. However, unlike most ubiquitin-like factors, the TINCR protein lacks a characteristic C-terminal di-glycine motif7 (valine glycine motif in UFM1) required for protein conjugation16. The absence of this sequence in TINCR points to a specific role and suggests that this ubiquitin-like factor may not function as a substrate for lysine protein conjugation. Still, annotation of TINCR-associated protein complexes has suggested a potential role in the regulation of the proteasome7. Finally, the TINCR crystal structure stands out for the presence of a distinct basic N-terminal “cap”, whose highly positive charge suggests a potential role as a recognition module for either phospholipids, negatively-charged regions of proteins, or nucleic acids.
The TINCR mRNA and protein are readily detectable in the skin and, albeit at lower levels, in other stratified epithelial tissues, in line with the role of TINCR in the organization and function of the skin. Even though suppression of TINCR expression by siRNA knockdown can result in the impaired organization of three-dimensional human keratinocyte cultures in vitro4, Tincr knockout mice have been reported to have a mild skin phenotype primarily characterized by defects in skin wound healing7. However, we observed altered resolution of UV-induced skin damage (decreased epidermal thickening and increased neutrophil micro-abscess) in female Tincr knockout mice in support of a physiologic role of TINCR as a protective factor against UV light exposure. The presence of a more severe phenotype in UV-treated TINCR female knockout animals is not surprising given the prominent differences in skin thickness and response to injury between male and female mice11,12 and highlights the need to carefully consider sex as a biological variable in the characterization of phenotypes involving this tissue.
Importantly, a defect in the physiologic response to UV exposure potentially links TINCR with pathologies derived from excessive UV-induced skin damage including cancer. This possibility is further supported by the identification of TINCR as a direct P53 target gene upregulated following UV-induced genotoxic stress, particularly considering that the P53 tumor suppressor plays a major role as a guardian of genomic integrity in the skin, a tissue exposed daily to UV-induced DNA damage, and that mutational disruption of TP53 is a common clonal genetic alteration in cSCC17. In agreement with this model, chronic UV exposure in Tincr knockout mice heterozygous for the Xpc DNA repair factor resulted in increased penetrance of skin tumors and progression to carcinoma. Moreover, squamous carcinoma cell lines show no detectable TINCR expression and lentiviral expression of the TINCR coding mRNA sequences resulted in impaired cell growth. Of note, the antiproliferative effect of TINCR re-expression was more prominently detected in vivo than in two-dimensional cultures in vitro and gene expression profiling analyses point to elevated cell adhesion and differentiation pathways as downstream effectors.
Discordant correlative studies analysing TINCR transcript levels in human cancer have associated decreased TINCR levels with colorectal18, prostate19, and non-small cell lung carcinomas20,21 and increased TINCR RNA expression in hepatocellular22, nasopharyngeal23,24, breast25,26, bladder27, gastric, and esophagus carcinomas28–32. However, a recent report published while this manuscript was under revision shows convergent results in support of TINCR regulation downstream of TP53 activation and linking TINCR activity with a potential tumor suppressor role in epithelial tissues8. This hypothesis is solidly substantiated here by our demonstration of common TINCR gene loss and the presence of recurrent protein-disrupting mutations in squamous cell carcinoma tumors. In addition, even though TINCR was originally described as a lncRNA4,33,34, the presence of protein-disrupting mutations including, and most telling, initiating codon-disrupting single nucleotide substitutions, together with the antiproliferative phenotype of TINCR protein expression in HNSCC tumor cells strongly support that the tumor suppressor function of the TINCR gene is mediated by the TINCR ubiquitin-like microprotein.
The observed deregulation of TINCR RNA in multiple tumor types and the association of TINCR expression with epidermal differentiation argue for an in-depth characterization of the transcriptional regulatory networks controlling TINCR expression beyond its role as a TP53 target gene. In particular, it will be important to explore the potential role of TINCR downstream of proliferation-driving oncogenic (RAS) and tissue organization and differentiation tumor suppressor (NOTCH) pathways commonly disrupted in squamous cell carcinoma. Finally, extended analysis of homogeneously treated and clinically annotated tumor cohorts will be instrumental in better defining the association of loss of TINCR protein expression with aggressive poorly differentiated tumor histopathology, metastatic disease, and outcomes.
## Generation of mutant mice
*We* generated Tincr mutant mice at the Herbert Irving Comprehensive Cancer Center Transgenic Shared resource by cytoplasmic injection of fertilized eggs from C57BL/6 females with assembled RNP complexes, comprising recombinant Cas9 protein, synthetic sgRNA (5′-CCATGGAGGAGCTGCGGCGA-3′ with 2′-O-methyl 3′ phosphorothioate modifications in the first and last three nucleotides, Synthego) and a homologous recombination template of 100 nucleotides containing a mutant translation starting ATG codon (ssODN, SIGMA, PAGE-purified). To identify Tincr (Gm20219) mutant mice, we genotyped F1 founders by PCR amplification of genomic DNA using primers flanking the gRNA cleavage site (Forward: 5′-AAGCCATCACCATCCCACTG-3′, Reverse: 5′-TGACCTGCGAGAGAGTCTCA-3′) followed by PCR purification (QIAGEN) and Sanger DNA sequencing (Genewiz). No initiation codon mutations resulting from recombination with the mutant template were identified. A mutant line harboring a single nucleotide deletion creating an early frameshift protein-truncating allele (p.R6fs) was selected for analysis. We verified germline transmission of Tincr mutant alleles in F2 offspring and bred mice to homozygosity. Tincr homozygous mutant mice are fertile and viable with Mendelian segregation of the mutant allele. Heterozygous mutant mice for Xpc (B6; 129-Xpctm1Ecf/J) were purchased from the Jackson Laboratory (Strain #010563)13. Xpc mutant mice were bred with Tincr mutant mice to generate mice heterozygous for Xpc (Xpctm1Ecf/wt) and either wild-type (Tincrwt/wt) or homozygous mutant (Tincrp. R6fs/p.R6fs) for Tincr. All animals were maintained at the Irving Cancer Research Center at Columbia University Medical Campus in specific pathogen-free facilities. Animals were housed in a controlled environment at 20–24 °C temperature, 45–$56\%$ humidity, and 12-h light-dark cycles. Mice were fed a standard chow diet ad libitum. All animal procedures were evaluated and approved by the Columbia University Institutional Animal Care and Use Committee (IACUC).
## UV irradiation of mice
We used a UV Irradiation Unit (Daavlin Co., Bryan, OH) equipped with eight FS72T12-UVB-HO lamps that emit UVB (290-320 nm, 75–$80\%$ of total energy) and UVA (320–380 nm, 20–$25\%$ of total energy). The radiation emission is calibrated using an IL1700 Research Radiometer/Photometer (International Light Inc., Newburyport, MA) and the dose of UVB is quantified with a UVB Spectra 305 Dosimeter (Daavlin Co., Bryan, OH). The radiation source to the target distance is maintained at 38 cm, and no measurable increase in skin surface temperature occurs during the procedure. For acute UVB irradiation, we shaved mice with a hair clipper, exposed them on the following day to a single dose of 600 mJ/cm2, and collected skin samples for histological analyses 48 h after exposure. For long-term UVB skin carcinogenesis, we shaved mice once per week with a hair clipper and exposed them to a dose of 100 mJ/cm2 three times per week for 35 weeks. Mice were monitored for the number of tumors, tumor growth, and progression of papillomas to squamous cell carcinomas for 54 weeks.
## Cell cultures and treatment
We obtained HNSCC cell lines (CAL-27 #CRL-2095 tongue, FaDu #HTB-43 pharynx) from the ATCC repository. CAL-27 contains a homozygous c.578 A > T p.H193L mutation in TP53 and FaDu contains a heterozygous c.376-1 G > A p.? and heterozygous c.743 G > T p.R248L mutation in TP53. Cancer cells were grown in the following media supplemented with $10\%$ FBS and antibiotics / antimycotics: DMEM (CAL-27), and MEM alpha Glutamax (FaDu). We obtained early passages of primary human keratinocytes (human keratinocytes) from neonatal foreskin samples under approved procedures by the Institutional Review Board. Primary mouse keratinocytes were obtained from the epidermis of neonatal pups on day 1 and subjected to dispase digestion (1 mg/mL) for 4 h at 37 °C. We cultured both primary human keratinocytes and mouse keratinocytes in a CnT-*Prime medium* without antibiotics/antimycotics (CnT-PR, CellnTec), and we used accutase (CnT-accutase-100, CellnTec) to detach cells when splitting was needed. Primary human keratinocytes and mouse keratinocytes were cultured for up to five passages. We irradiated human keratinocytes with UVC light at 20 or 100 mJ/cm2 in a Stratalinker 2400 equipped with five FG15T8 bulbs (254 nm wavelength) and let cells recover for 4 h or overnight, respectively. For primary human keratinocytes and mouse keratinocytes in vitro chemical-induced differentiation, we plated cells and CnT-PR media was replaced the following day by CnT-PR-D (2D differentiation) media containing 1.2 mM of CaCl2. We collected cells for further analyses at the indicated time points.
## Plasmids and vectors
The human TINCR full coding sequence encompassing 20 nucleotides upstream of the TINCR translation initiation codon and C-terminal HA-FLAG tag was synthesized by GenScript and cloned into the pCDH-CMV-MCS-EF1-puromycin lentiviral vector (Systems Biosciences CD510B). The mutant TINCR smORF sequences were synthesized using the BioXp system (Codex DNA). Mutant TINCR sequences were cloned into pcDNA 3.1+ expression vector linearized with KpnI/XbaI by Gibson assembly except for mutant sequence inserts from patients 7 and 12, which were cloned into the same vector but linearized with SnaBI/XbaI.
## CRISPR targeting of primary human keratinocytes
We used 293T-HEK cells to generate viral particles by transfecting with polyethylenimine (PEI) transfection reagent the corresponding lentiviral plasmids: lentiCRISPRv2-empty or lentiCRISPRv2 containing a single guide RNA against human TINCR (#116 5′-AGCCGGGCGGGCGCCATGGAGGG-3′ or #294 5′-CCTTCTACTACAACGCGCGGCGG-3′) and a single guide RNA against human p53 coding exons. We replaced the transfection media with fresh media on the following day and collected 48 h after transfection the lentiviral particles containing supernatant, which we filtered through a 45 µm PES filter before addition to the target cells. We infected primary human keratinocytes by spinoculation with the corresponding fresh raw viral supernatants and replaced viral media with fresh media after 3–4 h of recovery. We added puromycin (1 mg mL−1) on the following day and selected cells for 3 days.
## RNA isolation and quantitative real-time PCR (qRT-PCR)
We performed RNA extraction with the RNeasy Mini Kit (Qiagen, #74106) following the manufacturer’s instructions from cultured cells and mouse tissues collected and preserved in RNAlater (Millipore-SIGMA). Human total RNA samples were purchased from Thermo Fisher. RNA concentration and quality was measured with Nanodrop and equal amounts of RNA were converted into cDNA by using Superscript IV reverse transcriptase enzyme (Invitrogen). Real-time PCR was performed in a 7500 Real-Time PCR (Applied Biosystems) using TaqMan chemistry (Taqman master mix, Applied Biosystems) and TaqMan gene expression assays (Applied Biosystems, TINCR hS00542141_m1, and ACTB Hs00357333_g1). For quantification of the expression of the rest of the genes detected, SYBR chemistry was used (FAST-Start SYBR Green master mix, ROCHE) in combination with the corresponding primers indicated below. All reactions were performed in triplicates and normalized to ACTB mRNA levels as an endogenous control.
Human KRT1 Fw 5′-TGAGCTGAATCGTGTGATCC-3′, Rv 5′-CCAGGTCATTCAGCTTGTTC-3′; human FLG Fw 5′-AAAGAGCTGAAGGAACTTCTGG-3′, Rv 5′-AACCATATCTGGGTCATCTGG-3′; human LOR Fw 5′-CTCTGTCTGCGGCTACTCTG-3′, Rv 5′-CACGAGGTCTGAGTGACCTG-3′; human CDKN1A Fw 5′-GATTAGCAGCGG AACAAGGAGT-3′, Rv 5′-TACAGTCTAGGTGGAGAAACGGG-3′; human ACTB F 5′-ACAGAGCCTCGCCTTTGC-3′, R 5′-AGGATGCCTCTCTTGCTCTG-3′; mouse Klf4 Fw 5′-GTGCCCCGACTAACCGTT C-3′, Rv 5′- GTCGTTGAACTCCTCGGTCT-3′; mouse Ivl Fw 5′-GGGCAGAAACAGAAGCAGA-3′, Rv 5′-CAGTTCTGGCTCAGGTGACT-3′; mouse Actb Fw 5′-ACCTTCTACAATGAGCTGCG-3′, Rv 5′-CTGGATGGCTACGTACATGG-3′.
## Gene expression profiling using the Cancer Genome Atlas Program
RNAseq transcriptional data of head and neck squamous cell carcinomas in The Cancer Genome Atlas (TCGA) database14 was queried in order to explore the transcriptional features of cases with high and low TINCR expression. We stratified tumor samples according to their TINCR expression score with a cutoff value of 1.5. Genes showing differential gene expression associated with high TINCR levels were extracted using cBioPortal. Next, Gene Set Enrichment Analysis (GSEA)35,36 was performed to define the Molecular Signatures Database (MSigDB)37,38 pathways associated with high and low TINCR expression in HNSCC patients. Genes and pathways associated with high and low TINCR expression are displayed in Supplementary Data 3, 4, 5.
## Generation of custom rabbit polyclonal antibody against TINCR protein
Custom rabbit polyclonal antibodies against TINCR protein were generated by immunization with a linearized KHL-conjugated peptide entailing an immunogenic conserved region between human and mouse that comprises the central region of TINCR from amino acids 30 to 44 at Covance and by immunization with an N-terminal (amino acids 2–25) TINCR peptide at the Salk Institute using standard procedures.
## Immunofluorescence
We carried out fluorescence detection of TINCR protein in frozen human foreskin sections that were blocked with $5\%$ rabbit serum at room temperature for one hour and incubated with rabbit polyclonal anti-TINCR antibody (1:2000) for an additional 1 h at room temperature. We used an anti-rabbit AF488-conjugated secondary antibody raised in donkey (Invitrogen A21206) to detect fluorescence signal with a confocal microscope (Nikon Ti Eclipse inverted) and a 40X/1.19Oil or 60X/1.49Oil TIRF oil lens. DAPI was used to counterstain nuclei.
## Immunoblotting
Cells were harvested and lysed in RIPA buffer (50 mM Tris-Cl pH 8, 1 mM EDTA, $1\%$ Triton-X100, $0.25\%$ sodium deoxycholate, $0.1\%$ SDS, 150 mM sodium chloride). Identical amounts of whole lysates were resolved on 4–$12\%$ SDS/PAGE gels (NuPAGE, Invitrogen) and transferred to nitrocellulose membranes. Blots were blocked in Odyssey Blocking buffer (LI-COR Odyssey) and incubated with the corresponding primary antibodies: rabbit monoclonal antibody anti-HA tag (CST #3724, clone C29F4, 1:1000 dilution), mouse monoclonal antibody anti-tubulin (SIGMA #T9026, clone DM1A, 1:5000 dilution), rabbit monoclonal antibody FLAG DYKDDDDK Tag (Cell Signaling, clone D6W5B, 1:1000 dilution) and mouse monoclonal antibody anti-β-Actin (SIGMA #5441, clone AC-15, 1:2000 dilution) and subsequently incubated with the corresponding secondary anti-IgG fluorescence-labeled antibodies (LI-COR Odyssey, 1:5000 dilution). Signals were acquired by an LI-COR Odyssey detector.
## Chromatin immunoprecipitation (ChIP) assay
ChIP assay was carried out as previously described in ref. 39, with some modifications. Briefly, we crosslinked control and UVC-treated primary hKCs, lysed cells, and sonicated lysates with the Bioruptor Pico device (Diagenode) at 8 °C for 30 cycles (30 s on, 30 s off). We quantified chromatin with the Qubit system (Thermo Fisher) and incubated 2 ug of crosslinked chromatin with 5 ug of normal rabbit IgG (SCBT, sc-2027) or anti-p53 antibody (rabbit polyclonal, Diagenode C15410083). We used magnetic protein G beads (Dynabeads, Invitrogen 10003D) for immunoprecipitations. Inputs correspond to $1\%$ of the total DNA sample. We de-crosslinked the inputs and immunoprecipitates and purified DNA with MicroChIP DiaPure columns (Diagenode C03040001). We analyzed TINCR p53 RE enrichment over the input chromatin by quantitative real-time PCR (Applied Biosystems) using FastStart Universal SYBR Green (ROCHE) with the following primers: P53 RE TINCR int1 Fw 5′-CAACATGGTGAAACCCCATC-3′, and P53 RE TINCR int1 Rv 5′-CGCCTCCCAGACTCAAG-3′.
## Mouse organs histopathology
We fixed mouse organs in $10\%$ buffered formalin. The Molecular Pathology shared resource facility at the Herbert Irving Cancer Comprehensive Center proceeded to embed fixed mouse organs in paraffin blocks, sectioning, and hematoxylin and eosin (hematoxylin-eosin) stain by following standard procedures. All slides were digitalized on a Leica SCN 400.
## Squamous stratified epithelia analyses
We quantified the thickness of the epidermis from hematoxylin-eosin-stained back and tail skin samples by taking the average of three randomly selected skin regions (six measures per region) per mouse. In the case of squamous stratified epithelia from the esophagus and forestomach, we quantified thickness by taking the average of 25 measures from one large randomly selected area per organ and mouse. We counted manually the number of cells found in the differentiated layers from the tail skin, esophagus, and forestomach, and normalized this calculation per area (mm2).
## Patients and tissue specimens
We collected surgical tissue specimens from HNSCC patients who underwent resection of their tumors at the Hospital Universitario Central de Asturias between 1990 and 2010, with written informed consent in accordance with approved institutional review board guidelines. The formalin-fixed, paraffin-embedded tissue samples and data from donors included in this study were provided by the Principado de Asturias BioBank (PT$\frac{17}{0015}$/0023), integrated into the Spanish National Biobanks Network, and they were processed following standard operating procedures with the appropriate approval of the Ethical and Scientific Committees.
A homogenous cohort of 306 surgically treated HNSCC patients was selected for study according to the following criteria: (a) having a single primary surgically treated tumor in the oropharynx, hypopharynx, or larynx; (b) confirmed microscopically clear surgical margins; (c) no treatments prior to surgery; (d) a minimum follow-up of 5 years. The main clinical and pathological features are summarized in Supplementary Data 11. Information on HPV status was available for all the patients. HPV detection was performed using p16 immunohistochemistry, high-risk HPV DNA detection by in situ hybridization, and genotyping by GP5+/6+ -PCR, as previously reported40,41.
The Department of Pathology electronic database at Hospital Universitario Central de Asturias was searched to locate all the patients who had developed nodal metastases from cSCC of the head and neck (cSCCHN) during 1998–2008. All the electronic medical records were reviewed (by Drs. García-Pedrero and Santos-Juanes), and information regarding clinical variables, date of nodal metastasis, and death were collected. A total of 50 patients with primary cSCCHN were included who had histologically confirmed lymph node metastasis. Controls (50 patients) were randomly selected among those patients with cSCCHN who did not have any metastases and had a minimum follow-up of 4 years.
All the tumors were excised by wide local excision with ≥5-mm margins. Patients with positive margins were excluded. None of the patients received any form of adjuvant therapy after their surgery. Ethics approval was obtained from the Hospital Universitario Central de Asturias ethics committee. The study was conducted and the results were reported according to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines for case-control studies. Clinical patient-related data were collected retrospectively. Patient age was defined as the age at the time of resection. Pathologic tumor staging was based on the seventh American Joint Committee on Cancer classification. Outcome data were from patients with one tumor.
A commercial cutaneous SCC with adjacent normal skin tissue array (SK801c) containing 67 tumor samples and nine normal skin samples was from US Biomax Inc.
## Head and neck and cutaneous SCC tissue microarray (TMA) construction and immunohistochemistry
Three morphologically representative areas (1 mm diameter cylinders) were selected from each individual tumor paraffin block for the construction of tissue microarrays (TMAs) as described previously40. In addition, each TMA included three cores of the normal epithelium (tonsillar, pharyngeal, and laryngeal mucosa obtained from non-oncologic patients) as an internal negative control. The TMAs were cut into 3 μm sections and dried on Flex IHC microscope slides (Dako, Glostrup, Denmark). The sections were deparaffinized with standard xylene and hydrated through graded alcohols into water. Antigen retrieval was performed using Envision Flex Target Retrieval solution, high pH (Dako). Staining was done at room temperature on an automatic staining workstation (Dako Autostainer Plus, Dako, Glostrup, Denmark) using TINCR rabbit polyclonal antibody at 1:1000 dilution. Immunodetection was carried out with the Dako EnVision Flex + Visualization System (Dako Autostainer, Dako, Glostrup, Denmark), using diaminobenzidine as chromogen. Counterstaining with hematoxylin was the final step. A sample of normal skin was used as a positive control. Negative controls with an omission of the antiserum from the primary incubation were also included.
To quantify TINCR expression, a semiquantitative scoring system based on staining intensity was applied, divided into three categories: negative (absence of staining, score 0), weak to moderate (some cytoplasmic staining in tumor areas, score 1), and strong protein expression (intense and homogeneous cytoplasmic staining in tumor areas, score 2), with an inter-observer concordance higher than $95\%$. For statistical purposes, TINCR staining was dichotomized as positive expression (score 1–2) versus negative or loss of TINCR expression (score 0).
## Fluorescence in situ hybridization (FISH)
We used two FISH probes to study the TINCR locus. RP11-565J3 specific bacterial artificial chromosome (BACs) that map at the TINCR locus (19p13.3) and RP11-937M15 that map to a control region (19q13.11) were purchased from the Human BAC Clone Library, Children’s Hospital Oakland Research Institute (CHORI) and labeled by Nick translation assay with FITC and Texas-Red, respectively, to generate locus-specific FISH probes. We used the RP11-937M15 BAC clone to generate a control probe to enumerate chromosome 19. FISH analyses were performed according to the manufacturer’s instructions, as previously described in ref. 42 on 5 mm TMA sections mounted on positively charged slides (SuperFrost, Thermo Scientific). Briefly, the slides were first deparaffined in xylene and rehydrated gradually in a series of ethanol (70, 80, $95\%$). We used the Histology FISH Accessory Kit (DAKO) following the manufacturer’s instructions. Briefly, we pre-treated in 2-[N-morpholino]ethanesulphonic acid (MES), followed by protein digestion performed in a pepsin solution. After dehydration, we denatured the samples in the presence of the specific probe at 66 °C for 10 min and left them overnight for hybridization at 45 °C in a DAKO hybridizer machine. Finally, we washed the slides with 20×SSC (saline-sodium citrate) buffer with detergent Tween-20 at 63 °C and mounted them in a fluorescence mounting medium (DAPI). We manually enumerated FISH signals within nuclei all over the tissue. FISH images were also captured using a CCD camera (Photometrics SenSys camera) connected to a PC running the Zytovision image analysis system (Applied Imaging Ltd., UK) with a focus motor and Z stack software.
## Mutation analysis of tumor samples
We performed dideoxynucleotide DNA sequencing of PCR products encompassing the TINCR coding exons in DNA extracted from formalin-fixed paraffin-embedded HNSCC samples using standard procedures at Genewiz (South Plainfield, New Jersey). Mutations were identified using Alignments (Benchling, San Francisco, California) and manual curation of sequencing chromatograms. To detect mutations in Trp53 from UVB-induced mouse lesions, we performed dideoxynucleotide DNA sequencing of PCR products encompassing the Trp53 coding exons in DNA-extracted formalin-fixed paraffin-embedded mouse papilloma and squamous cell carcinoma samples using standard procedures at Genewiz (South Plainfield, New Jersey).
## TINCR recombinant protein production and purification
We amplified the human TINCR coding sequence by PCR from pcDNA 3.1-TESO and cloned it in-frame following an N-terminal hexahistidine (His6) tag in BseRI linearized pET28a-LIC expression vector (Addgene 26094). We expressed TINCR recombinant protein from Rosetta 2 (DE3) *Escherichia coli* cells by induction with 0.5 mM isopropyl-b-d-thiogalactopyranoside 3 h at 37 °C. We resuspended cells in lysis buffer (50 mM HEPES pH 7.5, 500 mM sodium chloride, $10\%$ glycerol, 0.5 mM TECP, 20 mM imidazole) supplemented with complete EDTA-free protease inhibitor (Roche) and lysed cells by sonication. We purified TINCR recombinant protein using an AKTA fast protein liquid chromatography system (GE Healthcare). We first performed affinity chromatography using a 1 mL Nickel-charged HisTrap column (GE Healthcare) in a step-wise method with elution buffer (lysis buffer with 500 mM imidazole) by first setting the buffer ratio to $25\%$ elution buffer for eight column volumes, and then switching to a linear gradient to $100\%$ elution buffer over 10 column volumes. We pooled TINCR-containing fractions and purified further by size exclusion chromatography using a Hi Load $\frac{16}{60}$ Superdex 200 gel filtration column (GE Healthcare) equilibrated in 50 mM HEPES pH 7.5, 100 mM NaCl, $10\%$ glycerol, and 0.5 mM TCEP. We assessed protein expression and purity by SDS-PAGE and Coomassie staining and concentrated protein samples to ~5–6 mg/ml.
## Crystallization and structure determination
The human TINCR protein at a concentration of 5.5 mg/ml in a protein buffer (20 mM HEPES (pH 7.5), 100 mM sodium chloride, $5\%$ (v/v) glycerol, and 1 mM TCEP) was initially subjected to extensive robotic screening at the High-Throughput Crystallization Screening Center of the Hauptman-Woodward Medical Research Institute (HWI) (https://hwi.buffalo.edu/high-throughput-crystallization-center/)43. For the first week, the crystallization plate was placed in a 4 °C incubator, during which six crystal hits were detected by manually checking the robotically-taken images. All six crystallization conditions were initially used to set up an in-house crystallization plate using the micro-batch, under oil, method in a cold room (4 °C). Very small rod-like crystals of the TINCR protein appeared after a week in all conditions. Further crystal optimization was performed using the seeding method. The largest clusters of rod-like crystals appeared in a condition comprising 0.1 M magnesium sulfate heptahydrate, 0.1 M sodium acetate, pH 5, and $20\%$ (w/v) PEG 1000, after a few days. A single crystal with dimensions approximately 200 µm long and 10 µm wide was transferred to the aforementioned crystallization condition, which was already supplemented with $20\%$ (v/v) glycerol as a cryo-protectant, and flash-frozen in liquid nitrogen for the subsequent data collection at the NE-CAT 24-ID-E beamline of Advanced Photon Source in Lemont, IL. The TINCR crystal diffracted the X-ray beam to a resolution of 2.12 Å. The images were processed and scaled in space group P43212 using XDS44. The structure of TINCR was determined by molecular replacement method using both MOLREP45 and PHASER46 programs. The first model, generated by the protein fold recognition server Phyre247 from the crystal structure of the ubiquitin-like domain of the BAG protein (PDB id: 4HWI48) from Arabidopsis thaliana, with $21\%$ sequence identity with that of TINCR was successfully used as a search model for structural determination of the ubiquitin-like domain of the TINCR protein. The N-terminal 15 amino acids of the TINCR protein, which forms an α-helical cap, was subsequently modeled by programs XtalView49 and Coot50 and refined using both CNS51 and Phenix52 programs. The refined structure reveals that there are two TINCR protomers, forming one dimer, in the asymmetric unit of the crystal. The a-helical cap is visible in one protomer and it is highly mobile as inferred by its high B-factor values. The crystal structure of the TINCR protein has been deposited in Protein Data Bank with accession code 7MRJ. Crystallographic statistics are shown in Supplementary Data 6, 7.
## Modeling
A structural model was generated for each of five TINCR mutants (R5W, G7K, T30I, A42T, and V18M-V49M) using Coot50, followed by refinement by Phenix52 against the TINCR wild-type dataset. In each case, the resulting model was compared with those generated by modeling servers Phyre247 and iTASSER53. Electrostatic surface potential was calculated using APBS54 and visualized in PyMOL (https://pymol.org/2/).
## Colony formation assay
We plated 500 cells (CAL-27 and FaDu) at a single-cell suspension into 12-well plates containing a complete medium. We cultured cells for 2 weeks with the medium being replaced every 3–5 days. Then, we fixed cells with $4\%$ formaldehyde, stained them with $0.1\%$ crystal violet, and scanned for quantification of colonies with FIJI ImageJ. We used $10\%$ acetic acid to dissolve incorporated crystal violet and we measure absorbance at an optical density of 590 nm in a plate reader (Biorad).
## Tumor xenografts
Female 6-week-old Nude (NU/NU [088] Charles River) mice were used for orthotopic transplantations and xenograft studies. Human HNSCC cell lines (CAL-27 and FaDu) were suspended in $50\%$ Matrigel (BD, 356237) diluted with DMEM (SIGMA, #H0135) media at a concentration of 50,000 cells per inoculation, and injected intradermally into Nude (Charles River, NU/NU [088]) recipient mice. Tumors were detected by palpation, measured using a digital caliper, and tumor volume was calculated (VTumor = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\pi }{6}$$\end{document}π6xlxw2, where l = length in mm and w = width in mm).
## Statistics
For patient tumor sample analysis, χ2 and Fisher’s exact tests were used for comparison between categorical variables. For time-to-event analysis, Kaplan–Meier curves were plotted. Differences between survival times were analyzed by the log-rank method. All tests were two-sided. p values of ≤0.05 were considered statistically significant. We conducted statistical analyses using Prism software v8.0 (GraphPad Software).
## Study approval
All experimental procedures were conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of the Hospital Universitario Central de Asturias and by the Regional CEIm from Principado de Asturias (date of approval May 14, 2019; approval number: $\frac{141}{19}$) for the project PI$\frac{19}{00560.}$ Animal studies were conducted under the supervision of the Columbia University Irving Medical Center IACUC.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
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|
---
title: Lrig1-expressing epidermal progenitors require SCD1 to maintain the dermal
papilla niche
authors:
- Sophia Beng Hui Lim
- Shang Wei
- Andy Hee-Meng Tan
- Maurice A. M. van Steensel
- Xinhong Lim
journal: Scientific Reports
year: 2023
pmcid: PMC10006094
doi: 10.1038/s41598-023-30411-7
license: CC BY 4.0
---
# Lrig1-expressing epidermal progenitors require SCD1 to maintain the dermal papilla niche
## Abstract
Niche cells are widely known to regulate stem/progenitor cells in many mammalian tissues. In the hair, dermal papilla niche cells are well accepted to regulate hair stem/progenitor cells. However, how niche cells themselves are maintained is largely unknown. We present evidence implicating hair matrix progenitors and the lipid modifying enzyme, Stearoyl CoA Desaturase 1, in the regulation of the dermal papilla niche during the anagen-catagen transition of the mouse hair cycle. Our data suggest that this takes place via autocrine Wnt signalling and paracrine Hedgehog signalling. To our knowledge, this is the first report demonstrating a potential role for matrix progenitor cells in maintaining the dermal papilla niche.
## Introduction
Skin niches regulate tissue-resident stem and progenitor cells by specifying cell fates1,2, replenishing niche cells1,3, and facilitating parent-daughter cell crosstalk in tissue regeneration4,5. A well-studied example is the dermal papilla (DP) of the hair follicle, a dynamic skin mini-organ that is regulated through reciprocal interactions between hair follicular epithelial cells and DP cells6–15. As an important stem cell niche, the DP plays pivotal roles in hair follicle maintenance and cycling5,6,15–21. It consists of mesenchymal cells that regulate the proliferation of hair follicle stem cells (HFSC) and germ (HG) progenitor cells, as well as promote the differentiation of their more mature progeny19,22–24. However, how DP cells themselves are sustained in vivo is not well understood.
Niches can influence stem and progenitor cell behaviour by initiating intercellular crosstalk by modulating the secretion of diffusible, inductive signals such as Wnt, Hedgehog and TGF-β ligands14,20,25. For instance, Wnt ligands secreted by epithelial matrix cells11,19,26 stimulate proliferation of HG progenitors4,13,19, by creating a Wnt permissive milieu via Rspondin induction in the DP5 during the transition from the hair resting (telogen) to growth (anagen)19,27. Sonic hedgehog (Shh) ligands expressed in matrix progenitor cells regulate DP maturation and maintenance28–30. TGF-β ligands, induced in the DP, induce apoptotic cell death in matrix progenitors to promote hair follicle regression (catagen)20,31,32. However, it is less clear if and how stem and progenitor cells may signal back to niche cells post-development.
Many of these signalling molecules, such as Wnt and Hedgehog, require palmitoylation for their activation and secretion33. This involves covalently linking ligands with palmitoleate, a monounsaturated fatty acid that is synthesized by the key lipid desaturating enzyme, SCD134,35. Previous studies have demonstrated the importance of SCD1 for the maintenance of skin organs such as hair follicles and sebaceous glands (SG), since mice with germline36 or epidermal-specific37 knockout of Scd1 lose their SG and hair. SCD1 is expressed most highly in SG cells, and the hair loss resulting from Scd1 loss-of-function mutations has historically been attributed to the lack of sebum stemming from the absence of SGs. However, mice without differentiated sebocytes do not develop hair loss38, suggesting that differentiated sebocytes and sebum are not required for hair maintenance. This led us to hypothesize that the hair loss may instead be caused by the deletion of Scd1 in the SG stem/progenitor cell compartment, which is contiguous with the junctional zone (JZ) and expresses Lrig139,40.
Here, we tested this hypothesis by conditionally deleting Scd1 in Lrig1-CreERT2/+ cells during the first telogen phase, and observed initially normal hair growth but subsequently hair loss. We obtained the Lrig1-CreERT2 mouse from The Jackson Labs, which is constructed differently from the Lrig1-EGFP-CreERT2 mouse reported by Page et al.39 and Jensen et al.40. This Lrig1-CreERT2 mouse has been reported to display greater efficiency of inducible Cre recombination activity compared to Lgr-eGFP-IRES-CreERT2 mice, possibly because the presence of the eGFP-IRES may make the translation of the CreERT2 less efficient. Accordingly, Lrig1-CreERT2 mice may similarly be more efficient at Cre recombination than Lrig1-eGFP-IRES-CreERT2 mice, and thus be more sensitive at marking and deleting in Lrig1+ve cells. In addition to the JZ expression that was also reported by Page et al.39 and Jensen et al.40, our RNA in situ hybridization and lineage tracing data showed Lrig1+ve cells and their labelled daughter cells in multiple epidermal compartments, including the hair follicle lower isthmus, telogen bulge, secondary hair germ and their anagen bulb matrix progeny. Consistent with previous studies, deletion of Scd1 in Lrig1-CreERT2/+ cells resulted in ablation of the SG, though the hair follicle was still able to continue to grow and transition from telogen to anagen. However, the absence of Scd1 in Lrig1-CreERT2/+ hair matrix cells eventually led to a cascade of events which culminated in progressive hair loss during the late anagen phase of adult mice. Our data suggest a model where Scd1 is needed to regulate autocrine Wnt signalling in the matrix and paracrine Shh signalling to the DP cells. Degradation of the DP, in turn, may cause matrix progenitors to lose proliferative ability so that hair growth stops.
## Scd1 is knocked out in Lrig1-CreERT2/+ progeny in the interfollicular epidermis (IFE), junctional zone (JZ), isthmus (IS), sebaceous glands (SG) and hair bulge
We first verified the location of Lrig1-expressing cells in the skin using RNA in situ hybridisation at the first telogen phase. While we observed that Lrig1 is enriched in the JZ, as originally described40–42, we also observed expression in the IFE, IS and the bulge, albeit at lower levels (Fig. 1a). Lineage tracing using Lrig1-CreERT2/+; Rosa-mTmG mice showed Lrig1+ve progeny distributed throughout the epidermis, IS, hair bulge and outer lower bulge cells (secondary hair germ) at 2 days post treatment (Fig. S1a). While previous reports have suggested that Lrig1+ve cells in the hair bulge are not CD34+ve hair follicle stem cells39,40, careful examination of the data suggests that they are present in the outer bulge cells, something which others have not reported. Consistent with this, we detected Lrig1+ve cells in the outer and lower bulge, including the secondary hair germ, when we administered a higher dose of unmetabolized tamoxifen just prior to the start of hair regeneration (Fig. S1a). After 1 month of tracing, these cells contributed not just to the SG but also to the IFE and all the different lineages of the hair follicle, including the matrix progenitors (Mx) (Fig. S1a). However, no Lrig1+ve cells were observed in the DP. In addition, a few Lrig1+ve cell progeny were detected in papillary dermal fibroblasts (Fig. S1a)43,44, and sporadically in intradermal adipocytes (Fig. S1b). Altogether, our data demonstrates that Lrig1+ve progeny cells contribute to the IFE, JZ, IS, SG, hair bulge, matrix progenitors and differentiated cells of the mature hair follicle. Figure 1Phenotypic changes following Scd1 deletion in Lrig1-expressing epidermal hair follicle cells. ( a) RNA in-situ hybridisation of Lrig1 on P19 WT dorsal skin. Polr2A (housekeeping gene) was used as positive control and DapB (*Bacterial* gene) as negative control. ( b) RNA in-situ hybridisation of Scd1, Lrig1 and Duplex Lrig1 and Scd1 on P19 WT Telogen hair follicle. ( c) RNA in-situ hybridisation of Scd1, Lrig1 and Duplex in P45 Control Lrig1+/+; Scd1fl/fl anagen bulb. ( d) RNA in-situ hybridisation of Scd1, Lrig1 and Duplex in P45 Mutant Lrig1-CreERT2/+; Scd1fl/fl anagen bulb. Purple dotted line denotes boundary of DP cells. Black arrowhead denotes loss of Scd1 transcript expression (scale bar 20 μm). ( e) Representative image of shaved Lrig1 Control mouse whole body hair cycle progression ($$n = 10$$ mice), post tamoxifen injection at P19. ( f) H&E stained sections of Lrig1 Control skin at P21, P40, P48 and P52. ( g) Representative image of shaved Lrig1 Mutant mouse whole body hair cycle progression ($$n = 13$$ mice), post tamoxifen injection at P19. ( h) H&E stained sections of Lrig1 Mutant skin at P21, P40, P48 and P52. Blue arrowhead indicates presence of cellular infiltrates. Black arrowhead indicates exposed club-like hair shaft and missing DP (scale bar 50 μm).
During telogen, Scd1 mRNA is highly enriched in the SG and weakly expressed in the telogen hair bulge (Fig. 1b). During anagen, it is strongly expressed in the hair bulb (Fig. 1c). When Scd1 deletion was induced in Lrig1-CreERT2/+ mice at P19, we observed that SGs completely disappeared by P25, 6 days post tamoxifen treatment (Fig. S2a,b). Nevertheless, the hair follicles continued to grow and transition from telogen to anagen (Fig. 1g,h), even though Scd1 expression was greatly reduced in the P25 telogen hair bulge (Fig. S2a) and most of the P45 anagen hair bulb (Fig. 1d). Our data shows that deletion of Scd1 in Lrig1-expressing cells leads to rapid loss of SGs, with no change in hair follicle morphology.
## Deletion of Scd1 in Lrig1-expressing cells leads to abnormal “dysmorphic” hair follicles and progressive hair loss
Previous studies evaluating Scd1 deficiency reported abnormal hair cycling (prolonged anagen phase) and subsequent hair loss36. We observed that HFs progressed from telogen to anagen in Lrig1-CreERT2/+; Scd1fl/fl mutant mice (Fig. 1g). To investigate the specific events leading up to the hair loss, we decided to examine the hair cycle more closely in mice that were shaved every two days. Despite reduced Scd1 in the bulge (Fig. S2a), both control and mutant mice entered anagen from P35 onwards (Figs. 1e,g and S2e, injected mutant and control), suggesting that Scd1 is not required for anagen entry in Lrig1-expressing cells. The timing of the hair cycles of both Lrig1 mutant and control differ from that described in the literature for male C57BL6 mice45, and may have resulted from the mixed 129S6/SvEvTac genetic background that we used (Fig. S2e, un-injected mutant and control), as well as the inhibitory effect that tamoxifen has on hair growth46–48 (Fig. S2e, injected mutant and control). Further tracking of the hair cycle revealed that, while control hair follicles progressed to telogen at P52 (Fig. 1e,f), mutant mice displayed a “dysmorphic” hair coat with fewer hair shafts at P80 (Figs. 1g and S2d). Histological examination of the mutant skin revealed the presence of narrow and elongated hair follicles lacking hair bulb structures that typically contain matrix progenitors and DP (Fig. S2c). Loss of both compartments eventually culminated in exposed “club-like” hair shafts that became surrounded by an intradermal inflammatory infiltrate (Figs. 1h and S2c). This abnormal hair phenotype seemed to present itself during the anagen-catagen transition phase (occurring from P48-52 in our mice), where the mouse coat appeared superficially to still be in anagen (Fig. S2e, injected mutant). The observation that SG loss in the mutant mice did not immediately cause abnormal hair cycling and growth suggests that the hair phenotype is independent of the loss of the SG and sebum, in contrast to what has been previously proposed36. These observations suggest that Scd1 is required in Lrig1-expressing cells for hair follicle maintenance in late anagen, and hair cycle progression from anagen to catagen.
## Loss of Scd1 in matrix progenitors leads to abrogation of DP marker expression
Upon closer histological examination, we found that the hair follicles in mutant skin presented as abnormal, “dysmorphic” structures apparently lacking DPs (Figs. 1h and S2c). To test this notion, we stained the skins for Alkaline Phosphatase (AP), a widely accepted DP marker for anagen and catagen hair follicles17,45,49. We also probed for Igfbp3 and APCDD1, which we had previously found to be highly expressed in DP cells throughout the hair cycle (unpublished data). Control hair DP exhibited well-defined AP staining from P42 to P48 during anagen and catagen (Fig. 2a–d). On the other hand, mutant hair DP stained positively for AP at P42 (Fig. 2a,d) but much more weakly at P45 (Fig. 2b,d) and not at all from P48 onwards (Fig. 2c,d). Consistent with this, Igfbp3 and APCDD1 were highly expressed in control but not mutant hair DP from P48 onwards (Figs. 2e–h and S3a–d). Despite the presence of αSMA+ve DP precursors50–53 (Fig. S3u), mutant hair DP marker expression began to disappear at P45 (Fig. 2b,d). These data suggests that Scd1 is required in Lrig1-expressing cells to maintain DP marker expression. Figure 2Altered expression of dermal papilla and hair matrix markers following Scd1 deletion. ( a–c) Representative images of alkaline phosphatase (AP) stain of Lrig1 Control and Mutant hair follicles at P42, P45 and P48. Red arrowhead indicates the presence of AP+ve DP in anagen and catagen hair follicles. ( d) Quantification of AP+ve DP in Lrig1 Control and Mutant hair follicles at P42, P45, P48 and P52 ($$n = 2$$ mice, 100 hair follicles per mouse) Student's t-test applied, *$p \leq 0.05$ and **$p \leq 0.001.$ ( e,f) RNA in-situ hybridisation of Igfbp3 in DP cells at P48 and P52. Black arrowhead indicates the presence of Igfbp3+ve DP in hair follicles. ( g,h) RNA in-situ hybridisation of APCDD1 in DP cells at P48 and P52. Black arrowhead indicates the presence of APCDD1+ve DP in hair follicles. ( i–k) Ki67 antibody stained sections of Lrig1 control and Mutant skins at P45, P48 and P52. White arrowhead indicates Ki67+ve matrix. ( l) Percentage of Ki67+ve cells in Lrig1 Control and Mutant matrix progenitors at P45, P48 and P52 ($$n = 3$$ mice, 12 hair follicles analysed per mouse). Student’s t-test applied, *$p \leq 0.05$ and **$p \leq 0.001.$ Yellow opaque line denotes analysed area of expression. ( m–o) RNA in-situ hybridisation of Msx2 in matrix cells, IRS and pre-cortex of Lrig1 Control and Mutant skins at P45, P48 and P52. Black arrowhead indicates Msx2+ve matrix cells (scale bar 50 μm).
## Matrix cell proliferation is abrogated in the context of Scd1 deletion-induced DP marker disappearance
Since the DP acts as a signalling centre that directs the surrounding matrix cells to proliferate4,18,19,54, we asked whether matrix cells were affected by Scd1 deletion-induced DP degradation. Hair bulbs of control mice in early catagen at P48 showed strong Ki67 expression, while the majority of hair follicles in the mutant mice showed either reduced or no Ki67 staining (3.8-fold, $$p \leq 0.001$$) at the base (Fig. 2i,j,l). Neither control nor mutant hair follicles at P52 showed Ki67 staining ($$p \leq 0.0756$$) (Fig. 2k,l), though control follicles appeared to be in telogen while mutant follicles still appeared “dysmorphic”. In addition, we observed an absence of the matrix marker Msx255,56 in mutant hair from P48 onwards (Figs. 2n,o and S3g,h). Similarly, at P52, the “dysmorphic” mutant hair still did not have any Msx2+ve cells (Figs. 2o and S3i–j). TUNEL+ve ($$p \leq 0.9701$$) and Cleaved Caspase-3+ve (CC3) ($$p \leq 0.2302$$) cells were detected at the bottom of both control and mutant hair follicles from P48 onwards (Fig. S3p,m). Control hair follicles eventually lacked Msx2+ve, TUNEL+ve ($$p \leq 0.4622$$) and CC3+ve ($$p \leq 0.2341$$) cells because they were in telogen and did not have hair bulbs (Figs. 2o, S3p,m, respectively). Our data suggests that Scd1 is required in Lrig1-expressing cells to regulate matrix progenitor cell proliferation and Msx2 expression.
## Scd1 deletion affects Wnt signalling in matrix progenitors
Given that Wnt signalling is involved in hair stem cell maintenance57–60 and that Wnt ligands require palmitoylation for Wnt activation33, we hypothesized that Scd1 ablation might affect Msx2 expression in matrix cells by altering Wnt signalling in matrix progenitors. We found that Wnt signalling is significantly downregulated prior to DP degradation at P45. Using RNA in situ hybridisation, we observed that the expression of Axin2, a well-known Wnt target gene57,61, was significantly reduced (2.85-fold, $$p \leq 0.0002$$) in P42 matrix progenitors where Scd1 was ablated (threefold, $$p \leq 0.0138$$), while it remained highly expressed in areas where Scd1 was still present (Figs. 3a,b,d and S5a,b). Consistent with reduced Axin2 expression, the expression of Wls, another Wnt target gene13, was also significantly reduced (1.54-fold, $$p \leq 0.0004$$) in matrix cells after Scd1 deletion (Fig. 3c,d). Our data suggests that Scd1 deletion in Lrig1-expressing cells reduces Wnt signalling in matrix progenitors. Figure 3Scd1-deletion reduced expression of genes in key hair follicle signalling pathways, Wnt and Hedgehog. ( a) RNA in-situ hybridisation of Scd1 in Lrig1 Control and Mutant hair bulb at P42. Purple arrowhead indicates loss of Scd1 transcript expression. ( b) Duplex RNA in-situ hybridisation of Scd1 and Axin2 in Lrig1 Control and Mutant hair bulb at P42. Purple arrowhead indicates loss of Axin2 transcript expression. ( c) Duplex RNA in-situ hybridisation of Scd1 and Wls in Lrig1 Control and Mutant hair bulb at P42. Purple arrowhead indicates loss of Wls transcript expression. ( d) Scd1, Axin2, Wls, PPIB and DapB transcripts by spot detection in P42 Lrig1 Control and Mutant mice ($$n = 3$$ animals, 12 hair follicles analysed per animal). * $p \leq 0.05$ and **$p \leq 0.001.$ Yellow solid line indicates area of representative image analysed for transcripts. ( e) Duplex RNA in-situ hybridisation of Scd1 and Shh in Lrig1 Control and Mutant hair bulb at P42. Purple arrowhead indicates loss of Shh transcript expression. ( f) Shh transcripts by spot detection in adjacent matrix of P42 Lrig1 Control and Mutant mice ($$n = 3$$ animals, 12 hair follicles analysed per animal). * $p \leq 0.05$ and **$p \leq 0.001.$ Yellow solid line indicates area of representative image analysed for transcripts. ( g) Duplex RNA in-situ hybridisation of Scd1 and Ptch1 in Lrig1 Control and Mutant hair bulb at P42. ( h) Duplex RNA in-situ hybridisation of Scd1 and Gli1 in Lrig1 Control and Mutant hair bulb at P42. Purple dotted line denotes boundery of DP cells. Purple arrowhead indicates loss of Shh/Ptch1/Gli1 expression and area analysed (scale bar 20 μm). ( i) Ptch1 and Gli1 transcripts by spot detection in DP of P42 Lrig1 Control and Mutant mice ($$n = 3$$ animals, 12 hair follicles analysed per animal). * $p \leq 0.05$ and **$p \leq 0.001.$ Yellow solid line indicates area of representative image analysed for transcripts. ( j) Duplex RNA in-situ hybridisation of Scd1 and Ptch1 in Lrig1 Control and Mutant hair bulb at P45. Purple arrowhead indicates loss of Ptch1 transcript expression. ( k) Duplex RNA in-situ hybridisation of Scd1 and Gli1 in Lrig1 Control and Mutant hair bulb at P45. Purple arrowhead indicates loss of Gli1 transcript expression. ( l) Ptch1 and Gli1 transcripts by spot detection in DP of P45 Lrig1 Control and Mutant mice ($$n = 3$$ animals, 12 hair follicles analysed per animal). * $p \leq 0.05$ and **$p \leq 0.001.$ Yellow solid line indicates area of representative image analysed for transcripts. ( m) Ptch1 and Gli1 transcripts by spot detection in Matrix of P45 Lrig1 Control and Mutant mice ($$n = 3$$ animals, 12 hair follicles analysed per animal). * $p \leq 0.05$ and **$p \leq 0.001.$ Yellow solid line indicates area of representative image analysed for transcripts. Purple dotted line denotes boundary of DP cells (scale bar 20 μm).
Matrix cells express a variety of Wnts58,62, suggesting that matrix cell Wnt signalling may proceed in an autocrine manner. To determine if reduced Wnt activity in matrix progenitors could be attributed to the deficiency of specific ligands or receptors, we surveyed the expression of 17 Wnt ligands (Fig. S4a–q) and 10 Frizzled receptors (Fig. S4r–z) using RNA in situ hybridisation. We found that the expression of Wnt ligands, particularly in the matrix (Fig. S4f,g,i,k,n,o), and receptors was largely similar in control and mutant skin (Fig. S4r–z). Taken together, our data suggests that reduced Wnt signalling in matrix progenitors is not due to changes in Wnt ligand and receptor expression.
## Scd1 ablation is followed by the loss of Shh in matrix progenitors and paracrine Shh signalling in the DP
Several studies have shown that Wnt activation can induce Hedgehog signalling in hair follicles63–65 where Shh signalling is known to regulate hair morphogenesis and hair cycling11,29,66–68, and HFSC-associated events28,30. Post-translational palmitoylation also seems to enhance secretion of Hedgehog ligands69–73. Therefore, we next examined whether Hedgehog signalling was affected by Scd1 deletion in the matrix. Following Scd1-mediated Wnt attenuation, Shh expression was significantly reduced (fourfold, $$p \leq 0.0035$$) in mutant hair matrix cells neighbouring the DP at P42 (Fig. 3e,f), while there was no significant change in the expression of Hedgehog target genes Ptch1 ($$p \leq 0.7255$$) and Gli1 ($$p \leq 0.0594$$) in mutant hair matrix and DP cells (Fig. 3g,h,i). This was followed at P45 by a significant loss of Ptch1 (sixfold, $$p \leq 0.0007$$) and Gli1 (1.6-fold, $$p \leq 0.0113$$) despite the presence of Scd1 in the DP (Fig. 3j,k,l), whereas Gli1 and Ptch1 remained expressed in Scd1-deleted matrix cells (Fig. 3j,k,m). Ihh and Dhh were not expressed in either control or mutant skins at P45 (Fig. S6a,b), including SGs (Fig. S6e–g). These data suggests that Scd1 deletion in Lrig1-expressing cells leads to the loss of Shh expression in matrix progenitors, and Hh signalling in the DP.
## Discussion
Although it is well established that the DP, as an important skin niche, promotes the maintenance of stem and progenitor cells in the hair follicle2,4,27,59, what sustains the DP in turn remains largely obscure. Our findings collectively support a model where matrix progenitors derived from Lrig1+ve JZ and outer lower bulge cells (secondary hair germ) (Fig. S7a,b) require Scd1 to regulate DP cells and enable anagen-catagen transition (Fig. S7c–e).
Consistent with the aberrant hair cycle phenotype reported in mice with germline36 and epidermal-specific (K14Cre) deletion of Scd1, we found that Lrig1CreERT2/+; Scd1fl/fl mice exhibit abnormal “dysmorphic” hair with exposed hair shafts that gradually stopped growing during the second hair cycle. This phenotype suggests that Scd1 deletion in Lrig1-expressing cells leads to abnormal “dysmorphic” hair and progressive hair loss. In addition, we found that the expression of DP markers in Lrig1CreERT2/+; Scd1fl/fl hair follicles disappeared at P45 (Fig. 2b) prior to the loss of proliferative Msx2+ve matrix cells at P48 and stalling of hair growth in the late anagen phase (Fig. 2i,m). These data show that Scd1-deletion affects DP and matrix marker expression, as well as matrix cell proliferation in mutant hair follicles. As we did not detect retention of hair fibres in the upper hair follicles (Fig. S8), we conclude that exposure of club-like hair shafts at the follicle base was not the result of accumulated inner root sheath (IRS) hair fibres and sebum loss, as previously proposed36. Moreover, immune cell infiltrates appeared much later, after DP marker expression was lost and when hair shafts became exposed (Fig. S3s). This observation suggests that inflammation is not the reason why the DP markers are lost. Considering that immunosuppressive factors are thought to be expressed in hair matrix cells60,61, we propose that intradermal inflammation likely occurred after DP marker disappearance and hair bulb shrinkage, and thus could not have caused the bulb to diminish.
We have interpreted the mutant hair follicles as “dysmorphic”, and surmise that the phenotype occurs during the anagen to catagen transition because the molecular and signalling pathway changes occur in our animals at P45, when the hair coat and follicles clearly still exhibit anagen morphology and characteristics such as proliferative matrix. However, while the majority of Lrig1 mutant hair follicles remained in an abnormal “dysmorphic” phase, we detected some follicles with catagen-like morphology at P48 (Fig. 2i) that appear to still have some DP-like structures attached. While we believe that this could be the result of incomplete Cre-mediated deletion of Scd1 in Lrig1-expressing cells, we cannot exclude the possibility that some abnormal mutant hairs may have actually managed to transition into early catagen and then remained trapped in that state. Regardless, the follicles appear unable to transition any further, and we detect no DP-like structures in P52 mutant skin.
The DP is generally thought to communicate with matrix cells to promote hair growth and specify cell lineages in the hair follicle2,4,13,59. We found that the expression of Wnt ligands, particularly in the matrix (Fig. S4f,g,i,k,n,o), and receptors was largely similar in control and mutant skin (Fig. S4r–z). Our data suggest that loss of Scd1 activity, rather than changes in Wnt ligand/receptor expression, disrupts autocrine Wnt/β-catenin signalling in matrix progenitors. Consistently, we found that Shh ligand expression in matrix progenitors was abolished following decreased Wnt signalling at P42 (Fig. 3e,f), just prior to DP cell degradation at P45. This was subsequently followed by attenuation of Hedgehog signalling in the DP, when it began to degenerate at P45 (Fig. 3j–l). We speculate that Lrig1-expressing matrix progenitors rely on Scd1 to regulate autocrine Wnt signalling and paracrine Hedgehog signalling.
Our lineage tracing data show Lrig1+ve matrix progenitors derived from Lrig1+ve ancestor cells in the JZ, bulge and outer lower bulge (secondary hair germ) (Fig. S1a). Our data also indicate that Lrig1+ve cells can be found in the (A) IFE, (B) dermal fibroblast and (C) intradermal adipocyte compartments (Fig. S1a,b). ( A) Most Lrig1-expressing IFE and (B) dermal fibroblasts reside in the upper papillary dermis (Fig. S1a), distant from the mature DP. We did not detect Hh ligand expression in the IFE and papillary dermis, where Lrig1+ve fibroblasts reside (Fig. S6e,f). Moreover, the DP appears unaffected in late anagen hair follicles of Pdgfra-CreER; Smofl/fl mice74. ( C) While intradermal adipocytes have been shown to stimulate hair growth through secreted adipogenic factors75–77, genetic ablation of mature and adipocyte precursors did not affect DP cells during development and post-natal maintenance77. Furthermore, K14-mediated Scd1 knockout mice lose hair despite having intact Scd1 in the skin adipocyte compartment37. Taken together, these observations suggest that Lrig1+ve IFE keratinocytes, papillary dermal fibroblasts and intradermal adipocytes are unlikely to mediate Hedgehog signalling in our mutant mice. While Hedgehog signals can have both short- and long-range activity, we speculate that the matrix cells located adjacent to the DP are the most likely source of Hedgehog ligands here. It would be ideal to functionally test this using genetic mouse models for matrix-specific hedgehog ligand deletion but that is beyond the scope and resources of the present study.
While it is well recognised that the niche signals to and regulates stem and progenitor cells, the signals that maintain the niche and the sources of those signals remain poorly understood. Our data indicate that epithelial progenitor cells could signal back to their niches by providing at least one source of ligands to activate the Hedgehog signalling that DP cells may rely on for its maintenance and survival (Fig. 3j–l). The apparent requirement for this signal during the anagen-catagen transition raises several questions: how do progenitor cells sense hair cycle transitions and produce the right ligands? Does the niche only require the pro-survival signal at one point in the hair cycle, and if so, why? If not, what are the other signals and cells that promote niche survival? Are these signals conserved in other niche systems? Answers to these questions would yield new insights into the relationship between stem cells and their niches.
## Experimental animals
Lrig1-CreERT2/+74, Scd1fl/fl76 and Rosa26-mTmG77 mice have been described previously. Lrig1-CreERT2/+ and Rosa26-mTmG mice were obtained from The Jackson Laboratory, and Scd1fl/fl mice were a gift from Dr James M. Ntambi, University of Wisconsin-Madison. All knockout and lineage tracing experiments were performed in postnatal day 19 (P19) animals. For knockout experiments in Lrig1-CreERT2/+ mice, a single dose of tamoxifen dissolved in corn oil (4 mg/ml/25 g body weight) was administered via intraperitoneal injection. Bioethics Council along with A*STAR Animal Care and Use Committee (IACUC no. 130883) approved the experimental study. We confirm that this study was performed in accordance with the guidelines and regulation of Bioethics Council and A*STAR Animal Care and Use Committee, and reported in accordance with ARRIVE guidelines.
## Histology and immunostaining
Animals were sacrificed using CO2 asphyxiation followed by cervical dislocation. Skins were removed from the dorsal region. For paraffin sections, tissues were fixed in $4\%$ PFA overnight at room temperature with shaking. Tissues were then washed in phosphate buffered saline (PBS) and dehydrated through a series of ethanol baths, followed by immersion in xylene and paraffin wax. Tissues were cut into 7-μm thick sections using a Leica RM2255 microtome (Leica Microsystems). Sections were rehydrated and counterstained with Hematoxylin and Eosin where specified.
For frozen sections, tissues were fixed in $4\%$ PFA overnight on the roller at 4 °C, washed in PBS, and stored overnight in $30\%$ sucrose (w/v) at 4 °C. Tissues were embedded in OCT medium and stored at − 80 °C, and then sectioned at varying thicknesses using a Leica CM3050S cryostat (Leica Microsystems).
For immunostaining, frozen 7–10 μm thick sections were washed in PBS and incubated in blocking buffer ($2\%$ normal goat serum (catalogue no. 005-000-121, Jackson ImmunoResearch) and $0.2\%$ Triton X in PBS) for 1 h at room temperature, then incubated with primary antibody diluted in blocking buffer overnight at 4 °C. This was followed by incubation with secondary antibody diluted in blocking buffer for 1 h at room temperature and mounting in Prolong Gold with DAPI mounting medium (catalogue no. P-36931, Life Technologies). All washes were performed using PBS. The following antibodies were used: Rat monoclonal anti-Ki67 (catalogue no. 14-5698-82, eBioscience), Rabbit polyclonal anti-mouse Scd1(M38) (catalogue no. # 2438, Cell signalling), and Goat anti-Rabbit conjugated to Alexa Fluor A568 (catalogue no. A11036, Life Technologies). The following dyes were used: LipidTOX Green neutral lipid stain (catalogue no. H34475, Life Technologies), Phalloidin Alexa Fluor 568 (catalogue no. A12380, Life Technologies).
## Alkaline phosphatase stain
Fixed frozen tissues were sectioned at 10-16 μm thickness using a Leica CM3050S cryostat (Leica Microsystems). Frozen sections were washed in PBS and primed with Tris–EDTA pH9.1 for 5 min at room temperature, followed by incubation in AP solution (5 ml Tris–EDTA pH9.1, 33 µl of NBT and 16.5 µl of BCIP from Promega) for 5–7 min in room temperature. Tissues were then washed in deionized water and dehydrated in solutions of increasing ethanol concentration ($70\%$, $80\%$, $90\%$, and 3 times at $100\%$), followed by xylene prior to mounting with Cytoseal (Thermo Scientific).
## RNA in situ hybridisation
Skin was harvested and fixed in $4\%$ PFA for 20–24 h at room temperature, dehydrated and then embedded in paraffin. Tissue sections were cut at 7-μm thickness, air-dried at room temperature, and processed for RNA in situ detection using the RNAscope 2.5HD Red Detection assay and RNAscope2.5HD Duplex assay according to the manufacturer’s instructions (Advanced Cell Diagnostics). RNAscope probes used were as follows: Scd1, Axin2, Wls, Wnt1, Wnt2, Wnt2b, Wnt3, Wnt3a, Wnt4, Wnt5a, Wnt5b Wnt6, Wnt7a Wnt7b, Wnt9a, Wnt9b, Wnt10a, Wnt10b, Wnt11, Wnt16, Fzd1, Fzd3, Fzd4, Fzd5, Fzd6, Fzd7, Fzd8, Fzd9, Fzd10, Shh, Gli1, Ptch1, all of which were detected using Fast Red and HRP-based black detection reagent.
## Microscope imaging
All sections were imaged using Olympus FV3000 RS Inverted Confocal and Zeiss AxioImager microscopes. Image processing was performed with Fiji software version 1.0 (written by Wayne Rasband).
## Statistical analysis of immunohistochemistry staining
For RNA transcript quantification, transcript/dots within the region of interest (DP or hair matrix) were measured by area factor analysis. Dots per micron were calculated by dividing the area of red/black color spots by the area of each region of interest. For quantification of Ki67 +ve (magenta) cells within the hair bulb, the percentage of Ki67 +ve cells were calculated by area factor analysis, by dividing the area of magenta spots/cells (Ki67 + ve) by number of nuclei within the bulb matrix.
To evaluate statistical significance, all measurements were pooled for each animal, with mean and SEM values calculated. Statistical analyses were generated using Prism (Graphpad, Prism 9 version 9.3.1) to perform unpaired Student t tests, with statistically significant scores determined by p-values below 0.05 (*$P \leq 0.05$) and very statistically significant scores by p-values below 0.001 (**$P \leq 0.001$).
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30411-7.
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|
---
title: Experimental study on the effect of chlorhexidine gluconate (CG)-induced atrial
fibrillation on renal water and sodium metabolism
authors:
- Shuyu Li
- Heng Pei
- Yaomeng Huang
- Da Liu
- Liqun Yang
- Qi Zhang
- Zhijun Wang
journal: Scientific Reports
year: 2023
pmcid: PMC10006165
doi: 10.1038/s41598-023-30783-w
license: CC BY 4.0
---
# Experimental study on the effect of chlorhexidine gluconate (CG)-induced atrial fibrillation on renal water and sodium metabolism
## Abstract
To construct an animal model of atrial fibrillation and observe the effect of acute atrial fibrillation on renal water and sodium metabolism in mice. A total of 20 C57 mice were randomly assigned to 2 groups ($$n = 10$$/group): control group (CON) and atrial fibrillation group (AF). The mice model of atrial fibrillation was induced by chlorhexidine gluconate (CG) in combination with transesophageal atrial spacing. The urine of the two groups of mice was collected, and then we calculate the urine volume and urine sodium content. The expression of TGF-β and type III collagen in the atrial myocardium of the two groups was detected by immunohistochemistry and Western Blot. The levels of CRP and IL-6 in blood were observed by ELISA, and the NF-κB, TGF-β, collagen type III, AQP2, AQP3, AQP4, ENaC-β, ENaC-γ, SGK1 and NKCC proteins in the kidneys of the two groups of mice was observed by Western Blot. Compared with CON, the expression of TGF-β and type III collagen in the atrial myocardium of the mice in AF were increased, the levels of CRP and IL-6 in the blood in AF were increased, and the renal NF-κB, TGF-β, type III collagen AQP2, AQP3, ENaC-β, ENaC-γ, SGK1 and NKCC protein expression in AF were up-regulated. The level of urine volume and urine sodium content in AF were significantly reduced. In the acute attack of atrial fibrillation, the formation of renal inflammatory response and fibrosis is activated, and the renal water and sodium metabolism is hindered, which is related to the up-regulated of the expressions of renal NKCC, ENaC and AQPs.
## Introduction
Atrial fibrillation (AF) is a commonly encountered arrhythmia in clinical practice, and a major predisposing factor for heart failure. Despite significant progress in its treatment, morbidity, disability and mortality associated with AF continue to be high1,2. Numerous studies have implicated atrial fibrosis, sympathetic activation, reentry trigger, oxidative stress and inflammation in the occurrence and progression of AF. The reentry trigger leads to the onset of AF, while sympathetic activation provides the basis for electrical remodeling. Atrial interstitial fibrosis disrupts and blocks excitation conduction between cardiomyocytes, and myofibroblasts have been shown to have a direct impact on the development of atrial arrhythmias. This structural remodeling sets the pathological basis for the onset and maintenance of AF, while the inflammatory state promotes the degeneration and fibrosis of atrial myocytes, alters the electrophysiological characteristics of cardiomyocytes, and increases susceptibility to AF. Inflammation is a systemic response, and a significant number of studies have identified inflammatory markers in circulating blood of AF patients3. The transforming growth factor β (TGF-β), as an inflammatory factor, is a pivotal growth factor that promotes fibrosis, stimulates fibroblast proliferation and differentiation, increases collagen expression, and encourages fibrosis progression. Nuclear transcription factors are also important in regulating inflammation. For instance, the nuclear factor-kappaB (NF-κB) is activated by inflammatory cytokines, resulting in nuclear translocation, leading to a significant increase in expression of inflammation-related genes and promoting an inflammatory response4. Although many studies have reported that AF is associated with a high risk of stroke, heart failure and mortality, few studies have examined the association between acute AF attack and peripheral organ damage, such as kidney damage. Therefore, our study aimed to induce AF in mice model using chlorhexidine gluconate (CG), and to observe whether the acute attack of AF could activate and maintain the formation of renal inflammation and fibrosis, and affect water and sodium metabolism at the physiological and molecular level.
## Experimental methods
Experimental animals and grouping: 20 C57 male mice (18–22 g) were provided by Beijing Huafukang Biotechnology Co., Ltd. They were given clean drinking water and kept in indoor temperature 22–24 °C, humidity 50–$60\%$, light cycle 12 h, light and dark were reared alternately and divided into cages. And they were randomly divided into 10 mice in CON and 10 mice in AF. All animal experiments were conducted in accordance with the guidelines of the Institutional Animal Care Committee, and experiments were conducted in accordance with the Arrival Guide. The animal experiment protocol was approved by the Animal Ethics Committee of Hebei Medical University (Approval Number: 20210366).
## Main reagents
The main reagents are chlorhexidine gluconate stock solution (McLean), pentobarbital (provided by the Chemistry Center Laboratory of Hebei Medical University), CRP and IL-8 ELISA kit (Beijing Solarbio company), immunohistochemical kit (Beijing Zhongshan Jinqiao), BCA protein quantification kit (Beijing Solarbio Company), rabbit anti-GAPDH (Servicebio Company), rabbit anti-TGF-β (Servicebio Company), rabbit anti-NF-κB(Genetex Company), rabbit anti-collagen III (Servicebio Company), rabbit Anti-NKCC (Abclonal Company), rabbit anti-ENaC-α (Stressmarq Company), rabbit anti-ENaC-γ (Affiniti Company), rabbit anti-SGK1 (Huaan Biotechnology Company), rabbit anti-AQP2 (Abcam Company), rabbit anti-AQP3 (Abclonal Company), rabbit anti-AQP4 (Proteintech).
## Main instruments
PowerLab biological signal acquisition and analysis system (ADINSTRUMENTS company), Scisense catheter electrode (Transonic company), XD-5A cardiac electrophysiological stimulator (Suzhou Industrial Park Dongfang Electronic Instrument Factory), small animal ventilator (Riveworld Life Technology Co., Ltd. company).
## Establishment of chlorhexidine gluconate model in mice
Anesthesia was induced in the mice by a peritoneal injection of $3\%$ pentobarbital (50 mg/kg). Once an appropriate level of anesthesia was achieved, the mice were placed in a supine position and secured with medical tape on a wristband. The breast area was sanitized using $75\%$ alcohol and the skin was shaved. An oral endotracheal tube was inserted, and the small animal mechanical ventilation was set at a frequency of 100 times/min, with a tidal volume of 2.5 ml and an inspiration-to-expiration ratio of 1:2. The skin, subcutaneous fascia, major pectoral, minor pectoral, and intercostal space were dissected layer by layer to expose the heart. The lungs were protected with small cotton balls, and the pericardium was carefully removed. Sterile CG was gently and evenly applied to the left and right atrium and atrial appendage. After removing the cotton balls, the chest was closed layer by layer. The mice were then placed on a temperature-controlled pad, and the endotracheal tube was removed once the mice had regained consciousness. Following the completion of the experiment, all devices were cleaned and sanitized. In the control group (CON), the mice were subjected to thoracotomy without pericardial tearing and CG application. No deaths occurred in the CON group, while one death occurred in the experimental group (AF).
## Cardiac electrophysiological indexes of mice were detected and recorded through esophageal pacing
On the fourth day following the establishment of the CG-induced atrial fibrillation (AF) model, the mice were anesthetized with $3\%$ pentobarbital (50 mg/kg) and maintained at a body temperature of 37 °C. The Scisense Millar 1.1F electrophysiological catheter electrode was inserted into the esophagus near the left atrium, and a II limb lead body surface electrocardiogram (ECG) was recorded using a computerized data acquisition system. Atrial fibrillation induction was tested with 5-s pulses through the catheter electrode, using high-frequency stimulation S1S2 at a frequency of 50 Hz. After a 5-min stabilization period, the electrical stimulation was repeated three times. Atrial fibrillation induction was considered successful if two out of three stimulation tests resulted in AF. Successful AF induction was defined as a rapid and irregular atrial rhythm lasting at least one second. Transesophageal stimulation was performed in all mice, including those in the control group (CON) and the experimental group (AF). No animal mortality occurred during AF induction.
## Collection of blood, urine and sample of heart and kidney
Urine Collection: Following induction, mice were individually placed in metabolic cages and allowed to feed and drink ad libitum for 12 h. Urine volume was collected over this period.
Collection of Blood, Heart, and Kidney Samples: After 12 h of feeding, the mice were anesthetized with $3\%$ pentobarbital, and the abdominal cavity was opened to collect blood via the inferior vena cava using a coagulation tube. The collected blood was kept at 4 °C. The kidneys and heart were quickly removed, cleaned with ice-cold normal saline, and dried with filter paper. The left atrial appendage was identified, and the left atrial tissue was separated below the left atrial appendage. For each group, the left ear tissue from four animals was partially transformed into paraffin wax samples, and the remaining tissue was stored with liquid nitrogen. The kidney tissues were also stored with liquid nitrogen.
## Enzyme-linked immunosorbent assay (ELISA)
The blood CRP and IL-6 levels were detected by Elisa method, and the experimental procedures were carried out in strict accordance with the instructions of the Elisa kit. The OD value was measured with a microplate reader, and the average value was taken for each group.
## Immunohistochemistry
Immunohistochemical method was used to qualitatively detect the expression of TGF-β and collagen III in the atrium, and the experimental steps were carried out in strict accordance with the instructions of the immunohistochemical kit. The positive expression in the immunohistochemical section is yellow. Under the 200X field of view, 4–5 fields of view are randomly selected from each section for shooting.
## Western blot
Protein lysate (RIPA) reagent was used to extract the total protein of atrium muscle and kidney, and BCA kit was used for protein quantification. Add 20 μg of total protein to the loading buffer, separate with $10\%$ SDS-PAGE gel, transfer to PVDF membrane, block with $5\%$ skimmed milk shaker for 2 h, add primary antibody, incubate overnight at 4 °C with shaker, and then the secondary antibody was incubated at room temperature for 40 min on a shaker. Finally the bands were counted and analyzed using a Bio-Rad gel imager and Image J software.
## Experimental statistical processing
SPSS 23.0 statistical software was used for statistical analysis. The measurement data were expressed as mean ± standard deviation. The measurement data conforming to the normal distribution were analyzed by t test, and $P \leq 0.05$ was considered statistically significant.
## Ethics statement
The animal study was reviewed and approved by the Central Laboratory of the first hospital of the Hebei Medical University—the experimental platform.
## Results 1. The inflammatory response and collagen production of atrial myocytes in CG induced atrial fibrillation model group were increased
By immunohistochemistry, we observed that the structure of atrial myocytes in the control group was intact, with evenly distributed myocardial fibers arranged both vertically and horizontally (Fig. 1A). In contrast, the atrial myocytes in the group with atrial fibrillation (AF) showed oval or polygonal shapes, with clear cytoplasm, edema, and necrosis, occasional vacuoles, and disordered myocardial fibers. The normal network structure was also lost (Fig. 1B). Moreover, the expression of TGF-β and type III collagen was significantly higher in the AF group than in the control group, as shown by immunohistochemistry (Fig. 1C–F). Quantitative analysis by Western Blot revealed a significant increase in the expression of TGF-β and type III collagen in atrial myocytes of the mice with AF compared to the control group ($P \leq 0.05$) (Fig. 1G–I). These results indicate that the CG-induced aseptic atrial myocardium inflammation and fibrosis were successful. Figure 1The inflammatory response and collagen production of atrial myocytes were increased in the CG induced atrial fibrillation model group. ( A) Atrial myocytes histopathologic change in CON was observed by hematoxylin and eosin (HE) staining. ( B) Atrial myocytes histopathologic change in AF was observed by HE staining. ( C) TGF-β expression in atrial myocytes in CON was detected by immunohistochemistry. ( D) TGF-β expression in atrial myocytes in AF was detected by immunohistochemistry. ( E) Collagen III expression in atrial myocytes in CON was detected by immunohistochemistry. ( F) Collagen III expression in atrial myocytes in AF was detected by immunohistochemistry. ( G) Results of Western blotting detecting Collagen III and TGF-β expressions in atrial myocytes in CON and AF. ( H) Comparison of the expression of Collagen III in atrial myocytes in CON and AF. ( I) Comparison of the expression of TGF-β in atrial myocytes in CON and AF.
## Electrophysiology
The ECG waveform of the mice in CON is shown in (Fig. 2A), and the ECG waveform of the mice in AF is shown in (Fig. 2B). Compared with CON, the ECG of the mice in AF showed no P wave and the arrhythmia was absolutely arrhythmic, which was the ECG manifestation of atrial fibrillation. This indicated that the CG-induced mice atrial fibrillation model was successfully prepared. Figure 2The feature of ECG waveform in two mice groups. ( A) The feature of ECG waveform in CON mouse. ( B) The feature of ECG waveform in AF mouse.
## The expression of inflammatory factors in blood, inflammatory response and collagen production in kidney increased in AF mice
Relative to CON, there was a significant increase in plasma CRP and IL-6 concentrations in AF by ELISA, $P \leq 0.05.$ ( Table 1) This finding indicated that AF acts on systemic inflammatory responses. In comparison with CON, the expression of the NF-κB, TGF-β and collagen type III proteins were significantly higher in the AF group, $P \leq 0.05.$ ( Fig. 3A–D) Which indicated there were a prominent inflammation and fibrosis in kidney in AF.Table 1Comparison of urine volume, urinary Na +, plasma CRP and IL-6 levels between 2 groups (x ± s).GroupUrine output (ml/12 h)*Urinary sodium* (umol/12 h)Plasma CRP (μg/L)Plasma IL-6 (ng/L)Control group ($$n = 10$$)0.276 ± 0.05832.840 ± 7.045154.100 ± 12.7696.570 ± 4.849Atrial fibrillation group ($$n = 9$$)0.096 ± 0.04312.030 ± 5.219237.600 ± 27.31131.000 ± 8.017t-value7.6797.2448.68611.46p-value < 0.0001 < 0.0001 < 0.0001 < 0.0001Figure 3Inflammation and collagen production in mice kidney were increased in AF. ( A) Results of Western blotting detecting Collagen III, NF-κB and TGF-β expressions in kidneys in CON and AF. ( B) Comparison of the expression of Collagen III in kidneys in CON and AF. ( C) Comparison of the expression of NF-κB in kidneys in CON and AF. ( D) Comparison of the expression of TGF-β in kidneys in CON and AF.
## Effects of acute onset of atrial fibrillation on renal water and sodium metabolism, renal Aquaporins (AQPs) channels and sodium transporter proteins in mice
Urinary volume and urinary sodium concentrations were measured. We found that, compared with CON, the urine and sodium excretion in AF were significantly reduced, $P \leq 0.05.$ This was indicating that the water and sodium metabolism of mice in AF was impaired. ( Table 1) Relative to CON, the levels of AQP2, AQP3, ENaC-β, ENaC-γ, SGK1 and NKCC proteins expression were increased significantly in AF through Western Blot. $P \leq 0.05.$ ( Fig. 4B–E,G,H). There was no difference in the expression of AQP4 proteins between the two groups. ( Fig. 4F). This finding suggested that acute onset of AF affected renal regulation of water and sodium metabolism, which was related to up-regulation of AQPs channels and sodium transporter proteins. Figure 4Effects of acute atrial fibrillation on renal water channels and sodium transporters in mice. ( A) Results of Western blotting detecting NKCC, ENaC-β, ENaC-γ, SGK1, AQP4, AQP3 and AQP2 expressions in kidneys in CON and AF. ( B) Comparison of the expression of NKCC in kidneys in CON and AF. ( C) Comparison of the expression of ENaC-β in kidneys in CON and AF. ( D) Comparison of the expression of ENaC-γ in kidneys in CON and AF. ( E) Comparison of the expression of SGK1 in kidneys in CON and AF. ( F) Comparison of the expression of AQP4 in kidneys in CON and AF. ( G) Comparison of the expression of AQP3 in kidneys in CON and AF. ( H) Comparison of the expression of AQP2 in kidneys in CON and AF.
## Discussion
This study found that the onset of atrial fibrillation (AF) was linked to myocardial inflammation and fibrosis. The expression levels of TGF-β and collagen III proteins were higher in AF, along with increased levels of blood inflammatory factors, such as C-reactive protein (CRP) and interleukin-6 (IL-6). Additionally, the expression of renal TGF-β, NF-κB, and collagen III increased, indicating that renal inflammatory response and fibrosis were activated. This, in turn, upregulated the renal sodium transporter proteins and AQPs channels, ultimately impacting renal water and sodium metabolism. Prior research on the effects of acute atrial fibrillation on the kidney from the perspective of inflammation and fibrosis is limited, and this study sheds light on the subject.
Atrial fibrillation (AF), referred to as the “cardiovascular epidemic of the twenty-first century,” arises from various factors, including structural remodeling. This process involves vacuolar degeneration of atrial myocytes, breakage and loss of myogenic fibers, mitochondrial cristae swelling, amyloid degeneration, and atrial interstitial fibrosis caused by collagen aggregation (including degenerated myocardial parenchyma replaced by fibrous tissue). Excessive interstitial fibrosis separates myocardial bundles from each other, and fibroblasts and myofibroblasts couple with cardiomyocytes to interfere with the onset, continuity, and conductivity of electrical signals in cardiomyocytes, thus affecting the self-repair of atrial myocardium. The interaction between disordered atrial fibrillation and structural remodeling plays a crucial role in the development and maintenance of AF5,6.
Numerous studies have shown that inflammation stimulates atrial stromal fibrosis7. Inflammation not only induces differentiation of atrial fibroblasts into myofibroblasts but also stimulates myofibroblasts to secrete large amounts of collagen. Inflammatory cytokines induce cellular autophagy and apoptosis, which are accompanied by fibroblast recruitment and extracellular matrix deposition. This eventually results in an imbalance in extracellular matrix regulation, manifested by atrial tissue remodeling and stromal fibrosis8. This indicates that inflammation and fibrosis can undergo crosstalk and cascade amplification. Therefore, inflammatory animal models have been used to study atrial fibrillation, among which talc-induced atrial fibrillation due to aseptic pericarditis in mice is relatively stable9. CG has also been shown to be effective in inducing atrial fibrillation in mice by Yunming Jin et al.10. Compared with talc, CG has non-toxic, non-irritating, and stronger antibacterial characteristics, and is often used as a typical inducer of fibrosis models in basic animal experiments. The CG-induced atrial fibrillation model in mice is relatively easy and convenient to operate and has a high success rate; therefore, we chose this model for our study. We found that the expression of TGF-β and type III collagen was significantly increased in the atrial fibrillation group after CG induction. The atrial fibrillation waveform was induced quickly and maintained for a long time, indicating that structural remodeling of the atria had occurred in this atrial fibrillation mice model.
The biological function of TGF-β in promoting inflammation and tissue repair is well-established, with numerous studies confirming its involvement in the structural remodeling of atrial fibrillation11. During atrial fibrillation, myofibroblasts and cardiomyocytes release matrix metalloproteinases (MMPs), which degrade the extracellular matrix (ECM) and release cryptic epitopes and TGF-β from the ECM12. These cryptic epitopes can interact with cells such as endothelial cells and white blood cells. TGF-β activates the TGF-β/Smads signaling pathway, increasing extracellular matrix accumulation, promoting atrial fibrosis, and ultimately inducing atrial fibrillation13.
One characteristic of the inflammatory response is the rise in plasma pro-inflammatory factors. Several studies have established a significant correlation between the occurrence or prognosis of AF and multiple inflammatory markers, such as CRP and IL-614–16. CRP, produced by hepatic cells, is a systemic marker highly susceptible to inflammation. Qiu et al. discovered that salvianolate considerably decreased left atrial enlargement in mice that underwent ligation of the anterior descending branch, lowered serum IL-6 and CRP levels by impeding TGF-β/SMAD-mediated collagen deposition, and thus reduced the susceptibility to atrial fibrillation14. This finding confirmed the relationship between CPR, IL-6, and AF in animal experiments. In a subsequent study on 202 AF patients, it was discovered that individuals with higher CRP levels had a higher recurrence rate of AF, implying that CRP was closely linked to the treatment effectiveness of AF17. IL-6, a cytokine generated by various cells stimulated by viral inflammation, possesses numerous biological activities in addition to mediating inflammation, such as stimulating fibrinogenic synthesis. Chen’s team found that IL-6 levels were significantly higher in AF patients than in the normal group, and that IL-10 levels were significantly lower. Additionally, IL-6 was discovered to promote the expression of Mir-210 by regulating hypoxia-inducible factor 1α (HIF-1α) in cultured mouse cardiomyocytes. Mir-210 inhibited T cells by targeting Foxp3, promoting atrial myocyte fibrosis18. In this study, it was discovered that the expression of atrial type iii collagen and TGF-β increased in the atrial fibrillation group in the CG-induced mouse atrial fibrillation model, while the levels of plasma CRP and IL-6 increased, consistent with the aforementioned findings, indicating that atrial fibrillation, inflammation, and myocardial fibrosis are closely connected. At the same time, the study confirmed that the inflammatory response initiated in the acute episode of atrial fibrillation in mice is systemic.
Increased pro-inflammatory mediators and inflammatory factors can affect renal function. In this study, the expression of NF-κB and TGF-β proteins in the kidneys of mice with atrial fibrillation also increased with an increase in plasma inflammatory factors. The study found that the NF-kappa B as a nuclear transcription factor can be mediated the classic inflammatory signaling pathways, lead to TGF beta activation, the activation and the activation of kidney, glomerular capillary endothelial cells, basement membrane of kidney damage caused directly or indirectly, to increase the accumulation of extracellular matrix and protein loss, promote kidney fibroblast proliferation and fibrosis. Impaired kidney function19–23. In this study, the expression of type I collagen was significantly up-regulated in the atrial fibrillation group. As the main component of the extracellular matrix, over-expression of collagen type indicates the appearance of fibrosis24. At the same time, it was found that the renal drainage and sodium discharge ability of mice in the AF group decreased, indicating that the renal function of mice in the AF group was affected. Elevated concentrations of AQP2, ENaC-β, ENaC-γ and NKCC were also found. ENaC, namely amiloride-sensitive epithelial sodium channel, is composed of α, β and γ subunits and is expressed in the intracellular vesicles and apical membranes of the main cells of the connective and collecting ducts of the kidney, which is a rate-limiting step of sodium reabsorption and plays a crucial role in controlling systemic sodium balance25. Functional ENaC relies primarily on hydrolysis of the γ subunits and phosphorylation of the β subunits for transfer of the endoplasmic reticle to the cell membrane26,27. Serum glucocorticoid kinase 1 (SGK1), as a widespread in vivo regulator, plays a major role in the inflammation and metabolism of sodium hydrates. In this study, we found that SGK1, as a direct upstream regulator of ENaC, reflects the increased inflammatory response and sodium hydrate metabolism disorder in mice with acute onset of atrial fibrillation. NKCC, a furosemide-sensitive sodium–potassium-chloride cotransporter, is expressed in epithelial cells of the coarse segment of the romance of the medullary loop and in dense spots, and is mainly involved in saline reabsorption and tubular feedback. NKCC is mainly regulated by AVP system and Natriuretic peptide system28. AQP2 and AQP3 are located in apical membrane and the basement membrane of renal tubules, respectively, and their expression level is increased, indicating that renal tubules have increased water absorption29. Therefore, it can be inferred that the inflammatory response and fibrosis activation of the kidney up-regulate the expressions of NKCC, ENaC and water channels in the renal epithelium, thus affecting the hydration and sodium metabolism of the kidney.
Several studies have reported that stimulating the production and secretion of atrial Natriuretic peptide (ANP) after the onset of atrial fibrillation can play a diuretic role due to atrial dilatation and traction, as well as sympathetic nerve excitation. However, other studies have suggested that the increase in ANP may not result in a corresponding diuretic effect when the expression of Natriuretic peptide receptor is decreased or desensitized, which can diminish the diuretic effect of ANP30. Additionally, Cao et al. observed that as atrial fibrillation progressed, the ANC level initially increased and then decreased31. Further research is needed to confirm whether changes in the Natriuretic peptide system occur during the acute atrial fibrillation episode.
Of course, heart failure, the most frequent complication of atrial fibrillation, is also a critical factor that affects kidney function32. Atrial fibrosis, as a pathological structural basis of atrial fibrillation, represents a potential association between atrial fibrillation and heart failure33. Atrial fibrosis can result from decreased left ventricular myocardial compliance, left ventricular diastolic dysfunction, increased left ventricular filling pressure, and chronic inflammatory response to heart failure34,35. Left ventricular diastolic dysfunction can also occur as a result of rapid ventricular response due to atrial fibrillation. Pathological changes such as hemodynamics, neurohormones, and inflammation after the onset of heart failure can cause renal failure in approximately 40–$50\%$ of patients with heart failure36. In this study, the disorder of renal water and sodium metabolism caused by acute atrial fibrillation may be the initiating factor of heart failure, which predates its occurrence. The acute onset of atrial fibrillation triggers a systemic inflammatory response, leading to an increase in systemic blood volume, while promoting kidney inflammation and fibrosis, resulting in water and sodium metabolism disorders. This mechanism can lead to a significant increase in water and sodium retention, which increases the heart's preload and accelerates the development of heart failure. Additionally, the acute onset of atrial fibrillation may lead to the activation of neurohormones, the activation of the renal renin-angiotensin system, and result in disorders in water and sodium metabolism.
The specific mechanism requires further verification through experiments. Juan et al.32 reported a fivefold increase in the risk of renal deterioration in patients with atrial fibrillation after developing heart failure. Studies have demonstrated that glucocorticoids can produce a potent diuretic effect and improve cardiac function by inhibiting the renin–angiotensin–aldosterone system and arginine vasopressin pathway, as well as activating the natriuretic peptide system in mice with heart failure37. It remains to be determined whether glucocorticoids can improve inflammation and fibrosis in mice with atrial fibrillation by the aforementioned mechanisms, thereby improving renal function.
This study examined the mechanism of renal damage caused by atrial fibrillation for the first time, but it has several limitations. Firstly, the study only used male C57 mice, which may not reflect sex-specific differences in toxicity or metabolism. Secondly, the study was solely conducted in vivo and did not include in vitro experiments or investigate signal transduction pathways. Additionally, the study only examined the effects of acute atrial fibrillation on renal water and sodium metabolism, and did not explore the effects of persistent atrial fibrillation on the kidneys. The experimental conclusion requires confirmation from clinical research. Finally, the changes in the natriuretic peptide system, which play a diuretic role in atrial fibrillation, require further study for confirmation. Finally, Sp1 has been reported upregulated in glomerulonephritis and is an important transcriptional mediator of TGF-β signaling. And Sp1 was a hub gene of atrial fibrillation38. And G protein Signaling (RGS)-4 (RGS4) was found that may protect not only against calcium signaling-induced tachyarrhythmias and AF, but also against cholinergic-induced bradycardia. It was also reported to be potentially elevated in AF in the atrial myocytes of their AF mice recently39. In addition, osteopontin (OPN) is a ubiquitous pro-inflammatory cytokine, which is also known to mediate cardiac TGF-β pro-fibrotic and pro-inflammatory signaling that underlies AF40. Meanwhile, SGLT2, known to be involved in renal fibrosis and various nephropathies, has been reported to be induced by TGF-β in renal cells41. The expression of Sp1, RGS4 and OPN in atrium as well as the expression of OPN and SGLT2 expression in kidney, especially OPN in the medulla and cortex of kidney in mice in acute atrial fibrillation in mice is worth to study further. Future studies are warranted to test.
In conclusion, our study found that the acute onset of atrial fibrillation (AF) triggers an atrial inflammatory response, which in turn activates renal inflammatory response and fibrosis. This, in turn, leads to the dysfunction of renal water and sodium metabolism, which is related to the up-regulation of the expressions of renal NKCC, epithelial sodium channel, and AQPs channel. The findings of this study provide a novel direction for the prevention and treatment of heart failure on a global scale.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30783-w.
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|
---
title: Setd2 inactivation sensitizes lung adenocarcinoma to inhibitors of oxidative
respiration and mTORC1 signaling
authors:
- David M. Walter
- Amy C. Gladstein
- Katherine R. Doerig
- Ramakrishnan Natesan
- Saravana G. Baskaran
- A. Andrea Gudiel
- Keren M. Adler
- Jonuelle O. Acosta
- Douglas C. Wallace
- Irfan A. Asangani
- David M. Feldser
journal: Communications Biology
year: 2023
pmcid: PMC10006211
doi: 10.1038/s42003-023-04618-3
license: CC BY 4.0
---
# Setd2 inactivation sensitizes lung adenocarcinoma to inhibitors of oxidative respiration and mTORC1 signaling
## Abstract
SETD2 is a tumor suppressor that is frequently inactivated in several cancer types. The mechanisms through which SETD2 inactivation promotes cancer are unclear, and whether targetable vulnerabilities exist in these tumors is unknown. Here we identify heightened mTORC1-associated gene expression programs and functionally higher levels of oxidative metabolism and protein synthesis as prominent consequences of Setd2 inactivation in KRAS-driven mouse models of lung adenocarcinoma. Blocking oxidative respiration and mTORC1 signaling abrogates the high rates of tumor cell proliferation and tumor growth specifically in SETD2-deficient tumors. Our data nominate SETD2 deficiency as a functional marker of sensitivity to clinically actionable therapeutics targeting oxidative respiration and mTORC1 signaling.
SETD2 inactivation leads to heightened mTORC1 signaling, oxidative metabolism and protein synthesis in KRAS-driven mouse models of lung adenocarcinoma, contributing to the understanding of how SETD2 deficiency drives early and widespread tumor growth.
## Introduction
Inactivation of SETD2 (SET domain containing 2, histone lysine methyltransferase) is a prevalent feature of many cancer types including ~$7\%$ of lung adenocarcinomas1–3. SETD2 has the unique catalytic activity for histone H3 lysine 36 trimethylation (H3K36me3) which it deposits along gene bodies during transcriptional elongation4. However, additional non-histone substrates have been identified which link SETD2 action with diverse roles in chromosome segregation (α-tubulin), interferon signaling (STAT1), or regulation of other chromatin modifying enzymes (EZH2)5–7. Loss of H3K36me3 due to SETD2 inactivation is implicated in the impairment of DNA repair, accurate splice site usage, transcriptional control, and DNA and RNA methylation8–12. These numerous characterized functions obscure how SETD2 mediates lung tumor suppression and whether inactivation of SETD2 presents therapeutic vulnerabilities that can be targeted to limit cancer growth.
To study Setd2 inactivation in vivo, we previously used the well-characterized, Cre-inducible KrasLSL-G12D/+ (K) and KrasLSL-G12D/+; p53flox/flox (KP) mouse models of lung adenocarcinoma. Combined with a lentiviral vector that expresses Cre recombinase, along with the essential CRISPR components, Cas9 and an sgRNA to Setd2, we and others have shown that inactivation of Setd2 in KrasG12D-driven lung adenocarcinoma promotes cellular proliferation very early after tumor initiation; an effect that is widespread amongst all developing tumors13,14. Moreover, Setd2 quantitatively ranks at the apex of all major tumor suppressors for its ability to suppress KRAS-driven cell proliferation13,15. However, unlike other tumor suppressors such as Trp53 or Rb1, whose inactivation promotes cell state changes that drive malignant progression, loss of differentiation, and metastasis, inactivation of Setd2 seems only to fuel cellular proliferation in these models14,16,17. As such, we sought to interrogate the consequences of Setd2 inactivation in KRAS-driven lung adenocarcinoma in order to better understand how SETD2 deficiency drives early and widespread tumor growth and identify intrinsic therapeutic vulnerabilities that may exist.
## SETD2 deficiency promotes OXPHOS and protein synthesis gene expression programs
To gain mechanistic insights into how SETD2 constrains cancer growth, we performed a comprehensive analysis of RNA-sequencing data across multiple human cancer types expressing low or high levels of SETD22. Using Gene Set Enrichment Analysis (GSEA), we identified an enrichment of ribosomal- and mitochondrial-associated gene sets that negatively correlated with SETD2 expression. This association was strongly apparent in multiple tumor types that have a high frequency of SETD2 mutations, in addition to lung adenocarcinoma (Fig. 1A and Supplementary Fig. 1A–D)18. The negative correlation of ribosomal- and mitochondrial-associated gene sets was unique to SETD2 expression, as these genes sets were not associated with expression levels of other major tumor suppressors in lung adenocarcinoma such as TP53 or RB1, which instead correlated with DNA replication-associated gene sets (Supplementary Fig. 1E–G). These results suggest that ribosomal- and mitochondrial-biosynthetic pathways are activated in SETD2-deficient tumors and may be responsible for driving tumor cell proliferation. To extend this analysis, we analyzed gene expression data from KrasLSL-G12D/+ (K) tumors initiated with a non-targeting control lentiviral CRISPR vector (K-Ctrl) or an sgRNA targeting Setd2 (K-Setd2KO), as well as more advanced tumors isolated from KrasLSL-G12D/+; p53flox/flox;YFPflox/flox (KPY-Ctrl or KPY-Setd2KO) mice. In each case (K and KPY), control tumors and Setd2KO tumors were assessed via histological analysis to select stage matched specimens for comparison. K-Setd2KO tumors, which were low grade adenomas, and KPY-Setd2KO tumors, which were higher grade adenocarcinomas, had vastly different gene expression profiles than their K-Ctrl and KPY-Ctrl counterparts and a strong enrichment of ribosomal- and mitochondrial-associated gene sets. ( Fig. 1B, C, Supplementary Fig. 1H, I). Collectively these results suggest that ribosomal- and mitochondrial-biosynthetic pathways are activated in SETD2-deficient tumors and may be responsible for driving increased tumor cell proliferation. Fig. 1SETD2 deficiency promotes OXPHOS and protein synthesis gene expression programs in human and murine lung adenocarcinomas.a Gene set enrichment analysis (GSEA) network plot of the 20 most enriched gene sets negatively-correlated with SETD2 expression in human lung adenocarcinomas. The size of each node corresponds to the normalized enrichment score (NES) and the width of the connecting lines indicates the number of overlapping genes between gene sets. Gene sets are categorized as mitochondrial, ribosomal or other according to the genes represented. b GSEA network plot of the top 20 enriched gene sets in KPY-Setd2KO lung adenocarcinomas. The size of each node corresponds to the normalized enrichment score (NES) and the width of the connecting lines indicates the number of overlapping genes between gene sets. Gene sets are categorized as mitochondrial, ribosomal or other according to the genes represented. c Representative mitochondrial (GO Electron Transport Chain) and ribosomal (GO Cytoplasmic Translation) GO biological process ontology gene sets that are enriched in K-Setd2KO (left) and KPY-Setd2KO (right) tumors. d Depiction of mitochondrial electron transport chain genes that show enrichment in the molecular signatures database (mSigDB) Mootha VOXPHOS gene set in K-Setd2KO tumors53. Genes are grouped according to the relevant ETC complex. Genes that are part of the core enrichment of the gene set are marked in gray, genes that are not enriched are marked in white, and mitochondrial encoded genes are outlined in red. e Depiction of ribosomal 40 S and 60 S genes that show enrichment in the mSigDB GO Ribosome *Biogenesis* gene set in K-Setd2KO tumors. Genes that are enriched in this gene set are marked in gray while genes that are not enriched are marked in white.
## SETD2-deficient tumors have altered mitochondrial morphology and function
The major driver of enrichment for mitochondrial-associated gene sets in SETD2-deficient tumors was the significant up-regulation of genes encoding mitochondrial electron transport chain (ETC) proteins in complexes I, III, IV and V (Fig. 1D). To determine whether physical changes occur in the mitochondria when Setd2 is inactivated, we imaged KP-Ctrl and KP-Setd2KO tumors by transmission electron microscopy. Although the overall frequency of mitochondria observed per field of view was similar between genotypes (Supplementary Fig. 2A, B), the mitochondria in KP-Setd2KO tumors were significantly different than those found in KP-Ctrl tumors by several parameters. Grossly, the mitochondria of KP-Setd2KO tumors were significantly smaller and had a more electron dense matrix than controls (Fig. 2A–C Supplementary Fig. 2A). Morphologically, KP-Setd2KO mitochondria also had significantly more cristae overall, and on average each cristae was significantly more swollen (Fig. 2A, D, E)18,19. While some of these morphological changes can be associated with mitochondrial dysfunction and cell death, the higher electron density in the mitochondria matrix and a greater overall number of cristae in the mitochondria of KP-Setd2KO tumors may suggest greater oxidative function20.Fig. 2SETD2-deficient tumors contain mitochondria with distinct morphological features and increased electron transport chain activity.a Transmission electron micrograph of mitochondria from a KP-Ctrl tumor (left) or KP-Setd2KO tumor (right). White arrows denote swollen mitochondrial cristae. Scale bars = 250 nm. b Quantification of the area of individual mitochondria measured from electron micrographs. Data indicate the mean ± standard deviation. Data points represent individual mitochondria (KP-Ctrl: $$n = 42$$ mitochondria, $$n = 3$$ mice, KP-Setd2KO: $$n = 54$$ mitochondria, $$n = 3$$ mice). Significance determined by unpaired Student’s t-test. c Quantification of the matrix electron density of individual mitochondria measured from electron micrographs. Data indicate the mean ± standard deviation. Data points represent individual mitochondria (KP-Ctrl: $$n = 16$$ mitochondria, $$n = 2$$ mice, KP-Setd2KO: $$n = 28$$ mitochondria, $$n = 1$$ mice). Significance determined by unpaired Student’s t-test. d Quantification of the number of cristae per mitochondria normalized to mitochondrial area. Data indicate the mean ± standard deviation. Data points represent individual mitochondria (KP-Ctrl: $$n = 37$$ mitochondria, $$n = 3$$ mice, KP-Setd2KO: $$n = 34$$ mitochondria, $$n = 3$$ mice). Significance determined by unpaired Student’s t-test. e Quantification of the width of individual mitochondrial cristae measured from electron micrographs. Data indicate the mean ± standard deviation. Data points represent individual cristae (KP-Ctrl: $$n = 179$$ cristae, $$n = 42$$ mitochondria, $$n = 3$$ mice, KP-Setd2KO: $$n = 177$$ cristae, $$n = 50$$ mitochondria, $$n = 3$$ mice). Significance determined by unpaired Student’s t-test. f Quantification of the total mitochondrial mass by median fluorescence intensity of MitoTracker Deep Red FM within tumor cells isolated from KPY mice. Data represent the mean ± standard deviation. Data points represent individual tumors (KPY-Ctrl: $$n = 9$$ tumors, $$n = 2$$ mice, KPY-Setd2KO: $$n = 6$$ tumors, $$n = 2$$ mice). Significance determined by unpaired Student’s t-test. Histogram shows representative flow data from KPY-Ctrl and KPY-Setd2KO tumors with unstained control. g Quantification of the mitochondrial membrane potential by median fluorescence intensity of MitoProbe DiIC1[5] within tumor cells isolated from KPY mice. Data is normalized to the mitochondrial mass of each sample. Data indicate the mean ± standard deviation. Data points represent individual tumors (KPY-Ctrl: $$n = 13$$ tumors, $$n = 2$$ mice, KPY-Setd2KO: $$n = 11$$ tumors, $$n = 2$$ mice). Significance determined by unpaired Student’s t-test. Histogram shows representative flow data from KPY-Ctrl and KPY-Setd2KO tumors with unstained control. h Seahorse XF cell mitochondrial stress test assay performed in H2009 shSetd2 and shCtrl cells. Relative oxygen consumption rate was normalized to total protein abundance. Each symbol in OCR profile plots represents the mean of at least $$n = 6$$ technical replicates of three reading cycles. OCR profile plots for (i) basal respiration (j) ATP production, and (k) maximal respiration. Each symbol represents one technical replicate per cell line. Data indicate the mean ± standard deviation. Significance determined by unpaired Student’s t-test.
To investigate mitochondrial properties in neoplastic cells directly ex vivo, we crossed a Rosa26LSL-YFP Cre-reporter allele into the KP model, generating KPY mice (Supplementary Fig. 2C)21. Consistent with the EM data, KPY-Setd2KO tumor cells had decreased staining for MitoTracker Deep Red indicative of decreased mitochondrial mass (Fig. 2F). Though KPY-Setd2KO tumor cells did not differ from KPY-Ctrl tumor cells with respect to mitochondrial superoxide production, the mitochondrial oxidative potential and the total ETC activity was significantly higher in KPY-Setd2KO tumor cells (Fig. 2G, Supplementary Fig. 2D, E).
To determine if the observed mitochondrial changes in SETD2-deficient tumors result in increased mitochondrial function and consequential ATP production, we generated cell lines expressing two distinct shRNAs targeting Setd2 in the H2009 human lung adenocarcinoma cell line that harbors oncogenic KRAS and p53 mutations22. As expected, SETD2 shRNA-expressing cell lines had decreased SETD2 and H3K36me3 compared to control cell lines, confirming a knockdown of SETD2 (Supplementary Fig. 3A–D). To profile mitochondrial function we performed a cell mitochondrial stress test assay (Seahorse XF) on these cell lines. In agreement with our in vivo data, SETD2-deficient human cell lines displayed increased oxygen consumption rates and ATP production (Fig. 2H–K). These data demonstrate that SETD2-deficiency promotes increased mitochondrial metabolism and are consistent with the gene expression programs that are enriched in human and mouse tumors with low SETD2 expression.
## mTORC1 signaling and protein synthesis are heightened in SETD2-deficient tumors
An additional feature of SETD2-deficient tumors was the significant enrichment of genes associated with protein synthesis and ribosome biogenesis (Fig. 1B, C, E)23. To assess the impact of SETD2 deficiency on protein synthesis, we pulse-labeled K-Ctrl and K-Setd2KO mice with the tRNA mimetic O-propargyl-puromycin (OP-Puro). OP-*Puro is* incorporated into actively translated proteins to allow for quantification of protein synthesis by flow cytometry or fluorescent microscopy and offers a method to quantify the rate of protein synthesis that is compatible with small sample sizes24–26. Combined staining for H3K36me3 and OP-Puro incorporation demonstrated a marked enhancement of overall protein synthesis in K-Setd2KO tumors (Fig. 3A, B). The incorporation of the Rosa26LSL-YFP allele in our KPY model afforded an orthogonal approach to study differences in protein synthesis rates by measuring the brightness of cells expressing the YFP reporter protein. While KPY-Setd2KO tumor cells were only slightly larger ($3.8\%$ higher FSC-A) and mRNA expression from the Rosa26LSL-YFP allele was similar to KPY-Ctrl tumor cells, KPY-Setd2KO tumor cells had an $18.5\%$ increase in mean YFP fluorescence. This indicates a positive effect on YFP protein synthesis without affecting YFP mRNA production. ( Supplementary Fig. 4A, B). These data suggest that SETD2 normally constrains the rate of protein synthesis in addition to the degree of OXPHOS. These functions, which would be expected to limit cellular proliferation, are consistent with the proliferation-driving effects of Setd2 inactivation in KRAS-driven lung cancer. Fig. 3SETD2-deficient tumors have increased protein synthesis and high mTORC1 signaling.a Representative images of o-propargyl-puromycin (OP-Puro) incorporation (red), H3K36me3 (green), and DAPI-stained nuclei (white) in K-Ctrl (left) and K-Setd2KO(right) tumors. Scale bars = 100 μm, insets are magnified 5x. b Quantification of OP-Puro mean fluorescence intensity in K-Ctrl and K-Setd2KO tumors. Data represent the mean ± standard deviation. Data points represent individual tumors (K-Ctrl: $$n = 18$$ tumors, $$n = 3$$ mice, K-Setd2KO: $$n = 17$$ tumors, $$n = 3$$ mice). Significance determined by unpaired Student’s t-test. c Representative images of phosphorylated 4E-BP1(T$\frac{37}{46}$) staining (red) in K-Ctrl (left) and K-Setd2KO(right) tumors. Nuclei are counterstained with DAPI (white). Scale bars = 100 μm, insets are magnified 5x. d Quantification of p-4E-BP1(T$\frac{37}{46}$) mean fluorescence intensity in K-Ctrl and K-Setd2KO tumors. Data represent the mean ± standard deviation. Data points represent individual tumors (K-Ctrl: $$n = 24$$ tumors, $$n = 3$$ mice, K-Setd2KO: $$n = 29$$ tumors, $$n = 3$$ mice). Significance determined by unpaired Student’s t-test. e Representative images of co-immunofluorescence of mTOR (green) and LAMP2 (red) indicating localization of mTOR at the lysosome (yellow) in K-Ctrl (left) and K-Setd2KO(right) tumors. Nuclei are counterstained with DAPI (blue). Scale bars = 10 μm, inset is magnified 2X. f Quantification of the percentage of colocalization between mTOR and LAMP2 in K- Ctrl and K-Setd2KO tumors. Data points represent individual tumors (K-Ctrl: $$n = 8$$ tumors, $$n = 3$$ mice, K-Setd2KO: $$n = 9$$ tumors, $$n = 3$$ mice). Significance determined by unpaired Student’s t-test.
Tightly linked with both protein synthesis and OXPHOS is the master nutrient sensing complex mTORC127–29. To determine whether SETD2 deficiency is associated with increased mTORC1 activity, we first evaluated human lung adenocarcinomas using reverse phase protein array (RPPA) data from the Cancer Proteome Atlas30,31. An established marker of mTORC1 activity is the sequential phosphorylation of the translational repressor 4E-BP1, first at Thr37/Thr46 to prime subsequent phosphorylation at Thr70 and Thr6532,33. SETD2-deficient human tumors had significantly increased phosphorylation of 4E-BP1 at Thr70, while total 4E-BP1 levels were unchanged (Supplementary Fig. 4C). Additionally, there was a significant negative correlation between SETD2 mRNA expression and phosphorylated 4E-BP1(T70) (Supplementary Fig. 4D). By profiling human lung adenocarcinoma genomic data we also found that tumors with low SETD2 expression were significantly enriched for an mTORC1 signaling-related gene set (Supplementary Fig. 4E)2. Consistent with these analyses of human datasets, we identified significantly increased mTORC1-dependent 4E-BP1(T$\frac{37}{46}$) phosphorylation in K-Setd2KO tumors compared to K-Ctrl tumors (Fig. 3C, D)34. Further, SETD2-deficient tumors had significantly higher levels of mTORC1 localized at the lysosome where it is known to actively signal, further indicating that SETD2 deficiency promotes mTORC1 activity (Fig. 3E, F).
## Therapeutic targeting of OXPHOS and mTORC1 counteracts SETD2-deficient tumor growth
Our discovery that mTORC1 signaling, protein synthesis, and mitochondrial OXPHOS are increased in SETD2-deficient tumors suggested that these processes may drive cell proliferation and thus offer therapeutic susceptibilities for SETD2-mutant cancers. To assess this possibility, we treated mice bearing established K-Ctrl and K-Setd2KO tumors daily for 4 weeks with either the mTORC1 inhibitor rapamycin, the mitochondrial complex I inhibitor IACS-10759, or the anti-diabetic biguanide phenformin which inhibits both complexes (Fig. 4A)35–37. All three therapeutics had little effect on K-Ctrl tumors. However, each treatment significantly suppressed the increased tumor growth of K-Setd2KO tumors (Fig. 4B, C). SETD2-deficient tumors were particularly sensitive to rapamycin treatment, which had a similar suppressive effect on tumor growth as phenformin which inhibits both mitochondrial complex I and mTORC1 signaling (Fig. 4B, C). Inhibition of mTORC1 and mitochondrial complex I activity resulted in significantly reduced cell proliferation, marked by decreased phospho-H3 presence, demonstrating that the proliferative impact of SETD2 inactivation is driven, at least in part, through these pathways (Fig. 4D, Supplementary Fig. 5A). Further, inhibition of mTORC1 and mitochondrial complex I, alone or in combination, did not result in significant levels of cell death indicating that cell death is not the cause of the decreased tumor growth observed (Supplementary Fig. 5B). Phenformin is a highly potent inhibitor of both mitochondrial complex I and mTORC1. This potency has led to significant toxicity and its clinical replacement for the treatment of type II diabetes with the related biguanide metformin, which is used widely and is epidemiologically associated with suppressing cancer incidence38–40. Therefore, we treated K-Ctrl and K-Setd2KO mice bearing established tumors with metformin for an extended period of 12 weeks, mimicking long-term metformin treatment. While metformin treatment had no impact on K-Ctrl tumor growth, metformin-treated K-Setd2KO tumors were significantly smaller and less proliferative than vehicle treated K-Setd2KO tumors (Fig. 4E–G).Fig. 4mTORC1 signaling and mitochondrial OXPHOS are required for the growth- promoting effects caused by SETD2-deficiency.a Schematic of experiment whereby K-Ctrl or Setd2KO tumors were initiated using LentiCRISPRv2Cre. 16 weeks after tumor initiation (black line) mice were randomly assigned to vehicle or drug treatment regimen for 4 weeks (red line), at which point mice were sacrificed for assessment. For metformin treatment, mice were given the drug for 12 weeks prior to sacrifice. b Quantification of mean tumor areas in vehicle-, rapamycin-, IACS-10759-, or phenformin-treated K-Ctrl or K-Setd2KO mice. Individual tumor sizes were normalized to the mean area of vehicle-treated control mice. Box and whisker plots indicate the median, lower quartile, upper quartile, maximum and minimum data points. Data points represent individual tumors (K-Ctrl: $$n = 109$$ vehicle, $$n = 216$$ rapamycin, $$n = 155$$ IACS-10759, $$n = 144$$ phenformin; K-Setd2KO: $$n = 503$$ vehicle, $$n = 142$$ rapamycin, $$n = 99$$ IACS-10759, $$n = 138$$ phenformin). Significance determined by unpaired Student’s t-test. c Representative scans of tumor-bearing lobes from K-Ctrl or K-Setd2KO mice treated with vehicle, rapamycin, IACS-10759 or phenformin. d Quantification of cell proliferation by the percentage of p-H3 positive cells in K-Ctrl or K-Setd2KO tumors treated with vehicle, rapamycin, IACS-10759 or phenformin. Data indicate the mean ± standard deviation. Data points represent individual tumors (K-Ctrl: $$n = 14$$ vehicle, $$n = 14$$ rapamycin, $$n = 13$$ IACS-10759, $$n = 13$$ phenformin; K-Setd2KO: $$n = 20$$ vehicle, $$n = 14$$ rapamycin, $$n = 14$$ IACS-10759, $$n = 14$$ phenformin). Significance determined by unpaired Student’s t-test. e Quantification of mean tumor areas in vehicle- and metformin-treated K-Ctrl or K- Setd2KO mice. Individual tumor sizes normalized to the mean area of vehicle-treated control mice. Data represent the mean ± standard deviation. Data points represent individual tumors (K-Ctrl: $$n = 115$$ vehicle, $$n = 236$$ metformin; K-Setd2KO: $$n = 193$$ vehicle, $$n = 503$$ metformin). Significance determined by unpaired Student’s t-test. f Representative scans of tumor-bearing lobes from K-Ctrl or K-Setd2KO mice treated with vehicle or metformin. g Quantification of cell proliferation by the percentage of p-H3 positive cells in K-Ctrl and K-Setd2KO mice given vehicle treatment or metformin. Data indicate the mean ± standard deviation. Data points represent individual patient tumors (K-Ctrl: $$n = 13$$ Vehicle, $$n = 14$$ Metformin; K-Setd2KO: $$n = 14$$ Vehicle, $$n = 13$$ Metformin). Significance determined by unpaired Student’s t-test. Note: No survival experiments were conducted and efficacy of drug treatments is solely based on the differences in tumor size shown here.
## Discussion
Here, we identify a conserved enhancement of gene expression programs and functional markers associated with oxidative metabolism and protein synthesis in SETD2-deficient cancers. The tumor suppressive function of the chromatin modifier SETD2, at least in the context of KRAS-driven lung adenocarcinoma, is therefore to limit pro-proliferative metabolic pathways by restricting oxidative metabolism and protein synthesis. As such, our data bolster the expanding realization that the metabolic state of a cell is intimately linked to the repertoire of post translational modifications on histones41–43.
Our work uncovers a role for SETD2 in constraining mitochondrial OXPHOS and mTORC1 signaling to limit cellular proliferation in the context of KRAS-driven lung adenocarcinoma. It is notable that these roles are not entirely dissimilar to those regulated by the LKB1 tumor suppressor, a well-known regulator of nutrient-sensing mechanisms that impinge on mTORC1 signaling44. Further, silencing or inactivation of Lkb1 also sensitizes lymphoma and lung adenocarcinoma to phenformin35,45. The parallels between the consequences of SETD2 and LKB1 inactivation are all the more provocative given an apparent functional-genetic epistatic relationship in KRAS-driven mouse models and their mutually exclusive pattern of mutation in human of lung adenocarcinoma patients2,15. Determining whether and how LKB1 and SETD2 fit into a singular pathway predicted by these overlapping phenotypes and their genetic epistasis is an intriguing possibility that has yet to be determined.
Finally, we demonstrate not only that signaling through OXPHOS and mTORC1 is required for the proliferative benefit bestowed upon tumor cells following SETD2 inactivation, but that they also represent readily actionable therapeutic vulnerabilities for patients with SETD2-deficient tumors. Therefore, our study nominates OXPHOS and mTORC1 inhibition as a targeted therapy for SETD2-deficient lung adenocarcinoma.
## Animal studies and treatment
Animal studies were performed under strict compliance with Institutional Animal Care and Use Committee at University of Pennsylvania [804774]. KrasLSL-G12D mice (Jax stock number 008179), Trp53flox/flox mice (Jax stock number 008462), and Rosa26LSL-YFP/ LSL-YFP mice have previously been described21,46,47. Mice are mixed B6J/129S4vJae. Mice were transduced with 6×104 plaque forming units (PFUs) per mouse of LentiCRISPRv2Cre by endotracheal intubation48. LentiCRISPRv2Cre expressing sgRNAs targeting GFP or β-Galactosidase (BGal) were used as controls, while an sgRNA targeting Setd2 was used for knockouts. For control sgRNAs, sgGFP was used to induce K tumors for RNA-sequencing, while sgBGal was used for all other experiments. The sgRNA sequences are: sgGFP-GGGCGAGGAGCTGTTCACCG, sgBGal – CACGTAGATACGTCTGCATC, and sgSetd2-AATGGGCTGAGGTACGCCGT14,49. Lentivirus production and titration was performed as described previously14.
For drug experiments, OpenStandard Diet was formulated with Rapamycin (MedChem Express) at 15 mg kg−1 for a dose of 2 mg/kg/day or IACS-10759 (MedChem Express) at 37.5 mg kg−1 for a dose of 5 mg/kg/day by Research Diets. Mice were placed on treatment diet 4 weeks prior to analysis. For phenformin treatment, mice were given the drug by oral gavage daily on a 5 days on/2 days off schedule for 4 weeks. Mice were given 200 mg/ kg/day phenformin dissolved in water (Cayman Chemicals). For metformin treatment, metformin was placed in the drinking water of mice at 1.25 mg/ml for 12 weeks prior to sacrifice (Sigma-Aldrich). No statistical methods were used to predetermine sample sizes. The size of each animal cohort was determined by estimating biologically relevant effect sizes between control and treated groups and then using the minimum number of animals that could reveal statistical significance using the indicated tests of significance. All animal studies were randomized in ‘control’ or ‘treated’ groups, and roughly equal proportions of male and female animals were used. However, all animals housed within the same cage were generally placed within the same treatment group. For histopathological assessments of tumor size, researchers were blinded to sample identity and group. The animal protocol was approved by the University Laboratory Animal Resources (ULAR) at the University of Pennsylvania and the IACUC.
## Immunohistochemistry and immunofluorescence
Lung and tumor tissues were dissected into $10\%$ neutral-buffered formalin overnight at room temperature before dehydration in a graded alcohol series. Paraffin-embedded and H&E-stained histological sections were produced by the Penn Molecular Pathology and Imaging Core. Immunostaining for H3K36me3 (Abcam, ab9050, 1:1000), p-4E-BP1(T$\frac{37}{46}$) (Cell Signaling Technology, cs2855, 1:100), mTOR (Cell Signaling Technology, cs2983, 1:100), LAMP2 (Abcam, ab13524, 1:100) and p-H3 (Cell Signaling Technology, cs9701, 1:500) were performed after citrate-based antigen retrieval. H3K36me3 alone was assessed by immunohistochemistry using ABC reagent (Vector Laboratories, PK-4001) and ImmPACT DAB (Vector Laboratories, SK-4105) according to the product instructions. P-4E-BP1(T$\frac{37}{46}$) was assessed by immunofluorescence using a biotinylated secondary antibody (Vector Laboratories, PK-4001) according to product instructions, and Streptavidin-conjugated Alexa594 (Thermo Fisher S11227, 1:200). Colocalization of mTOR and LAMP2 was determined using an anti-Rat Alexa647 antibody (Thermo Fisher A21247, 1:200) to detect LAMP2, and a biotinylated anti-Rabbit secondary antibody (Vector Laboratories, PK-4001) followed by Streptavidin-conjugated Alexa488 (Thermo Fisher S32354, 1:200) to detect mTOR.
Immunohistochemistry and immunofluorescence were both performed on paraffin-embedded sections following the same antigen-retrieval protocol. Sections were incubated in primary antibody overnight at 4 °C, secondary antibody for 1 hour at room temperature, and for immunofluorescence Streptavidin-conjugated fluorophore for 1 hour at room temperature in the dark.
For TUNEL staining, tissues were deparaffinized and then permeabilized with $0.1\%$ sodium citrate and $0.1\%$ Triton-X in PBS for 8 minutes. FITC-conjugated TUNEL labeling mix (Millipore Sigma, 11684795910) was added to permeabilized tissue sections and incubated for 1 hour at 37 °C in the dark. For all immunofluorescence staining, nuclei were stained using 5 mg/ml DAPI at a 1:1000 dilution for 10 minutes, and then slides were mounted with Fluoro-Gel (EMS, 17985-50).
## O-Propargyl-Puromycin Analysis
For the quantification of protein translation, mice were injected intraperitoneally (IP) with 200 μl of a 10 mM solution of OP-Puro dissolved in PBS as previously described24. 1 hour after OP-Puro IP injection, mice were sacrificed and lungs were formalin fixed and paraffin embedded as described above. Co-immunofluorescence for H3K36me3 and OP-Puro was performed to quantify translation rates in tumors lacking SETD2 activity. Antigen retrieval was performed using a solution of 20 μg/ml proteinase K in TE Buffer (pH 8) at 37 °C for 10 minutes. A click chemistry reaction was then performed for 30 minutes at room temperature in the dark to conjugate Alexa594 to incorporated OP-Puro according to product instructions (Thermo Fisher, C10429, beginning at Step 5.1). Samples were kept in the dark for all further steps. Samples were treated with avidin and biotin blocking steps for 20 minutes each (Vector Laboratories, SP-2001), and a 30-minute protein block (Dako, X090930-2) before incubating with H3K36me3 primary antibody (Abcam, ab9050) at 1:300 overnight at 4 °C. H3K36me3 was then detected using a biotinylated secondary antibody for 1 hour (Vector Laboratories, PK-4001) followed by streptavidin-conjugated Alexa488 for 1 hour at a 1:200 dilution (Thermo Fisher, S-32354). Nuclei were stained using 5 mg/ml DAPI at a 1:1000 dilution for 10 minutes, and then slides were mounted with Fluoro-Gel (EMS, 17985-50).
## Histological quantification
The analysis of mTOR/LAMP2 colocalization was performed using a Leica TCS SP5 II confocal microscope. Z-stack projections of confocal images taken of control and SETD2-deficient tumors were analyzed. For the quantification of PLA individual loci were counted from z-stack projections. The LAS X colocalization tool was used for the quantification of mTOR/LAMP2 colocalization, All other photomicrographs were captured on a Leica DMI6000B inverted light and fluorescence microscope, and ImageJ software was used for subsequent histological quantifications50. For the quantification of staining intensity for OP-Puro and p-4EBP1(T$\frac{37}{46}$), single fluorescent channel images were obtained and the mean fluorescence intensity of staining for each tumor was quantified in ImageJ. Great care was made to ensure that background signal from blood vessels, or empty spaces were excluded from the analysis. For the quantification of tumor sizes under varying conditions including drug treatments, a tile scan of each mouse lung was obtained using the Leica DMI600B microscope and tumor area was measured in ImageJ. Tumor areas were then normalized to the mean area of a sgCtrl, vehicle treatment tumor. For the quantification of p-H3 staining, total nuclei in each tumor were counted using the IHC Profiler plugin for ImageJ, and p-H3-expressing nuclei were counted in ImageJ using the Cell Counter plugin51. For all histological analyses each data point represents an individual tumor.
## Flow Cytometry
Tumors were microdissected directly from the lungs of KrasLSL-G12D/+;Trp53flox/flox; Rosa26LSL-YFP/LSL-YFP (KPY) mice and individually placed in 500 μl of tumor digestion buffer consisting of PBS containing 10 mM HEPES pH 7.4, 150 mM NaCl, 5 mM KCl, 1 mM MgCl2, and 1.8 mM CaCl2, along with freshly added Collagenase 4 (Worthington 100 mg/ml solution, 20 μl per ml of digestion buffer) and DNase I (Roche 10 mg/ml solution, 4 μl per ml of digestion buffer). Tumors were manually disassociated using scissors, and then placed in a 4 °C shaker for 1 hour at 250 rpm. Digested tumors were then filtered into strainer-cap flow tubes (Corning, 352235) containing 1 ml of horse serum (Thermo Fisher, 16050122) to quench the digestion reaction. Cells were spun down at 200 g for 5 minutes with the cap in place to obtain all cells. The supernatant was aspirated, cells were washed once with PBS and then resuspended with a given mitochondrial dye to stain for 30 minutes at 37 °C. To quantify mitochondrial volume, cells were incubated with 50 nM MitoTracker Deep Red FM (Thermo Fisher, M22426), combined with 50 μM of CCCP (Thermo Fisher, M34151) to eliminate confounding effects of mitochondrial membrane potential differences. To quantify mitochondrial membrane potential cells were incubated with 20 nM MitoProbe DiIC1[5] (Thermo Fisher, M34151). To quantify mitochondrial oxidative potential cells were incubated with 100 nM MitoTracker Red CM-H2XRos (Thermo Fisher, M7513). To quantify mitochondrial ROS cells were incubated with 5 μM MitoSOX Red (Thermo Fisher, M36008). After 30 minutes of staining, cells were washed twice with PBS and then resuspended in 100 μl of staining solution of FACS buffer containing biotinylated antibodies against CD31 (BD Biosciences, 558737, 1:100), CD45 (BD Biosciences, 553078, 1:200), and Ter-119 (BD Biosciences, 553672, 1:100), for 25 minutes at a 4 °C. Cells were washed twice and resuspended in 100 μl of streptavidin-conjugated APC- eFluor 780 (Thermo Fisher, 47-4317-82) for 20 minutes. Finally, cells were washed twice and resuspended in FACS buffer containing DAPI at a 1:1000 dilution. Flow cytometry was performed using an Attune NxT flow cytometer (Thermo Fisher), and gating was performed to exclude doublets, dead cells, YFP- cells and non-epithelial contaminating cell types (see Supplementary Fig. 2c). Mitochondrial properties were then quantified by measuring the median fluorescence intensity of a given dye in live, YFP + tumor cells. For the quantification of YFP protein expression the mean fluorescence intensity of YFP was quantified for each sample by flow cytometry. For all flow cytometry analyses each data point represents an individual tumor.
## RNA sequencing and human dataset analysis
*For* gene expression analysis in human tumors, RNA-sequencing data was obtained from the Cancer Genome Atlas lung adenocarcinoma dataset2. Due to the relative infrequency of SETD2 mutations, a comparison of mutant and wildtype cases had insufficient statistical power to draw meaningful conclusions. Therefore, mRNA expression data was extracted by the Penn Institute for Biomedical Informatics, and a pearson correlation score was calculated comparing the expression of SETD2 to all other genes. *All* genes were ranked in order according to the genes most negatively correlated with SETD2 expression to most positively correlated, and this rank list was used to perform Gene Set Enrichment Analysis (GSEA) examining GO biological processes using the molecular signature database (MSigDB)52,53. *For* gene expression analysis in K-Ctrl and K-Setd2KO tumors, analysis was performed on previous sequencing results14. *For* gene expression analysis in KPY-Ctrl and KPY-Setd2KO tumors, tumors were microdissected away from normal lung tissue, and digested into a single cell suspension as described above. Live, YFP + tumor cells were isolated by cell sorting, spun down and flash frozen in liquid nitrogen. RNA was extracted using the RNeasy Plus Micro kit (Qiagen, Catalog #74034) using 350 μl of RLT Plus and the QIAshredder columns as per manufacturer’s instructions. Total RNA quantity was measured using the Qubit RNA HS assay kit (ThermoFisher, Catalog #Q32852) and RNA quality was measured using a BioAnalyzer RNA 6000 Nano assay (Agilent, Catalog #5067-1511). Sequencing libraries were prepared on the Illumina NeoPrep and subjected to 75-bp single-end sequencing on the Illumina NextSeq 500 platform. Fastq files for each sample were aligned against the mouse genome, build GRCm38.p5, using Salmon (v0.8.2)54.
Differentially expressed genes were identified with DESeq2 (v1.17.0) and ranked according to the Stat value which considers both the significance, fold-change and directionality of the gene expression change (Supplementary Data 1)55. This rank of genes most upregulated upon Setd2 loss was then used to perform GSEA examining GO biological processes using the MSigDB. GSEA network plots of the top 20 pathways negatively correlated with SETD2 expression in both human and mice were then generated as previously described23. A graphical depiction of the network plot was then generated using Gephi v.0.9.2, and gene sets were characterized according to their functions56. To quantify YFP RNA expression the total YFP read count was quantified from RNA- sequencing data of KPY tumors using Salmon (v0.8.2).
To analyze 4E-BP1 protein levels and phosphorylation in human tumors, level 4 RPPA data from human lung adenocarcinomas was extracted from the Cancer Proteome Atlas website30,31. RPPA z-scores were matched with gene expression and genetic information from each sample represented in the TCGA lung adenocarcinoma dataset2. The RPPA z-score for phosphorylated 4E-BP1(T70) was compared to SETD2 mRNA expression z-scores for each sample. The levels of total 4E-BP1 and phosphorylated 4E-BP1(T70) were also compared between tumor samples with wildtype SETD2, and SETD2 deficiency (defined as tumors containing either an inactivating mutation in SETD2, homozygous loss of the gene, or a loss of 1 copy of SETD2 along with a mRNA z-score < −0.5).
## Electron Microscopy
Tumors were microdissected directly from the lungs of mice and then the tissue was divided in half. One portion of each tumor was fixed for IHC to determine the H3K36me3-status of the given tumor as described above, while the other portion was fixed overnight in an osmium solution obtained from the Penn Electron Microscopy Resource Lab (EMRL), and then submitted to the EMRL for further tissue processing and staining with uranyl acetate and lead citrate. Transmission electron microscopy (TEM) was then performed using a JEOL JEM-1010 for both control and SETD2- deficient tumor samples. Images were taken at 60,000 to 150,000 X magnification, and mitochondrial properties were then quantified using ImageJ, normalizing to the magnification of the image. Mitochondrial size was in Image J, while mitochondrial number was quantified by counting the number of mitochondria per field of view across multiple 15,000 X magnification images. Mitochondrial cristae width was quantified in Image J by drawing a perpendicular line between the inner membranes of cristae, and then quantifying the resulting distance. Mitochondrial electron density was quantified by measuring the mean pixel darkness of the mitochondrial matrix in ImageJ and normalizing this to the mean pixel darkness of the surrounding cytoplasm. Mitochondrial cristae density was quantified by counting the number of cristae in an individual mitochondrion and then dividing by the mitochondrial area.
## Cell lines
SETD2 shRNA sequences from Skutcha et al.57 were cloned into the retroviral MLP shRNA expression vector. shRNA sequences are: shSETD2 #1: CAAGCAAAGAAGTATTCAGAA and shSETD2 #2: CAACCAACAGTCTGTCAGTGT. NCI-H2009 human lung adenocarcinoma cells (from NCI cell line repository) were infected with retrovirus harboring shSETD2 #1, shSETD2 #2, or MLP empty vector. Antibiotic selection was performed and efficiency of SETD2 knockdown was assessed via immunoblot analysis and RT-PCR.
## Immunoblot analysis
For whole cell lysates, cells were lysed in RIPA buffer. Acid-extracted histones were prepared for histone methylation Western blot. Samples were resolved on NuPage 4–$12\%$ Bis-Tris protein gels (Thermo Fisher) and transferred to polyvinylidene fluoride (PVDF) membranes. Blocking, primary and secondary antibody incubations were performed in Tris-buffered saline (TBS) with $0.1\%$ Tween-20. H3K36me3 (1:1000, Abcam, ab9050), SETD2 (1:1000, Cell Signaling Technology, E4W8Q), H3 (1:10000, Abcam ab1791), β-actin (1:10000,Sigma Aldrich, A2066), were assessed by western blotting. β-actin and H3 were used as loading controls. Protein concentration was determined using a BCA protein assay kit (Thermo Fisher Scientific, 23225).
## Quantitative reverse transcription-PCR
Total RNA was extracted from cells using Qiagen RNeasy Mini Kit (Qiagen, 74106). cDNA synthesis was performed using High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, 4368814). RT-PCR was performed using SYBR Green I Nucleic Acid Gel Stain (Invitrogen, S7563) in triplicate, following manufacturer instructions, and evaluated on an Applied Biosystems ViiA 7 RT-PCR machine. Setd2 forward primer: CTTCTACCACGTATCAGCAACC, Setd2 reverse primer: GTAATCACGTGTCCCACCATAC. β-actin forward primer: CCAACCGCGAGAAGATGA, β-actin reverse primer: CCAGAGGCGTACAGGGATAG.
## Seahorse XF Cell Mito Stress Analysis
Oxidative respiration was measured using XF Cell Mito Stress Test Kit (Agilent Technologies, 103015-100). 1 × 104 cells per well were seeded on an XF96 Cell Culture Microplate. Microplate was incubated for 24 h at 37 C. Seahorse XF96 FluxPak sensor cartridge was hydrated with 200 μl of Seahorse Calibrant in a non-CO2 incubator at 37 C overnight. After 24 h, cells were incubated with base medium (Agilent Technologies, 102353-100) containing 2 mM L-glutamine, 1 mM sodium pyruvate, and 10 mM glucose in a non-CO2 incubator at 37 C for 45 min prior to assay. Oxygen consumption rate (OCR) was measured by XFe96 extracellular flux analyzer with sequential injections of 1 μM oligomycin, 1 μM FCCP, and 0.5 μM rotenone/antimycin A. After the run, cells were lysed with 15 μl RIPA buffer and protein concentration was quantified using Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, 23225). OCR measurements were normalized to the protein concentration in each well.
## Statistics and Reproducibility
All analyses were performed using Graphpad Prism (v.8.1.1). For all analyses of mitochondrial properties, immunofluorescence, percentage of p-H3+ cells, normalized tumor areas and RPPA analysis comparing SETD2 wildtype and deficient tumors, unpaired Student’s t-tests were performed. For the comparison of SETD2 mRNA expression and p-4E-BP1(T70) levels by RPPA, a linear regression analysis was performed. Outliers were excluded from rapamycin, IACS-10759 and phenformin experiments using the ROUT method with a Q of $0.1\%$. Sample sizes for individual experiments are indicated in the figure legends. Reproducibility of the findings were confirmed by analyzing at least $$n = 3$$ technical and/or independent biological replicates as indicated in the figure legends. The findings of all the biological replicates were consistent.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Peer Review File Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04618-3.
## Peer review information
: Communications Biology thanks Albert Jeltsch, Fuchun Yang and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary Handling Editors: Marina Holz and Zhijuan Qiu. Peer reviewer reports are available.
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|
---
title: Identifying polymorphic cis-regulatory variants as risk markers for lung carcinogenesis
and chemotherapy responses in tobacco smokers from eastern India
authors:
- Debmalya Sengupta
- Pramiti Mukhopadhyay
- Souradeep Banerjee
- Kausik Ganguly
- Prateek Mascharak
- Noyonika Mukherjee
- Sangeeta Mitra
- Samsiddhi Bhattacharjee
- Ritabrata Mitra
- Abhijit Sarkar
- Tamohan Chaudhuri
- Gautam Bhattacharjee
- Somsubhra Nath
- Susanta Roychoudhury
- Mainak Sengupta
journal: Scientific Reports
year: 2023
pmcid: PMC10006236
doi: 10.1038/s41598-023-30962-9
license: CC BY 4.0
---
# Identifying polymorphic cis-regulatory variants as risk markers for lung carcinogenesis and chemotherapy responses in tobacco smokers from eastern India
## Abstract
Aberrant expression of xenobiotic metabolism and DNA repair genes is critical to lung cancer pathogenesis. This study aims to identify the cis-regulatory variants of the genes modulating lung cancer risk among tobacco smokers and altering their chemotherapy responses. From a list of 2984 SNVs, prioritization and functional annotation revealed 22 cis-eQTLs of 14 genes within the gene expression-correlated DNase I hypersensitive sites using lung tissue-specific ENCODE, GTEx, Roadmap Epigenomics, and TCGA datasets. The 22 cis-regulatory variants predictably alter the binding of 44 transcription factors (TFs) expressed in lung tissue. Interestingly, 6 reported lung cancer-associated variants were found in linkage disequilibrium (LD) with 5 prioritized cis-eQTLs from our study. A case–control study with 3 promoter cis-eQTLs ($p \leq 0.01$) on 101 lung cancer patients and 401 healthy controls from eastern India with confirmed smoking history revealed an association of rs3764821 (ALDH3B1) (OR = 2.53, $95\%$ CI = 1.57–4.07, $$p \leq 0.00014$$) and rs3748523 (RAD52) (OR = 1.69, $95\%$ CI = 1.17–2.47, $$p \leq 0.006$$) with lung cancer risk. The effect of different chemotherapy regimens on the overall survival of lung cancer patients to the associated variants showed that the risk alleles of both variants significantly decreased ($p \leq 0.05$) patient survival.
## Introduction
Exposure to tobacco smoke in active and passive modes is a significant player in the etiology of lung cancer. A high risk of tobacco smoke-induced lung cancer is prevalent in heavy and light smokers1–4. However, all individuals exposed to the same type and dose of tobacco smoke do not develop the disease5. Epidemiological data reveals that about 15–$20\%$ of smokers develop lung cancer while the rest evades the malady4,5, suggesting the existence of individual susceptibility. Although microarray analysis and SNP-based association studies have implicated many genes associated with lung carcinogenesis in tobacco smokers, the precise genetic risk signature(s) or prognostic marker(s) is still obscure.
Aberrant expression of xenobiotic metabolism and DNA repair genes is a hallmark of lung cancer6–9. The Phase I and Phase II xenobiotic-metabolizing genes (XMGs) are involved in the active clearance of tobacco smoke components that prevents subsequent oxidative stress-induced DNA damage in the pulmonary cells. Some of these genes function in the bio-activation of pro-carcinogenic tobacco smoke components into highly reactive and potent carcinogens resulting in increased carcinogen load in the lung cells10–12 causing DNA lesions. The increased burden of carcinogenic metabolites in the pulmonary cells causes increased genomic insults leading to DNA lesions13–15. Increased risk of smoking-induced lung cancer is, thus, not only due to exogenous/tobacco smoke contents but their interactions with genes involved in their detoxification or bio-activation10–12 and the extent and efficiency of repair of DNA damage16,17 caused by tobacco smoke. Microarray and RNA-seq analysis revealed differential expression patterns of XMGs and DNA repair genes (DRGs) in the airway bronchial epithelium of healthy smokers (HS)18 compared to healthy non-smokers (HNS)14,17 as well as in smokers with lung cancer (SLC) (Supplementary material Fig. S1). Therefore, genes with higher expression in smokers than non-smokers indicate their role in response to tobacco smoke, and their lower expression in lung cancer patients could be due to their inherent ineffective status. However, some genes overexpress in lung cancer patients and increase the carcinogenic load within the cells due to the bioactivation of smoke metabolites.
The regulation of differential gene expression could be due to the variations in the cis-regulatory elements of the gene concerned, often present at long-range upstream or downstream to the transcription start sites. The ENCODE (ENCyclopedia Of DNA Elements)19,20 has revealed the genomic positioning of DNase I hypersensitive sites (DHS), which are open chromatin structures accessible to DNA binding proteins like transcription factors21. Transcriptional regulation by proximal or distal DHS could be modulated by single nucleotide variants through alteration in the transcription factor (TF)-binding and structural looping22–24. Thus, these DHS-SNVs could be responsible for the aberrant expression of XMGs and DRGs in a certain fraction of the smoker population, resulting in the accumulation of carcinogens within the pulmonary cells causing oxidative DNA lesions17, which, if not repaired effectively, might lead to a tumorigenic transformation of the cells. These SNVs, individually or together25, could act as risk markers of lung cancer, conferring an inherited predisposition in specific individuals.
The standard adjuvant chemotherapy regimens include platinum-based drugs, which are ineffective in increasing the median life expectancy of lung cancer patients and are also extensively toxic26,27. Earlier investigations have reported differential gene expression as a predictor for determining patient-specific chemotherapy regimens28 and polymorphic variants’ role in modifying chemotherapeutics’ sensitivity and efficacy on different cancers29,30. Bioactivation and bioavailability of chemotherapeutic drugs depend on phase I and phase II xenobiotic metabolism enzymes, making them a central player in the efficacy of lung cancer treatment31. Moreover, most standard chemotherapy drugs introduce DNA damage, which, if repaired, leads to lesser efficacy of the drugs32. Therefore, the differential expression of specific lung cancer-associated genes from xenobiotic metabolism and DNA repair pathways due to cis-regulatory variants could modulate the efficacy of standard adjuvant chemotherapy.
Therefore, this study aims to identify, annotate and prioritize the DHS-SNVs of xenobiotic metabolism and DNA repair genes as genetic susceptibility markers for lung cancer in tobacco smokers, followed by a case–control association study on the eastern Indian population. Further, we aimed to evaluate the role of lung cancer-associated regulatory SNVs on the effect of standard chemotherapy drugs used to treat the patients and their overall survival.
## Selection of candidate genes
We followed a detailed literature search to identify xenobiotic metabolism and DNA repair genes showing differential expression between lung cancer and healthy individuals. Following this, we checked the SEGEL database33 for expressional differences between HNS and HS groups. We considered all the genes that showed differential expression ($p \leq 0.05$) and no significant expressional differences between the HNS and HS groups in more than two lung cell types. We did not consider the alveolar macrophage cell type from the SEGEL database in our study. Similarly, we listed median-gene expression of the same set of genes between HS and SLC individuals reported in the literature4,7,9 and ONCOMINE34 considering fold change ≥ 1.5 and $p \leq 0.05.$ Further, we validated the expression of the selected genes by comparing their expression between the lung adenocarcinoma (LUAD) and/or lung squamous cell carcinoma (LUSC) RNA-seq datasets of The Cancer Genome Atlas (TCGA) and normal lung epithelium from GTEx processed and presented as a web server, GEPIA (Gene Expression Profiling Interactive Analysis)35 (http://gepia.cancer-pku.cn/). Based on our hypothesis, we listed those genes that showed differential normalized median expression considering fold change ≥ 1.5 and p-value < 0.05 between the LUAD/LUSC and GTEx datasets as our selection criteria for our SLC vs. HS group. Finally, we selected those genes that showed reciprocal expressional patterns between HS vs. HNS and SLC vs. HS groups.
## Selection of candidate DHS and DHS-SNVs
We curated the top 10 expression-correlated DHS (GRCh37/hg19 human genome assembly; cut-off $p \leq 0.05$) from the "Regulatory Elements Database" (http://DNase.genome.duke.edu/)21,36,37 for each of the selected genes. According to Sheffield et al.21, this method calculates Pearson correlation across samples between gene expression and normalized DNase I scores for each DHS within 100 kb of each gene. A minimum value for DNase I signal and gene expression is set, followed by the calculation of permutation P-value using the null distribution of DHS correlations for each gene to a random sample of 10,000 DHSs from different chromosomes ($p \leq 0.05$). We obtained the SNVs within such selected DHS from the UCSC Table Browser38 (http://genome.ucsc.edu/). The UCSC Table Browser was configured to our desired settings by changing the default assembly parameter to Feb 2009.GRCh37/hg19″, “group: variation”, “track: common SNPs [141]”, “table: All SNPs [141]”.
## Computational prioritisation of DHS-SNVs
We used the ENCODE data analyzing tools: rSNPBase 1.039 and RegulomeDB v 1.140 to prioritize DHS-SNVs to ascertain their regulatory potential. We performed SNV enrichment analysis for the rSNPBase and RegulomeDB filtering steps for the DHS-SNVs compared to a universe of randomly selected SNVs. For all the 23 XMGs and 25 DRGs, we selected the transcription start sites (TSS) ± 100 kb region and extracted all the SNVs listed in the dbSNP build 141. Among this pool of SNVs, we randomly selected 1720 SNVs from the XMGs and 1264 SNVs from DRGs as the universe of SNPs. Then, we performed Fisher’s exact text to evaluate the difference in the outcomes between the DHS-SNVs and the universe of SNVs at $5\%$ level of significance. Further, we assessed the impact of DHS-SNVs in genotype-specific transcriptional regulation of target genes in normal healthy post-mortem lung tissue from GTEx Portal v6 (https://www.gtexportal.org/home/)41–43. Similarly, as mentioned above, we performed SNV enrichment for the GTEx filtering step for the DHS-SNVs compared to the universe of randomly selected SNVs. Lung cell-type-specific DHS of the genes were obtained from the Regulatory Elements Database21, considering DHS peak for at least one lung cell type. Further, LD blocks of the cis-eQTLs were obtained from HaploReg v4.1 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php)44 based on the information from 1000 genome phase 3 data. The gain or loss of transcription factor binding sites (TFBS) due to rSNVs from position weight matrices (PWM) listed in JASPAR45 and ENCODE motif libraries were statistically (pimpact < 0.001) evaluated in an R-based web server, known as “atSNP” (http://atsnp.biostat.wisc.edu/)46. Further, the expression of such TFs in lung cancer was determined from the Database of Transcription Factors for Lung Cancer (DbTFLC) (https://vit.ac.in/files/database/Home.php)47.
## Regulatory functional annotation of prioritized rSNVs
Further, we assessed the prioritized rSNVs for more functional attributes that justify their cis-regulatory role in modulating lung cancer risk through the following analyses:
## Epigenomic signatures at the rSNVs
According to their epigenetic marks, we classified the identified cis-eQTLs (rSNVs) into functional chromatin domains, such as enhancers, promoters, and insulators. The data was obtained from HaploReg v4.144, which hosts the epigenomic data of the Roadmap *Epigenomics consortium* 201548,49.
## Cis-eQTLs in lung cancer
The PancanQTL web server (http://bioinfo.life.hust.edu.cn/PancanQTL/)50 contains the processed cis-eQTL mapped data on 33 different cancers from The Cancer Genome Atlas (TCGA) raw data. We used this webserver to analyze the cis-eQTL mapping of the prioritized rSNVs of the selected XMGs and DRGs in lung cancer datasets.
## Linkage disequilibrium (LD) block of rSNVs
We prioritized the LD SNPs (r2 ≥ 0.8) of the prioritized rSNVs for their association with lung cancer and other carcinogen-induced cancer. In addition, we obtained the LD block SNPs from HaploReg v4.144 for each of the queried rSNVs, which was taken from the 1000 Genome Project Phase 3 data. Finally, we checked for an indirect association of the rSNVs with lung cancer by itself being in LD with lung cancer-associated SNPs.
## Co-occurrence of risk alleles and unweighted genetic risk scores
Furthermore, we assessed the co-occurrence of risk alleles of the prioritized rSNVs for all the 26 populations listed in the 1000 Genome51 Phase 3 data to identify the risk population based on their unweighted genetic risk scores. In addition, we calculated the unweighted genetic risk score (uGRS)52 (i.e., the summation of the number of risk alleles across all the prioritized rSNVs) for each 1000 Genome Project enlisted population.
## Interactome analysis
We performed an interactome analysis for the prioritized protein coding in STRING v10.5 (http://string-db.org)53,54, including known and predicted protein–protein interactions. The interactome was expanded to gain more interactors, with a required confidence score > 0.4 as the cut-off.
## Selection of the study subjects
This study included lung cancer patients ($$n = 101$$) from Saroj Gupta Cancer Centre and Research Institute and the Department of CHEST, IPGME&R in Kolkata. We recruited clinico-radiologically confirmed healthy smokers ($$n = 401$$) above 55 years55 of age and without any history of cancer as controls. The patients and controls belong to the same geographical region with a confirmed smoking history. We did not consider former smokers (who had quit smoking ≥ 15 years) for the study. First, a detailed questionnaire that included age at sample collection, ethnicity, pack-years, and tumor details like histotype, and TNM staging56, were filled up under medical supervision. Then, we noted a detailed account of the followed treatment regimen, including drug combinations, dosages, cycles, responses, and survival time and status. All patients received platinum-based doublet chemotherapy consisting of either cisplatin or carboplatin and another drug in combination. Initially, the patients received 4 cycles of chemotherapy with careful observations of their responses. The treatment was stopped if significant toxicity was observed; otherwise, it was extended to 6 cycles.
## Collection of blood samples
We collected 10 ml of peripheral blood by venipuncture from lung cancer patients and healthy controls under the supervision of our collaborating clinicians in ethylene-diamine-tetraacetic acid (EDTA) coated tubes. Before sample collection, we obtained informed written consent from the subjects or their family members for voluntary participation in the study.
## Isolation of genomic DNA and genotyping
We performed the conventional phenol–chloroform method57 to isolate genomic DNA and store them at − 20 °C. In addition, we used the PCR–RFLP method for genotyping. Primer sequences were custom designed in Primer3 software (http://bioinfo.ut.ee/primer3-0.4.0/primer3/) and purchased from Integrated DNA Technologies (IDT), USA. The restriction enzymes (New England Biolabs) and their cut patterns were determined from NEBcutter V2.0 (http://nc2.neb.com/NEBcutter2/). We performed the PCR with the reaction mixture (20 µl) containing 50–80 ng of genomic DNA, 20 pmol of each primer, 10 μl of 2X GoTaq PCR Master Mix (Promega), and adjusted the final volume to 20 µl with nuclease-free water. After the quality check, the PCR amplicons were digested with their respective restriction enzymes following the manufacturer’s (NEB) protocol and run on $12\%$ polyacrylamide gels (non-SDS) with 100 bp DNA Ladder (Promega, Cat No. G2101). Three independent individuals verified each gel, 2 without having prior knowledge of the case/control status of the subjects, to avoid biased genotype calls. Further, we confirmed the genotype status of ~ $10\%$ of the study subjects by Sanger Sequencing.
## Statistical analyses
The statistical analyses were performed in R Version 3.4.258, considering statistical significance at $p \leq 0.05$ (two-sided). We performed a goodness of fit chi-square test to assess the Hardy–*Weinberg equilibrium* status of the variants in our control population. Student t-test and Pearson’s chi-square tests evaluated the association of allele distribution and the demographic variables with lung cancer. Further, we performed logistic regression in additive, dominant, and recessive genetic models to assess the odds ratio (OR), standard error (SE), and $95\%$ confidence intervals ($95\%$ CI) adjusted for covariates to measure the association of the rSNVs with lung cancer risk. We also conducted a subgroup analysis and effect modification test for the rSNPs stratified by covariate status on lung cancer risk.
Furthermore, we have independently replicated all the 22 rSNVs from the C34-Malignant neoplasm of bronchus and lung dataset of the UK Biobank hosted in the Gene Atlas webserver (http://geneatlas.roslin.ed.ac.uk/)59. We performed a Kaplan–Meier log-rank test that estimated the overall survival (OS) distribution for each lung cancer-associated rSNVs. The multivariate Cox-proportional hazard model was used to assess the effect of each rSNPs on the OS of lung cancer patients, adjusted for age, sex, and pack-years of smoking. Finally, we used the Cox hazard methodology to evaluate the relationship between OS and the rSNVs stratified by drug combinations of the treatment regimen. The patients that showed complete or partial responses to the treatment were categorized as responders.
In contrast, patients with stable disease, poor responses, or progressive disease were grouped as non-responders. The time to event for the survival analysis was considered a negative outcome, i.e., time to death from the administration of the therapy. The Kaplan–Meier log-rank and Cox-proportional Hazard tests were done in R using survival60 and survminer61 packages. Furthermore, we compared the responses of the first and second chemotherapy regimens with the OS of the lung cancer patients considering the effects of the variants rs3764821 (ALDH3B1) and rs3748523 (RAD52) through a Cox proportional hazards model.
## Ethics approval and consent to participate
The Ethics Committee of Saroj Gupta Cancer Centre and Research Institute (IEC SGCCRI Ref No-2017/MS/1; dated: 11.10.2017), IPGME&R (Memo No. Inst/IEC/$\frac{2015}{545}$; dated: 10.12.2015), Kolkata and the University of Calcutta (Ref No: $\frac{0024}{16}$-$\frac{117}{1434}$; dated: 24.10.2016), Kolkata, India; approved the study with human subjects as per the regulation of the Indian Council of Medical Research (ICMR) following the Declaration of Helsinki, 1964. Informed consent was obtained from all individual participants included in the study.
## Gene prioritization
Text mining revealed 53 xenobiotic metabolism genes (XMG) and 67 DNA repair genes (DRG) as contenders for identifying rSNVs. *These* genes are potential candidates for tobacco smoke metabolism and smoke-induced DNA damage repair (Fig. 1). Among the 53 XMG and 67 DRG, further analysis of microarray datasets of ONCOMINE and SEGEL, and RNA-seq dataset of GEPIA, revealed 34 XMG and 17 DRG to be up-regulated, 11 XMG and 26 DRG to be down-regulated, 7 XMG and 24 DRG with no significant difference in the median expression and 3 XMG to be inconclusive when healthy smokers (HS) were compared to healthy non-smokers (HNS). The set of genes as obtained were clustered as ‘Set A.’ Again, among the 53 XMG and 67 DRG, 23 XMG and 38 DRG were down-regulated; 23 XMG and 22 DRG as up-regulated; only 4 XMG and 1 DRG were found with no significant expressional change in Smoker Lung Cancer (SLC) group compared to HS. These were grouped as ‘Set B’ for the mentioned study groups (Supplementary material Table S1 and Fig. S2). Five XMG and 6 DRG showed inconclusive results. For the xenobiotic metabolism gene set, the selection of genes was segregated into the following categories: (a) genes that were found to be up-regulated in ‘Set A’ but down-regulated in ‘Set B,’ (b) genes down-regulated in ‘Set A’ but up-regulated in ‘Set B,’ and, (c) genes with no significant expressional change in ‘Set A’ but downregulated in ‘Set B.’ For DRGs, the genes belonging to category (b) were not considered for further prioritization because the higher expression of DNA repair genes should not render individuals susceptible to tobacco smoke-induced lung carcinogenesis. *Our* gene prioritization pipeline revealed 23 XMG and 25 DRG potential susceptibility markers for tobacco smoke-induced lung carcinogenesis (Fig. 1). Therefore, $43.4\%$ of total XMGs and $37.31\%$ of total DRGs show significant differential expression between SLC and HS.Figure 1Pathway analysis for identifying regulatory genetic loci as susceptibility markers conferring risk towards tobacco smoke-induced lung carcinogenesis. ( A) *After a* detailed literature review on epidemiological reports, association studies, expression studies, and case studies followed by ONCOMINE and TCGA validation, important xenobiotic metabolism and DNA repair genes were selected in cigarette smoke-induced lung cancer. ( B) The selection and screening of genes, their DNase I hypersensitive sites, and SNVs within the DNase I hypersensitive sites by step-wise use of in silico tools and databases to prioritize potential regulatory SNVs as susceptibility loci in lung carcinogenesis with replications in case–control cohorts. DHS, DNase I hypersensitive sites, eQTL, expression quantitative trait loci, XMG, xenobiotic metabolism gene group, DRG, DNA repair gene group, rSNP, regulatory single nucleotide polymorphism, MAF, minor allele frequency.
## Curation of expression-correlated DHS and DHS-SNVs
From the Regulatory Elements Database, we curated 370 expression-correlated DHS ($p \leq 0.05$) for the 48 prioritized genes. The regulatory elements database enlists DHS sites, which showed correlations between DNase hypersensitivity to the expression of the nearest genes. For example, we listed 181 positively and 77 negatively correlated DHS coordinates for 23 XMGs. Similarly, 174 positively correlated and 99 negatively correlated DHS coordinates were found for 25 DRG (Supplementary material Table S2). Furthermore, screening for SNVs within these DHS sites revealed 1720 SNVs for xenobiotic metabolism genes, of which 1187 SNVs belonged to positively correlated DHS and 533 SNVs to negatively correlated DHS. Similarly, 1264 SNVs were obtained for DNA repair genes that consist of 813 SNVs within positively correlated DHS and 451 SNVs within negatively correlated DHS (Fig. 1).
## Functional annotation and prioritization of DHS-SNVs
Analysis of 1720 SNVs of xenobiotic metabolism and 1264 SNVs of DNA repair genes in rSNPBase 1.0 revealed 526 SNVs (ORenrichment = 1.48, $95\%$ CI = 1.27–1.72, penrichment = 3.3 × 10−7) and 609 SNVs (ORenrichment = 1.37, $95\%$ CI = 1.18–1.59, penrichment = 2.4 × 10−5) as ‘rSNPs,’ respectively based on various regulatory features such as the proximal and distal regulatory effect of the SNV, RNA binding protein-mediated regulation, and miRNA-mediated regulation in an SNV-specific manner (Supplementary material Table S3). The 1135 SNVs (526 SNVs + 609 SNVs) obtained from rSNPBase were then queried to RegulomeDB v1.1. Scores ranging between 1a to 1f indicate a high regulatory potential of the SNV concerned. Scores between 2a to 3b depict evidence of transcription factor binding disruption without any evidence of QTL; score 4 implies supporting evidence of transcription factor binding and DNase peak. In contrast, scores 5 and 6 depict minimal to no evidence for regulatory annotation of the SNVs37. We selected 419 SNVs (ORenrichment = 3.40, $95\%$ CI = 2.86–4.04, penrichment = 2.2 × 10−16) from the XMG set and 392 SNVs (ORenrichment = 3.1, $95\%$ CI = 2.57–3.75, penrichment = 2.2 × 10−16) from the DRG set with scores between 1a to 4 for further prioritization (Supplementary material Table S4). GTEx portal (http://www.gtexportal.org/home/) revealed 13 SNVs from 7 XMG (ORenrichment = 2.13, $95\%$ CI = 1.03–4.37, penrichment = 0.037) and 9 SNVs from 7 DRG (ORenrichment = 2.64, $95\%$ CI = 1.27–5.87, penrichment = 0.006) as lung tissue-specific cis-eQTLs ($p \leq 0.05$) (Table 1; Supplementary material Table S5) of the respective genes (Supplementary material Fig. S3). During this analysis, risk alleles were identified based on the genotype-specific expression of the concerned gene following the expressional status in SLC. Ambiguous QTL data that failed to interpret allele-specific expression were not considered for further analysis. The 22 prioritized potential regulatory SNVs (rSNVs) reside within at least one lung cell-type DHS studied in the ENCODE project, justifying tissue-specific transcriptional cis-regulation (Supplementary Material, Table S6). Analysis of these rSNVs through the atSNP web server predicts 15 rSNVs to impart statistically significant gain of TFBS for 39 transcription factors (TFs). Similarly, 13 rSNVs were predicted to exhibit a statistically significant loss of TFBS for 28 transcription factors (TFs) (Supplementary material Table S7). Out of these 67 TFs, mining the DbTFLC revealed 44 TFs for 22 rSNVs of 14 genes to express in lung cancer (Supplementary material Table S8). Thus, these 22 rSNVs predictably alter the binding of 44 TFs found to express in lung cancer, further substantiating the loci’s cis-regulatory attribute. HaploReg v4.1 revealed rs1802061C > T (synonymous SNP; Q117Q) and rs4986947G > A (intronic SNP) of GSTA4 to be in LD. Similarly, rs2153608A > G (intronic SNP) and rs3219472C > T (intronic SNP) of MUTYH were also found to be in LD (Supplementary material Table S9).Table 1Chromatin States and risk allele prediction of DHS-SNVs as cis-eQTLs of the target genes belonging to Xenobiotic metabolism and DNA repair pathway. Gene symbolSNPp-valueEffect sizeTissuePredicted risk allelecancer typeBeta (β)t-statp-valueRisk alleles in lung cancerThe Chromm States in the lung (25-core model)[I] Xenobiotic metabolism genes SULT1A1rs7435902.00E−11− 0.37LungGLUAD− 0.2− 4.026.68E−05GActive Enhancer 1 SULT1A1rs37600911.40E−120.36LungGNo dataNo dataNo dataNo dataNo dataPromoter Upstream of TSS SULT1A1rs1124112100.0120.38LungANo dataNo dataNo dataNo dataNo dataActive Enhancer 2 GSTA1rs109487234.00E−07− 0.15LungCLUAD0.358.046.92E−15CQuiescent SULT1A2rs7435905.50E−060.21LungANo dataNo dataNo dataNo dataNo dataActive Enhancer 1 GSTA1rs22079506.80E−050.12LungALUAD0.275.945.39E−09AQuiescent GSTA4rs18020610.0015− 0.15LungTNo dataNo dataNo dataNo dataNo dataQuiescent ALDH3B1rs37648210.0023− 0.08LungGNo dataNo dataNo dataNo dataNo dataPromoter Downstream of TSS 1 SULT1A2rs37600910.023− 0.097LungCNo dataNo dataNo dataNo dataNo dataPromoter Upstream of TSS GSTO1rs122505920.0270.13LungCNo dataNo dataNo dataNo dataNo dataPromoter Downstream of TSS 1 GSTO1rs178831500.000850.078LungGNo dataNo dataNo dataNo dataNo dataPrimary DNase site GSTA4rs49869470.0015− 0.15LungANo dataNo dataNo dataNo dataNo dataQuiescent SULT1A2rs133313760.0058− 0.39LungTNo dataNo dataNo dataNo dataNo dataPromoter Upstream of TSS GSTO1rs70834650.0270.13LungGNo dataNo dataNo dataNo dataNo dataPrimary H3K27ac possible Enhancer MAFGrs355686250.042− 0.043LungCNo dataNo dataNo dataNo dataNo dataQuiescent[II] DNA *Repair* genes RAD52rs37485232.50E−28− 0.39LungGNo dataNo dataNo dataNo dataNo dataActive TSS EME2rs2386790.00017− 0.11LungGNo dataNo dataNo dataNo dataNo dataQuiescent EME2rs16253930.0012− 0.13LungGNo dataNo dataNo dataNo dataNo dataQuiescent ERCC5rs41502760.00090.094LungTNo dataNo dataNo dataNo dataNo dataQuiescent MUTYHrs21536080.012− 0.091LungGNo dataNo dataNo dataNo dataNo dataWeak Enhancer 2 MUTYHrs32194720.026− 0.082LungTNo dataNo dataNo dataNo dataNo dataQuiescent POLMrs117643441.40E−11− 0.38LungCNo dataNo dataNo dataNo dataNo dataQuiescent PMS1rs57429260.000130.15LungGLUSC0.294.114.72E−05GPromoter Downstream of TSS 1 MLH1rs1450704980.01− 0.12LungTNo dataNo dataNo dataNo dataNo dataPromoter Upstream of TSSData from healthy cadaver lung tissue as obtained from the Genotype to Tissue Expression (GTEx) dataset. For lung cancer tissue, cis-eQTL mapping data was obtained from the PancanQTL webserver by analyzing the TCGA data. The chromatin states that data were obtained from HaploRegv4.1 linked to RoadMap Epigenomics, 2015 data. Normal lung tissue-specific cis-eQTL was calculated, and screening of the rSNVs as cis-eQTL was based on a p-value < 0.05*. The risk alleles from the lung cancer group match that of the predicted risk alleles in healthy individuals. FDR corrected $p \leq 0.05$*. TSS, transcription start site. Lung cancer cis-eQTLs are depicted in bold. Significant values are in bold.
## Cancer-associated SNPs in LD with the prioritized rSNVs
Text mining of independent candidate association studies revealed 2 prioritized rSNVs, i.e., rs3748523 in the DHS of RAD5262 and rs4150276 in the DHS of ERCC563, reported to be associated with lung cancer previously. The risk allele reported in the literature for these SNVs matches those predicted through our pipeline, thus providing evidence for the precision of our in silico data mining pipeline. We enlisted the 858 LD SNPs (r2 > 0.8) for all our 22 predicted rSNVs from HaploReg v4.1 and checked the literature for their association with cancer. Text mining revealed 5 lung cancer-associated SNPs in LD with 5 of our prioritized rSNVs.
Furthermore, 8 SNPs associated with other carcinogen-induced cancers were found in LD, with 6 of our prioritized rSNVs, of which 3 are shared with lung cancer (Supplementary material Table S10). This cross validates 8 of our prioritized rSNVs to be functionally relevant in carcinogenesis. Furthermore, we checked for the association of LD SNPs with lung cancer in the UK Biobank GWAS dataset C34 Malignant neoplasm of bronchus and lung, hosted by the Gene Atlas webserver (http://geneatlas.roslin.ed.ac.uk/search/) and found 57 LD-SNPs of 2 prioritized rSNVs associated with lung cancer. Therefore, we obtained 62 (57 + 5) LD-SNPs associated with lung cancer from UK Biobank and literature. Similarly, we curated the 1010 LD-SNPs of randomly selected 22 SNPs from the TSS ± 100 kb region of the 23 XMGs and 25 DRGs. This set of 1010 LD-SNPs was considered the universe of SNPs. Out of these 1010 LD-SNPs, we found 26 LD-SNPs to be associated with lung cancer. Therefore, the LD-SNPs of the rSNVs are significantly enriched (ORenrichment = 2.81, $95\%$ CI = 1.73–4.67, penrichment = 8.78 × 10−6). Thus, due to strong LD (r2 > 0.8), there is transitive evidence that the prioritized rSNVs are also associated with lung cancer. In such a case, the prioritized rSNVs could be the causal variants or impart a combinatorial effect on lung cancer pathogenesis with another functional variant. The literature search also revealed 5 coding SNPs from 5 of our prioritized genes associated with lung cancer in Caucasian, Chinese, and Japanese populations (Supplementary material Table S11). This implies a higher risk of tobacco smoke-dependent lung carcinogenesis if the genes harbor the risk alleles of both coding and regulatory polymorphisms leading to significant impairment of gene activity and expression.
We found nominal associations ($p \leq 0.05$) of three rSNVs, such as rs35568625 (MAFG), rs3760091 (SULT1A2), and rs743590 (SULT1A1), with lung cancer in 1655 cases and 450,609 controls of all white British origin samples from the UK Biobank GWAS dataset C34 Malignant neoplasm of bronchus and lung, hosted by the Gene Atlas webserver (http://geneatlas.roslin.ed.ac.uk/search/). However, the three rSNVs, viz. rs3764821 (ALDH3B1), rs3748523 (RAD52), and rs5742926 (PMS1), with which we performed our case–control association study failed to show any association with lung cancer in the C34 Malignant neoplasm of bronchus and lung GWAS dataset (Supplementary material Table S12). From the pool of 2984 randomly selected SNPs within the TSS ± 100 kb region of 23 XMG and 25 DRGs, we randomly subsetted 100 SNPs as the universe of SNPs and found only 4 SNPs to be associated with lung cancer in the UK Biobank GWAS dataset C34 Malignant neoplasm of bronchus and lung (ORenrichment = 11.05, $95\%$ CI = 1.51–70.31, penrichment = 0.009).
## Epigenomic signatures classifying the rSNVs into chromatin domains
Using the Roadmap Epigenomic data, the 22 prioritized rSNVs were classified by their epigenomic signatures into functional chromatin domains specific to lung tissue. The analysis revealed 4 prioritized rSNVs bearing enhancer marks, 8 rSNVs with transcription start site flanking region/promoter marks, and 11 rSNVs with insulators/ heterochromatin/ repressed region-specific epigenomic marks (Supplementary material Table S13).
## Population segregation based on unweighted genetic risk score
The 1000 *Genome data* revealed 12 rSNVs from the 9 prioritized XMGs and 8 rSNVs from 8 DRGs to be polymorphic with global MAF > 0.01 (Supplementary Material, Table S14). The mean uGRS estimate for each of the geographical populations of the 1000 Genome project for 22 prioritized rSNVs revealed that the Europeans (uGRS = 83.51) are at the highest risk of developing tobacco-related lung cancer, followed by the East Asians (uGRS = 82.9) and South Asians (uGRS = 80.58). The Latin Americans (uGRS = 74.71) were at least risk, followed by the Africans (uGRS = 79.1) for tobacco smoke-induced lung carcinogenesis. However, the mean uGRS calculated for each subpopulation of the 1000 *Genome data* revealed the Gambians in the Western divisions in the Gambia (GWD) (uGRS = 95.92) to be at the highest risk of developing tobacco smoke-induced lung cancer, followed by Yorubans in Ibadan, Nigeria64 (uGRS = 90.96) and Iberians in Spain (IBS) (uGRS = 88.71). On the other hand, Americans of African Ancestry in South West USA (ASW) (uGRS = 50.42) are the population at least risk of tobacco smoke-induced lung cancer, followed by people of Mexican Ancestry from Los Angeles, USA (MXL) (uGRS = 56.04) and Mende people in Sierra Leone (MSL) (uGRS = 69.71). ( Supplementary material Table S15).
## Interactome analysis for more candidate genes
The interaction network analysis between the prioritized genes and expanded to 50 more interactors revealed strong associations among the GST family (GSTA1, GSTA4, GSTO1) proteins with a high mean score greater than 0.9. Furthermore, other candidate players, such as TP53, NFE2L2, TPT1, and NF2, involved in apoptosis, cytoskeletal remodeling, cell cycle regulation, cancer stemness, and many critical cancer regulatory pathways, were found to interact with our prioritized protein-coding genes (Supplementary material Fig. S4). The analysis revealed TP53 as the nodal gene that connects the xenobiotic metabolism pathway with apoptosis, DNA repair, cytoskeletal remodeling, and cancer stemness. Furthermore, co-expression of our prioritized genes with other reported lung cancer-associated genes was found, which depicts their possible functional interplay in the disease pathogenesis (Supplementary material Table S16). Pathway analysis revealed a cross-regulation between cytoskeletal remodeling, metastasis, apoptosis, xenobiotic metabolism, DNA repair, and cell cycle regulatory pathways. Such cross-regulation among the pathways reveals the gene regulatory interactome in lung cancer pathogenesis (Supplementary material Tables S17–S20).
## Analysis of mapped cis-eQTL in lung cancer cases
The prioritized rSNVs were further assessed for their cis-regulatory potential in lung cancer cases on the processed TCGA data hosted by the PancanQTL webserver. The analysis revealed a subset of 4 rSNVs as significant cis-eQTLs in both Lung Adenocarcinoma (LUAD) and Lung Squamous cell carcinoma (LUSC) datasets after false discovery rate (FDR) correction (pFDR < 0.05) (Supplementary material Fig. S5). Furthermore, the risk alleles of these 4 cis-eQTLs match our prediction, indicating the precision and accuracy of the predictive analysis (Table 1).
## The clinical and demographic attributes of the study subjects
The study involved 101 smoker cases and 401 smoker controls with a mean age of 58.93 ± 12.29 and 66.18 ± 7.85, respectively, collected from two hospitals in Kolkata. The formula for estimating pack-years of smoking: [(No. of cigarettes/beedis /cigars)/20) × No. of years smoked] showed no significant difference between cases and controls. However, the distribution of males over females is higher in both cases and controls. This contributed to a sex bias in our sampling of controls, for which we were unable to consider the parameter of gender in our association study. The histological subtype Adenocarcinoma (ADC) was found to be the most abundant type of lung cancer, followed by Squamous cell carcinoma (SqCC) and Small cell lung cancer (SCLC).
Furthermore, TNM staging data, available for 99 patients, showed that Stage III and Stage IV were highly over-represented compared to Stage I and Stage II, probably due to late reporting of the patients to oncologists. For 2 patients, TNM staging was not done till the date of sample collection. Nearly $90\%$ of the lung cancer cases of our sample population exhibit distant metastasis (M1), while the remaining patients did not show any sign of metastasis till the date of collection. The clinical and demographic characteristics, including age, sex, pack-years, tumor histology, TNM staging, and metastases, are summarised in (Table 2, Supplementary material Table S21).Table 2Clinical and demographic characteristics of lung cancer patients and controls. VariableCases, $$n = 101$$Controls, $$n = 401$$p-valueAge––– < 3980 40–4980 50–592872 60–6943212 ≥ 7014115Mean ± SD58.65 ± 12.1266.14 ± 7.83< 0.001***Pack years < 209103 20–4926156 ≥ 5066130Mean ± SD66.92 ± 34.9559.29 ± 37.470.064Gender Male80400< 0.001*** Female211Tumour histology Adenocarcinoma (ADC)50 Squamous cell carcinoma (SqCC)39 Small cell lung cancer (SCLC)13 Others1TNM staging I2 II11 III48 IV40 Unknown2Metastasis No9 Yes91SD standard deviation, N total number of case-patients or control subjects.p-values for sex were derived from the Chi-square test; the Student t-test was used for age and pack-years. All P-values are two-sided. $p \leq 0.05$ was considered statistically significant.
## Regulatory polymorphic variants and their association with lung cancer risk
The 1000 *Genome data* revealed 12 rSNVs from the 9 prioritized XMGs and 8 rSNVs from 8 DRGs to be polymorphic with global MAF > 0.01, and we would designate them as SNPs from now on in the text as per the definition of the term (Supplementary Material, Table S21). Out of these 20 rSNPs, 3 promoter rSNPs with GTEx p-value < 0.01, i.e., rs3764821 for ALDH3B1, rs3748523 for RAD52, and rs5742926 for PMS1, were selected for our case–control association analysis on the East Indian population. After genotyping, the three rSNPs were found in Hardy–*Weinberg equilibrium* (Supplementary Material, Table S22). Sanger Sequencing reconfirmed that the genotype calls in about $10\%$ of the total samples. The representative gel and chromatogram pictures are shown in (Fig. 2).Figure 2Genotyping of (A) rs3764821 of ALDH3B1, (B) rs3748523 of RAD52 and (C) rs5742926 of PMS1 by PCR–RFLP method with a representative chromatogram of Sanger sequencing for each genotype of the rSNPs. For rs3764821, Gel 1: Lane 1,2,4,6 depicts AG genotypes with cut patterns as 240 bp, 198 bp, 42 bp; Lane 3, 7, 8 depicts GG genotypes with 198 bp and 42 bp fragments. For rs3748523, Gel 2: Lane 1,2,3,7 & 8 depicts CC genotypes with 228 bp and 24 bp (not visible) fragments; Lane 4 & 6 depicts GG genotypes as uncut (252 bp) fragments; Lane 8 depicts CG genotype with 252 bp, 228 bp and 24 bp fragments. For rs5742926, Gel 3: Lane 1, 2 & 5 depicts GG genotypes as uncut (340 bp) fragments and Lane 4 depicts GT genotype with 340 bp, 231 bp, 109 bp. A representative chromatogram for heterozygous peak is also provided for each rSNP.
Analysis by Pearson’s chi-square revealed an association between the predicted risk allele of rs3764821-ALDH3B1 (G: OR = 2.54, $95\%$ CI = 1.55–4.15, $$p \leq 0.00022$$***) and rs3748523-RAD52 (G: OR = 1.65, $95\%$ CI = 1.13–2.41, $$p \leq 0.01$$*) with lung cancer while, no significant association of rs5742926 (PMS1) with lung cancer in smokers was found (Table 3).Table 3Association of 3 promoter cis-eQTLs belonging to Xenobiotic metabolism and DNA repair pathway. Gene-polymorphismGenotypes/allelesSmoker lung cancer cases; $$n = 101$$ (%)Healthy smoker controls; $$n = 401$$ (%)ModelComparisonsOR ($95\%$ CI)ap-valueaAdjusted OR ($95\%$ CI)bp-valuebALDH3B1-rs3764821 A > GAA71 (70.3)345 [86]AdditiveAA vs. AG vs. GG2.64 (1.63–4.29)0.00009***2.51 (1.42–4.67)0.002**AG28 (27.7)53 (13.2)Dominant(AG + GG) vs. AA2.69 (1.61–4.50)0.0002***2.49 (1.35–4.59)0.003**GG2 (1.9)1 (0.3)RecessiveGG vs. (AA + AG)8.04 (0.72–89.57)0.0913.98 (0.85–228.81)0.06AllelesAllelesA170 (84.2)743 (92.6)A–G32 (15.8)55 (6.9)G2.54 (1.55–4.15)0.00022***––RAD52-rs3748523 C > GCC54 (53.5)269 (67.1)AdditiveCC vs. CG vs. GG1.69 (1.17–2.47)0.006**1.83 (1.15–2.92)0.016*CG41 (40.6)122 (30.4)Dominant(CG + GG) vs. CC1.77 (1.14–2.76)0.01*1.73 (1.03–2.92)0.04*GG6 (5.9)10 (2.5)RecessiveGG vs. (CC + CG)2.47 (0.88–6.96)0.095.39 (1.35–21.54)0.02*AllelesAllelesC149 (73.8)660 (82.3)C–G53 (26.2)142 (17.7)G1.65 (1.13–2.41)0.01*––PMS1-rs5742926 G > TGG94 (93.1)346 (86.3)AdditiveGG vs. GT0.51 (0.22–1.15)0.100.52 (0.21–1.31)0.17GT7 (6.9)51 (12.7)Dominant–––––TT0 [0]0 [0]Recessive–––––AllelesAllelesG195 (96.5)743 (92.65)T–T07 (3.5)51 (6.4)G1.91 (0.82–4.69)0.13––Pearson’s chi-square test was done to determine allelic association with lung cancer, and multivariate logistic regression was done in additive, dominant and recessive models to ascertain genotypic association with lung cancer. aUnadjusted association with crude odds ratio and $95\%$ confidence interval and p-value. bAdjusted for age, sex, pack-years, alcohol consumption, tobacco chewing, betel quid chewing, wood smoke, coal smoke, asbestos, and pesticide exposures; CI: Confidence interval, OR: Odds ratio, Significance levels: $p \leq 0.001$ ‘***,’ 0.01 ‘**,’ 0.05 ‘*.’ n = number of cases and controls. Significant values are in bold and italics.
Unadjusted logistic regression revealed strong association of rs3764821 of ALDH3B1 (G vs. A: OR = 2.64, $95\%$ CI = 1.63–4.29, $$p \leq 0.00009$$***) and rs3748523 of RAD52 (G vs. C: OR = 1.69, $95\%$ CI = 1.17–2.47, $$p \leq 0.006$$**) with lung cancer in additive model. In the dominant model, association with lung cancer was found for both rs3764821 (AG + GG vs. AA: OR = 2.69, $95\%$ CI = 1.61–4.50, $$p \leq 0.0002$$***) and rs3748523 (CG + GG vs. CC: OR = 1.77, $95\%$ CI = 1.14–2.76, $$p \leq 0.01$$*). The rSNP, rs5742926 of PMS1, has no association with lung cancer (Table 3).
Further, covariate-adjusted logistic regression revealed an association of rs3764821 of ALDH3B1 in both additive (G vs. A: OR = 2.51, $95\%$ CI = 1.42–4.67, $$p \leq 0.002$$**) and dominant (AG + GG vs. AA: OR = 2.49, $95\%$ CI = 1.35–4.59, $$p \leq 0.003$$**) models. The rSNP, rs3748523 of RAD52 also revealed a significant association with lung cancer in additive (G vs. C: OR = 1.83, $95\%$ CI = 1.15–2.92, $$p \leq 0.016$$*), dominant (CG + GG vs. CC: OR = 1.73, $95\%$ CI = 1.03–2.92, $$p \leq 0.04$$*) and recessive (GG vs. CC + CG: OR = 5.39, $95\%$ CI = 1.35–21.54, $$p \leq 0.02$$*) effect models (Table 3).
## Association of the polymorphic regulatory variants with clinicopathological features of lung cancer
We found a significant association of rs3764821 with adenocarcinoma (OR = 2.79, $95\%$ CI = 1.03–7.53, $$p \leq 0.043$$) and SCLC (OR = 5.95, $95\%$ CI = 1.65–21.47, $$p \leq 0.007$$) adjusted for age, sex, and pack-years of smoking. Similarly, rs3748523 was associated with squamous cell carcinoma (OR = 2.32, $95\%$ CI = 1.01–5.34, $$p \leq 0.046$$) adjusted for age, sex, and pack-years of smoking. The Association of the variants with different TNM stages and distant metastases in additive and dominant models is summarized in the supplemental material (Supplementary Material, Table S23).
## Effect of tobacco smoking on the association of the polymorphic regulatory variants with lung cancer
The sub-group analysis stratified by pack-years revealed a significant association of rs3764821 of ALDH3B1 in both low pack-years (< 47 py) (OR = 2.58, $95\%$ CI = 1.13–5.88, $$p \leq 0.024$$*) and high pack-years (≥ 47 mean py) (OR = 2.73, $95\%$ CI = 1.49–5.01, $$p \leq 0.0012$$**) subgroups with risk of lung cancer in the additive model. The rSNP, rs3748523 of RAD52, showed significant association only in low pack-years (< 47 mean py) (OR: 1.92, $95\%$ CI = 1.20–3.06, $$p \leq 0.0062$$*) subgroup in the additive model. None of the rSNPs was found to show any significant ($p \leq 0.05$) effect modification on lung cancer risk based on smoking (Supplementary Material, Table S23). None of the other covariates revealed any significant effect on the association of the polymorphic variants with lung cancer (Supplementary Material, Table S24).
## The combined effect of the polymorphic regulatory variants on lung cancer risk
The association between lung cancer and possible combinations of rs3764821 and rs3748523 was assessed by genotype-genotype combination analysis. Interestingly, we found a significant association between the heterozygous genotypes of rs3764821 and rs3748523 (AG + CG: OR = 2.79, $95\%$ CI = 1.14–6.47, $$p \leq 0.013$$) with lung cancer risk (Supplementary Material, Table S25).
## Association of the polymorphic regulatory variants and the overall survival (OS) of lung cancer patients
We performed a survival analysis for 96 lung cancer patients (Table 4) and assessed the association between overall survival (OS) and the variants rs3764821 and rs3748523, using a univariate analysis expressed in Kaplan–Meier (KM) plots and log-rank test. In addition, we followed a multivariate Cox regression model to adjust various covariates like age, sex, pack-years of smoking, histological subtypes, and TNM stage (Table 4). In this subset of lung cancer patients, the genotypic distribution of rs3764821 (χ2 = 0.24; df = 2; $$p \leq 0.89$$) and rs3748523 (χ2 = 0.47; df = 2; $$p \leq 0.79$$) was in HWE.Table 4Relationship of the regulatory polymorphisms with the overall survival (OS) of lung cancer patients, its subtypes, and TNM stages. Regulatory polymorphismsGenotypesDeadAliveMedian OS (Months)Crude HR ($95\%$ CI)Log-rank p-value*Adjusted HR# ($95\%$ CI)p-value*[A] Overall Lung Cancer ALDH3B1-rs3764821A > GAA3631241 (Reference)AG + GG191092.07 (1.13–3.79)0.02*2.12 (1.16–3.89)0.015* RAD52-rs3748523C > GCC2531241 (Reference)CG + GG291172.19 (1.25–3.87)0.004**2.32 (1.30–4.12)0.004**[B] Adenocarcinoma ALDH3B1-rs3764821A > GAA1814241 (Reference)AG + GG1068.92.18 (0.93–5.15)0.0742.35 (0.99–5.59)0.053 RAD52-rs3748523C > GCC1514241 (Reference)CG + GG136121.47 (0.67–3.21)0.331.48 (0.68–3.22)0.33[C] Squamous Carcinoma ALDH3B1-rs3764821A > GAA1616241 (Reference)AG + GG4381.33 (0.43–4.16)0.61.25 (0.39–3.97)0.71 RAD52-rs3748523C > GCC614241 (Reference)CG + GG14265.25 (1.69–16.21)0.001**5.64 (1.76–18.1)0.003**[D] SCLC ALDH3B1-rs3764821A > GAA13271 (Reference)AG + GG529The sample size is insufficient RAD52-rs3748523C > GCC43111 (Reference)CG + GG2390.94 (0.15–5.66)0.941.10 (0.17–7.17)0.92[E] Stage I + II (Early Stages) ALDH3B1-rs3764821A > GAA3871 (Reference)AG + GG134.510.06 (1.04–96.66)0.01*2.78 (0.08–99,500,000)0.14 RAD52-rs3748523C > GCC28121 (Reference)CG + GG416.55.73 (0.58–56.38)0.09219.87 (0.19–246,200)0.83[F] Stage III + IV (Late Stages) ALDH3B1-rs3764821A > GAA3223131 (Reference)AG + GG1610111.64 (0.84–3.18)0.11.65 (0.84–3.85)0.14 RAD52-rs3748523C > GCC232391 (Reference)CG + GG241072.07 (1.13–3.79)0.02*2.32 (1.24–4.31)0.008**p-value < 0.05*, 0.01**, 0.001***. Hazard ratios, $95\%$ CI, and their corresponding p-values were calculated by Kaplan–Meier survival analysis after adjusting for remission and survival in months, and #adjusted hazard ratios, $95\%$ CIs and their corresponding p-values were calculated by Cox regression models adjusted for age, sex, and pack-years of smoking. Significant values are in bold and italics.
Individuals with the combined heterozygous and homozygous risk genotypes of both variants have a median survival time (MST) of 7 months compared to 9 months for the wild-type genotypes. We found a significant association of rs3764821 (AA vs. AG + GG: hazard ratio [HR] = 2.07; $95\%$ CI = 1.13–3.79; log-rank $$p \leq 0.02$$) and rs3748523 (CC vs. CG + GG: hazard ratio [HR] = 2.19; $95\%$ CI = 1.25–3.87; log-rank $$p \leq 0.004$$) (Fig. 3; Table 4) with the low OS of lung cancer patients using KM survival analysis and univariate Cox regression model. Multivariate Cox regression analysis revealed a lower OS in lung cancer patients for rs3764821 (AA vs. AG + GG: HR = 2.12; $95\%$ C.I. = 1.16–3.89; $$p \leq 0.015$$) and rs3748523 (CC vs. CG + GG: hazard ratio [HR] = 2.32; $95\%$ CI = 1.30–4.12; $$p \leq 0.004$$) adjusted for age, sex and pack year of smoking (Table 4). Figure 3Kaplan–Meier curves depict the association between overall survival in lung cancer patients and the cis-regulatory polymorphic variants in the eastern Indian population. It shows significantly lower overall survival in lung cancer patients with (A) rs3764821 (ALDH3B1); a combination of heterozygous and homozygous variant genotypes (AG + GG), and (B) rs3748523 (RAD52); the combination of heterozygous and homozygous variant genotypes (CG + GG). Significance at log-rank $p \leq 0.05$*.
The effect of the rSNPs on the OS of patients with a specific subtype of lung cancer was also evaluated by a multivariate Cox regression model adjusted for age, sex, and pack-years of smoking. The variant rs3748523 was significantly associated with a lower OS of patients with squamous cell carcinoma ($$n = 36$$; CC vs. CG + GG: hazard ratio [HR] = 5.64; $95\%$ CI = 1.76–18.1; $$p \leq 0.003$$) adjusted for age, sex, and pack-years of smoking. No significant association was observed for the other two lung cancer histological subtypes. The variant rs3748523 (RAD52) was found to be significantly associated with lower OS (CC vs. CG + GG: hazard ratio [HR] = 2.32; $95\%$ CI = 1.24–4.31; $$p \leq 0.008$$) in lung cancer patients of later stages (stage III and stage IV) adjusted for age, sex and pack-years of smoking (Table 4).
## Effect of polymorphic regulatory variants on the overall survival of lung cancer patients stratified by chemotherapy regimens with different drug combinations
The association of the polymorphic regulatory variants rs3764821 and rs3748523 with the OS of lung cancer patients stratified by chemotherapy regimens with different drug combinations in the dominant model is summarized in a table (Table 5). For some non-responders to the first chemotherapy regimen, treatment was extended up to three chemotherapy regimens with different combinations of drugs. Table 5Association of regulatory polymorphisms and overall survival according to the chemotherapy regimen. Regulatory polymorphismsGenotypesCrude HR ($95\%$ CI)Log-rank pAdjusted HR† ($95\%$ CI)p-value†Regimen 1—Docetaxel cis/carboplatin ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG2.51 (0.35–18.17)0.326.29 (0.18–3762.41)0.19 RAD52-rs3748523C > GCC1 (Reference)CG + GG0.47 (0.04–5.16)0.51.55 (0.03–90.37)0.83Regimen 1—Pemetrexed cis/carboplatin ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG1.28 (0.39–4.15)0.71.8 (0.39–8.19)0.49 RAD52-rs3748523C > GCC1 (Reference)CG + GG2.42 (0.78–7.47)0.11.33 (0.29–5.89)0.71Regimen 1– Paclitaxel cis/carboplatin ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG2.67 (0.93–8.39)0.063.62 (1.03–12.71)0.044* RAD52-rs3748523C > GCC1 (Reference)CG + GG3.19 (1.10–9.27)0.02*1.95 (0.58–6.58)0.28Regimen 1– Nanopaclitaxel cis/carboplatin ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG1.15 (0.19–6.98)0.93.37 (0.24–47.98)0.37 RAD52-rs3748523C > GCC1 (Reference)CG + GG7.95 (0.89–71.16)0.03*11.32 (0.18–698.64)0.25Regimen 1—Etoposide cis/carboplatin ALDH3B1-rs3764821A > GAA1 (Reference)AG + GGThe sample size is insufficient RAD52-rs3748523C > GCC1 (Reference)CG + GG1.01 (0.20–5.08)0.990.78 (0.10–5.97)0.82Combination Drug Regimen: Docetaxel cis/carboplatin (2nd) * Nanopaclitaxel cis/carboplatin (1st) ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG1.15 (0.19–6.98)0.92.05 (0.25–16.55)0.5 RAD52-rs3748523C > GCC1 (Reference)CG + GG7.15 (0.74–69.03)0.0516.75 (0.38–734.18)0.14Combination Drug Regimen: Nanopaclitaxel cis/carboplatin (2nd) * Pemetrexed cis/carboplatin (1st) ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG1.40 (0.38–5.10)0.61.39 (0.39–5.31)0.62 RAD52-rs3748523C > GCC1 (Reference)CG + GG2.27 (0.75–6.84)0.12.35 (0.75–7.37)0.14Combination Drug Regimen: Pemetrexed cis/carboplatin (2nd) * Paclitaxel cis/carboplatin (1st) ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG2.47 (0.78–7.84)0.13.25 (0.88–12.06)0.08 RAD52-rs3748523C > GCC1 (Reference)CG + GG2.97 (1.01–8.76)0.051.75 (0.51–6.07)0.38Combination Drug Regimen: Paclitaxel cis/carboplatin (2nd) * Pemetrexed cis/carboplatin (1st) ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG1.18 (0.32–4.28)0.81.21 (0.33–4.48)0.78 RAD52-rs3748523C > GCC1 (Reference)CG + GG3.10 (0.95–10.16)0.053.41 (0.99–11.81)0.05Combination Drug Regimen: Gemcitabine cis/carboplatin (2nd) * Paclitaxel cis/carboplatin (1st) ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG3.02 (1.09–8.39)0.03*4.16 (1.34–12.89)0.014* RAD52-rs3748523C > GCC1 (Reference)CG + GG2.79 (1.09–7.15)0.03*2.12 (0.74–6.11)0.16Combination Drug Regimen: Gemcitabine cis/carboplatin (2nd) * Pemetrexed cis/carboplatin (1st) ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG1.89 (0.74–4.85)0.21.86 (0.70–4.91)0.21 RAD52-rs3748523C > GCC1 (Reference)CG + GG2.21 (0.88–5.51)0.082.50 (0.94–6.68)0.07Combination Drug Regimen: Gemcitabine cis/carboplatin (2nd) * Nanopaclitaxel cis/carboplatin (1st) ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG3.03 (0.78–11.8)0.093.63 (0.93–14.08)0.06 RAD52-rs3748523C > GCC1 (Reference)CG + GG3.28 (0.92–11.67)0.053.38 (0.51–22.24)0.21Combination Drug Regimen: Eribulin cis/carboplatin (3rd) * Nanopaclitaxel cis/carboplatin (2nd) * Pemetrexed cis/carboplatin (1st) ALDH3B1-rs3764821A > GAA1 (Reference)AG + GG1.40 (0.38–5.10)0.61.39 (0.37–5.31)0.62 RAD52-rs3748523C > GCC1 (Reference)CG + GG2.26 (0.75–6.84)0.12.35 (0.75–7.37)0.14p-value < 0.05*, 0.01**, 0.001***. Hazard ratios, $95\%$ CI, and their corresponding p-values were calculated by Kaplan–Meier survival analysis after adjusting for remission and survival in months, and #adjusted hazard ratios, $95\%$ CIs and their corresponding p-values were calculated by Cox regression models adjusted for age, sex, and pack-years of smoking. Significant values are in bold and italics.
For the variant rs3764821 (ALDH3B1), lung cancer patient treated with paclitaxel-cis/carboplatin showed a significantly low OS (AA vs. AG + GG: hazard ratio [HR] = 3.62, $95\%$ CI = 1.03–12.71, $$p \leq 0.044$$) in our study population, adjusted for age, sex and pack-years of smoking using a multivariate Cox regression model. Lung cancer patients treated with gemcitabine-cis/carboplatin in the second chemotherapy regimen and paclitaxel-cis/carboplatin (AA vs. AG + GG: hazard ratio [HR] = 4.16, $95\%$ CI = 1.34–12.89, $$p \leq 0.014$$) in the first chemotherapy regimen showed a significant lower OS compared to the wild type, adjusted for age, sex and pack-years of smoking using a multivariate Cox regression model (Table 5). Using a KM survival analysis, lung cancer patients treated with gemcitabine-cis/carboplatin in the second chemotherapy regimen and paclitaxel-cis/carboplatin (AA vs. AG + GG: hazard ratio [HR] = 3.02, $95\%$ CI = 1.09–8.39, log-rank $$p \leq 0.03$$) in the first chemotherapy regimen showed a significant lower OS compared to the wild type (Fig. 4, Table 5).Figure 4Kaplan–Meier curves depict the association between the polymorphic cis-regulatory variants and overall survival in lung cancer patients treated with different chemotherapy regimens in the eastern Indian population. It shows significantly lower overall survival in lung cancer patients with (A) rs3764821 (ALDH3B1); treated with gemcitabine-cis/carboplatin in the second regimen and paclitaxel-cis/carboplatin in the first regimen, and (B) rs3748523 (RAD52); treated with paclitaxel-cis/carboplatin in the first regimen. Significance at log-rank $p \leq 0.05$*.
In the case of rs3748523 (RAD52), lung cancer patients treated with an etoposide-cis/carboplatin regimen showed a higher overall survival (OS) in the study population (CC vs. CG + GG: hazard ratio [HR] = 0.78, $95\%$ CI = 0.10–5.9, $$p \leq 0.82$$) adjusted for age, sex and pack-years of smoking using a multivariate Cox regression model but is not statistically significant. However, lung cancer patients treated with paclitaxel-cis/carboplatin showed significantly lower OS (CC vs. CG + GG: hazard ratio [HR] = 2.79, $95\%$ CI = 1.09–7.15, log-rank $$p \leq 0.03$$) (Fig. 4) in our study population using KM survival analysis. Patients treated with gemcitabine-cis/carboplatin in the second regimen and pemetrexed-cis/carboplatin (CC vs. CG + GG: hazard ratio [HR] = 3.83, $95\%$ CI = 1.39–10.53, $$p \leq 0.01$$) or nanopaclitaxel-cis/carboplatin (CC vs. CG + GG: hazard ratio [HR] = 8.66, $95\%$ CI = 1.33–56.17, $$p \leq 0.02$$) in the first regimen showed a significant lower OS, adjusted for age, sex and pack-years of smoking (Table 5). We also compared the differences in the responses to various drugs in the presence and absence of rs3764821 (ALDH3B1) and rs3748523 (RAD52) for first-line chemotherapy only. We found that the presence of the variants rs3764821 and rs3748523 showed poor response to Pemetrexed-cis/carboplatin, Etoposide-cis/carboplatin, Paclitaxel-cis/carboplatin and Nanopaclitaxel-cis/carboplatin (Supplementary material Table S26).
Furthermore, we observed a poor response and decreased OS of the lung cancer patients with the variants rs3764821 (ALDH3B1) and rs3748523 (RAD52) for both first- and second-line chemotherapy. Thus, it reflects the sample population as poor or non-responders to the standard chemotherapy drugs administered to treat advanced lung cancer. In addition, we found rs3748523 (RAD52) to decrease OS significantly and showed poor response to first-line Pemetrexed-cis/carboplatin chemotherapy (HR: 3.48, $95\%$ CI = 1.06–11.4, $$p \leq 0.039$$*) (Supplementary material Table S27).
## Discussion
Although several studies have implicated many genes and variants with lung carcinogenesis in tobacco smokers, the precise heritable genetic risk signature(s) or prognostic marker(s) is still obscure. *Differential* gene expression between lung cancer patients with a smoking history and healthy smokers is considered a significant player in lung cancer pathogenesis, particularly for xenobiotic metabolism and DNA repair genes. These two pathways act synergistically to determine the level of carcinogenic load within the lung cells and the capacity to repair DNA damage induced by such carcinogens. We hypothesized that the variants in certain genomic elements regulate such differential gene expression between patients and controls. Therefore, based on this hypothesis, we used the ENCODE data to curate the gene expression-correlated DHS. Such candidate genomic elements could have a cis-regulatory role in gene transcription. The variations within such genomic elements could be the potential modulators of gene expression and need to be characterized to understand the gene regulatory network conferring individual susceptibility to lung carcinogenesis among smokers.
We have designed a workflow to identify, annotate and prioritize such variants within the DHS of genes as risk signatures of lung cancer. We have integrated and interpreted various omics datasets of ENCODE, GTEx, Roadmap Epigenomics, and TCGA datasets through specific web tools to identify, annotate, and prioritize such genetic variants. Out of the 2984 DHS-SNVs in our candidate gene set, only 22 were cis-regulatory in function in lung tissue by integrating and interpreting various omics datasets of ENCODE, GTEx, Roadmap Epigenomics, and TCGA. Transcriptional regulation by genomic elements is tissue-specific36,65 and follows a distinctive pattern across the tissues with some conserved elements, while the rest are unique to the cell type. Our study has distinctively identified lung tissue-specific genetic loci responsible for genotype-specific regulation of candidate xenobiotic metabolism and DNA repair gene expression through the analysis of cis-eQTL mapped data. The categorization of rSNPs by the epigenomic signatures into functional gene regulatory chromatin domains provided an insight into the basis of cis-regulatory mechanisms of the genomic elements on their target gene expression. Out of our 22 prioritized cis-eQTLs, we found only 4 significant cis-QTLs in lung cancer from the analysis of TCGA lung cancer datasets harbored in the web tool GEPIA. It further affirmed our workflow’s predictive accuracy and precision as the predicted risk alleles through the pipeline match the reported risk alleles in lung cancer.
Both genome-wide and candidate association studies often reveal unexplained genetic associations with disease/trait, especially for the intronic and intergenic SNPs. We observed nominal associations ($p \leq 0.05$) of three rSNVs, such as rs35568625 (MAFG), rs3760091 (SULT1A2), and rs743590 (SULT1A1), with lung cancer in 1655 cases and 450,609 controls of all white British origin samples from the UK Biobank GWAS dataset C34 Malignant neoplasm of bronchus and lung, hosted by the Gene Atlas webserver (http://geneatlas.roslin.ed.ac.uk/search/), which further strengthened our variant prioritization procedure. Interestingly, the predicted risk alleles of these three rSNVs match the GWAS data, which strengthens our hypothesis and prioritization procedure. However, in an attempt to independently replicate the three rSNVs rs3764821 (ALDH3B1), rs3748523 (RAD52), and rs5742926 (PMS1) from our case–control association study, we failed to find any significant association of the variants with lung cancer in the white British population, which differs from our finding in the east Indian population. The reason for this could be the differences in the population-specific allelic distribution of the variants and the fact that the current study was focused only on smokers. In addition, most of the available lung cancer GWAS datasets represent the Caucasian and East Asian populations, and no such dataset is available on the Indian population.
With the advent of ENCODE and related datasets, scientists are trying to assess if these innocuous loci have any cis-regulatory role on their target genes or are in LD with a cis-regulatory variant that has not been included or filtered out from the specific association study. Detailed analysis indicates that by being in LD, 11 cancer-associated SNPs (5 LD SNPs in lung cancer and other types of cancer, 6 LD SNPs exclusively for different kinds of cancer) might act as surrogates for 8 prioritized rSNVs (3 rSNVs common in lung cancer and non-lung cancer dataset, 1 only in lung cancer dataset and 3 in other cancers). Thus, the finding strengthened our workflow where 5 prioritized cis-regulatory variants are in strong LD with 5 reported lung cancer-associated SNPs. Therefore, it provides transitive evidence of association of the prioritized rSNVs with lung cancer by being in strong LD with reported associations. Again, our revelation of the combination of damaging coding alleles with regulatory risk alleles could result in a significant loss of gene function and thereby have a higher risk modulatory effect in lung carcinogenesis. This could lead to a practical interpretation of the combinatorial role of alleles in a personalized genome approach40 for designing therapeutic strategies with precision medicine.
As revealed from our study, the expanded interactome analysis showed strong associations between our prioritized protein-coding genes that provide insight into their probable synergistic influence in mitigating tobacco smoke-induced damage. Interaction of critical proteins, such as TP53, has been found to interact with the NFE2L2 pathway indicating a vital relationship between the xenobiotic metabolism and cellular transformation pathways that paved the way for future investigations on cytoprotection and tumorigenesis. The cross-talk of the detoxification and DNA repair pathway with cytoskeletal remodeling, metastasis, apoptosis, and cell cycle regulatory pathways provides an insight into the carcinogen-induced gene regulatory mechanisms in lung carcinogenesis among smokers.
The prioritized genes have diverse functions related to the metabolism of tobacco smoke components and repairing oxidative DNA lesions induced by smoke carcinogens that form the basis of risk allele determination. We have summarized the probable impact of the risk alleles on the gene function contributing toward lung carcinogenesis among smokers (Supplementary Material, Table S28).
Earlier genome-wide association studies (GWAS) have shown rs10849605 of RAD52 significantly associated with an increased risk of lung cancer66. Our data found a significant association of rs3748523 of RAD52 with an increased risk of lung cancer, implicating collinearity in the studies for gene function in lung cancer. This is the first report on the regulatory polymorphism of ALDH3B1, significantly altering lung cancer risk by regulating the detoxification potential of the enzyme. However, the PMS1 gene shows an association with lung cancer63 in Caucasians. However, the lack of association of rs5742926 of PMS1 in our study could be attributed to the sample size due to low minor allele frequency in the eastern Indian population. It is worth mentioning that rs3748523 of the RAD52 gene is associated with lung cancer in low smokers of a young age. This indicates the potential role of the variant in reducing the expression of the DNA repair gene, conferring the early risk of lung cancer in individuals with low to medium smoking intensity. Earlier reports have indicated an association between tobacco and betel quid chewing and lung cancer67,68.
Interestingly, rs3764821 of ALDH3B1 and rs3748523 of RAD52 were associated with lung cancer in tobacco and betel quid chewers. The risk genotype of both polymorphisms would cause ineffective metabolism of the xenobiotics from tobacco and betel quid and sub-optimal DNA repair of DNA damages caused by the constant xenobiotic load. Thus, the combinatorial inheritance of risk alleles of the SNPs would confer a higher risk of developing lung cancer, and stratifying the genotypes based on tumor subtypes and TNM staging improved risk assessment. Prediction of the risk for specific tumor subtypes and cancer stages leads to the design of targeted early detection and prevention strategies. Moreover, identifying histotype-associated SNPs may define the mechanism underlying the unknown origins of morphological variations and contribute to a personalized treatment approach for subtype-specific lung cancer cases69.
In the present study, we have also evaluated the role of two lung cancer-associated regulatory polymorphic variants in the survival of lung cancer patients treated with platinum-based chemotherapy. None of the variants showed any improvement in the overall survival of patients post-treatment with standard platinum-based chemotherapy. However, the risk alleles of the polymorphic variants were found to significantly lower the overall survival of lung cancer patients post platinum-based chemotherapy, adjusted for covariates like age, sex, and pack-years of smoking. We found a significant reduction in OS in patients with the risk allele of rs3764821 (ALDH3B1), treated with gemcitabine-cis/carboplatin as a second line of treatment after paclitaxel-cis/carboplatin. This could be due to the lower expression of ALDH3B1 that causes an inadequate response to platinum-based chemotherapy leading to higher systemic toxicity and increased mortality among the advanced-stage (IIIB and IV) NSCLC patients in our sample population. To the best of our knowledge, this is the first study that reports the role of cis-regulatory polymorphic variants in modulating the overall survival in eastern India lung cancer patients post-treatment with standard chemotherapy. Therefore, ALDH3B1 and RAD52 play a pivotal role in tobacco smoke-induced lung carcinogenesis and platinum-based standard chemotherapy, which could be critical prognostic markers of the disease and predictors of chemotherapy responses. Aldehyde dehydrogenase, including ALDH3B1, is involved in the detoxification and clearance of chemotherapeutic drugs, leading to chemotherapy resistance70,71. Similarly, the RAD52 is a DNA-binding protein that repairs single-strand DNA breaks introduced by the genotoxic compounds in tobacco smoke72,73. A lower expression of both genes would imply impaired detoxification of tobacco smoke metabolites and the repair of DNA damage introduced by the same tobacco smoke metabolites, influencing overall survival and the efficacy of chemotherapy regimens with different drug combinations.
A limitation of this approach is the difficulty of getting the necessary sample sizes, given the relative rarity of many such histological subtypes or the lack of proper clinical records. However, our data mining approach with prior knowledge of the disease etiology helped prioritize the most relevant SNVs for replication, even in a small sample size. Furthermore, due to the lack of high-resolution HiC and ChIA-PET datasets on lung tissue, a more detailed analysis of the physical interaction of cis-elements, particularly promoter-enhancer/repressors, could not be done.
The co-occurrence of risk alleles and estimation of unweighted genetic risk scores (uGRS) of 22 prioritized rSNPs provided insight into individual and population-specific tobacco-dependent lung cancer. The preponderance of the risk alleles stratified by sub-populations of 1000 *Genome data* predicted the Gambians in Western Gambia (GWD) to be at risk while the Americans of African Ancestry in South West USA (ASW) to be at least risk. Traditionally, insufficient epidemiological studies on lung cancer incidences in the African population led to inconclusive risk assessment a priori. A recent development in maintaining nationwide cancer registries in different countries of the continent increased the coverage to $13\%$ of the population, which is a deviation from the earlier notion of Africans being the most protected population against tobacco smoking-related lung cancer. The increase in lung cancer incidences throughout the African continent, mainly in West Gambia and the sub-Saharan region, could be attributed to the increase in tobacco smoking and the aging of the predisposed population74. However, on stratification based on the larger geospatial population of 1000 Genome data, Europeans were at high risk of tobacco smoke-dependent lung carcinogenesis, substantiated by epidemiological reports74. Lung cancer rates showed a 20-fold variation stratified by region, which predominantly reflects the decrease in patterns of tobacco exposure, including intensity and duration of smoking, type of cigarettes, and degree of inhalation in the developed world. A diminution in smoking prevalence among men caused a decline in lung cancer rates in several high-income countries where smoking was first established, including the United Kingdom, Finland, the United States, the Netherlands, Australia, New Zealand, Singapore, Germany, and Uruguay. Recent reports in 26 European countries revealed a decline in age-standardized (35–64 years) incidences of lung cancer, with Bulgaria as an exception55. Therefore, being susceptible to tobacco-dependent lung cancer, the Europeans probably managed to reduce the disease load by changing their lifestyle habits75. All of these showcase the importance of this work towards identifying risk populations and designing effective tobacco control policies to reduce lung cancer incidences. Epidemiological reports76,77 corroborate our finding that Latinos/Non-white Hispanics are at the lowest risk of tobacco smoke-dependent lung cancer among all the other populations of the 1000 Genome data, followed by the Africans. Despite high smoking rates, lung cancer incidences are pretty low in the Central and South American Latinos/ Non-white Hispanic population76,77. In future studies, we would try to corroborate the weighted genetic risk score of the variants with the epidemiological data of lung cancer from the global lung cancer datasets.
The study has implied a pathway-based approach to identify 22 cis-regulatory variants of 14 genes (XMGs and DRGs) through integrating and interpreting various freely available omics data. The cross-validation of the statistical association of the identified rSNVs with lung cancer by their LD-SNPs and the precise match of the risk alleles of the cis-eQTLs in lung cancer to normal tissue shows the success of our prioritization pipeline. The case–control replication following the in silico prioritization provides population-specific risk markers of lung carcinogenesis. Incorporating more genes of critical lung cancer regulatory pathways would enable us to construct a comprehensive, personalized genomic map of individuals across different populations for assessing their lung cancer risk profiles to design personalized therapy based on precision medicine and formulating effective tobacco control policies and genetic counseling for the containment of the disease. We opine that the method followed in this study for identifying cis-regulatory risk markers of lung carcinogenesis among smokers could be implied to other complex diseases or traits.
## Supplementary Information
Supplementary Figures. Supplementary Tables. The online version contains supplementary material available at 10.1038/s41598-023-30962-9.
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|
---
title: Genetic association of the rs17782313 polymorphism with antipsychotic-induced
weight gain
authors:
- Korbinian Felix Schreyer
- Stefan Leucht
- Stephan Heres
- Werner Steimer
journal: Psychopharmacology
year: 2023
pmcid: PMC10006246
doi: 10.1007/s00213-023-06331-9
license: CC BY 4.0
---
# Genetic association of the rs17782313 polymorphism with antipsychotic-induced weight gain
## Abstract
### Rationale
Weight gain is a frequent side effect of treatment with SGAs (second-generation antipsychotics) and a leading cause for nonadherence. Several candidate genes have been identified that could influence the amount of AIWG (antipsychotic-induced weight gain). The polymorphism rs17782313 near the MC4R (human melanocortin 4 receptor gene) was strongly associated with obesity in a large scale GWAS (genome wide association study), yet previous studies investigating its impact on AIWG did not lead to a definite conclusion regarding its effect. In particular, they were all relatively short and had a naturalistic design.
### Objective
We therefore examined the influence of the rs17782313 polymorphism on SGA-related weight gain.
### Methods
Participants of a multicenter randomized, controlled, double-blind study comparing two treatment strategies in individuals with schizophrenia or schizoaffective disorder were genotyped using a rapid-cycle polymerase chain reaction. Up to 252 individuals completed the first 2 weeks (phase I), 212 the entire 8 weeks (hence ‘completers’). Patients received either amisulpride or olanzapine or both consecutively. Thirty-seven had their first episode. Weight gain occurring in different genotypes was statistically compared and confounding factors were adjusted by stepwise multiple linear regression. A correction for multiple testing was included.
### Results
Within 212 ‘completers’, carriers of the C allele had a higher absolute weight gain than those homozygous for the T allele (2.6 kg vs. 1.2 kg), though this observation was not significant ($$P \leq 0.063$$). In the amisulpride subpopulation, this association appeared stronger and reached significance (2.5 kg vs. 0.7 kg, $$P \leq 0.043$$), though failed to remain significant after correction for multiple testing. A stepwise multiple linear regression showed a significant association in both the whole study population ($P \leq 0.001$) and the amisulpride subpopulation ($P \leq 0.001$).
### Conclusion
Our results indicate that the rs17782313 polymorphism might influence antipsychotic-induced weight gain and therefore confirm some of the earlier conclusions.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00213-023-06331-9.
## Introduction
Schizophrenia is a chronic, psychotic mental disease that affects approximately one percent of people. The time of onset is typically in late adolescence or early adulthood and regularly continues through the whole life (Freedman 2003). It presents itself as a spectrum of disorders with positive, negative, cognitive, and affective symptoms. Its most common form, paranoid schizophrenia, is often characterized by paranoia and auditory delusions (Insel 2010).
Pharmacological treatment was introduced more than half a century ago with the discovery of D2 (dopamine 2 receptor)-antagonists such as chlorpromazine and haloperidol. It later shifted to SGAs (second-generation antipsychotics) that owing to their lower affinity to the dopamine receptor and a higher affinity to serotonin and norepinephrine receptors tend to show better improvement in negative symptoms and fewer extrapyramidal side effects. However, this alleged advantage is often bought at the price of metabolic alterations such as weight gain, hyperglycemia, or dyslipidemia (Allison and Casey 2001; Miyamoto et al. 2005), being a leading cause for nonadherence (Lieberman et al. 2005; Weiden et al. 2004). These adverse effects do not equally affect every patient though. While the substance is known to greatly influence the amount of weight gain (clozapine and olanzapine rank among the least favorable), genetic variation has also been suggested to play an important role (Shams and Müller 2014).
One of the leading causative genes regarding weight gain itself is MC4R (human melanocortin receptor gene). As more than 130 MC4R mutations have been detected, of which many lead to a loss of function (Fan and Tao 2009), the common SNP (single nucleotide polymorphism) rs17782313 showed the strongest association with BMI (body mass index) in a large-scale GWAS (genome wide association study) (Loos et al. 2008), which analyzed 16,876 individuals of European descent. The findings were confirmed in 60,352 adults, 5988 children, and 660 German families. The overall combined per-allele effect on BMI was 0.049 (0.037–0.061/Z-score units, $$P \leq 2.8$$ × 1015) in 77,228 genotyped adults.
A GWAS conducted later reported 20 SNPs near the MCR4 that exceeded a certain statistical threshold when assessing weight gain after 12 weeks of antipsychotic treatment in pediatric patients, underlining the possible relevance of mutations near the MC4R in extreme SGA-induced weight gain (Malhotra et al. 2012). The rs17782313 is located in close proximity to one of these 20 SNPs that can be found approximately 190 kilobases downstream from the MC4R, a locus overlapping the region identified by the GWAS from above.
So far, few studies have investigated the influence of the rs17782313 polymorphism on antipsychotic-induced weight gain and produced not entirely consistent results. Czerwensky et al. [ 2013] reported a significant influence of the polymorphism on weight gain in a naturalistic study with 345 patients after 4 weeks of treatment with various second-generation antipsychotics and showed similar results in a first-episode subpopulation and a subpopulation not receiving co-medication known to induce weight gain. Chowdhury et al. [ 2013] analyzed 224 patients that received antipsychotic treatment over up to 6 weeks and could not reveal a significant association, yet their analysis yielded a trend for higher AIWG and the C allele in a European sub-sample treated with clozapine or olanzapine. A meta-analysis published in 2016 only listed those two studies for this particular SNP. The combined effect showed a tendency, though lacked significance (Zhang et al. 2016). Zhang et al. [ 2019] could not find a significant association of the rs17782313 polymorphism and BMI percentage change in neither a total population of 1991 Han Chinese patients treated for schizophrenia for 6 weeks nor in subgroups with drug-naïve or medicated subjects.
After more than a decade since the discovery of rs17782313 as a strong mediator in obesity and nearly 10 years since the first investigation of its influence in AIWG, there remains a need for more studies to clarify the effect it has on SGA-related weight gain and create a basis for future meta-analyses on this matter. Thus, we investigated the influence of rs17782313 on weight gain in 252 patients that took part in a multicenter randomized, controlled, double-blind study comparing two treatment strategies for acute schizophrenia or schizoaffective disorder.
## Study design
Patients aged between 18 and 65 who suffered from schizophrenia, schizoaffective disorder, or schizophreniform disorder were recruited as part of a multi-center study (Heres et al. 2016). At the beginning of the 8-week lasting trial, patients were randomly assigned to two treatment groups, each one receiving amisulpride or olanzapine. After 2 weeks of treatment (resembling phase I), those individuals experiencing a ‘non response’ were randomized again, resulting in either a switch to the other medication or a continuation with the originally assigned drug for another 6 weeks (phase II). All ‘responders’ continued their medication throughout phase II. Therefore, patients received either amisulpride or olanzapine for 8 weeks, or switched after 2 weeks from one to the other. One site was excluded due to misconduct in a later, independent trial. Complete data regarding baseline weight and weight gain and consent to genetic testing was available for 252 subjects for the first 2 weeks (phase I) respectively for the entire trial — due to drop outs — for 212 individuals (hereinafter referred to as completers). No additional antipsychotics, mood stabilizers or recently initiated antidepressants were given. Rescue medication for symptomatic treatment of agitation, sleep disturbances or side effects were benzodiazepines and Z-drugs. The design of the study was double-blind. Neither the treating physicians nor the patients knew which study drug was assigned to them in phase I, nor if they switched to the other substance in phase II or maintained the treatment they started with. Group allocation and distribution of the medication were conducted by the pharmacy of the University of Mainz, which did not take part in conducting the study or in analyzing the results. More details can be found in Heres et al. [ 2016].
## Genotyping
We genotyped the rs17782313 polymorphism, a T to C transition located 188 kb downstream from the MC4R with a minor allele frequency of ~$23.1\%$ in Europeans (Phan et al. 2020). DNA was prepared using the QIAmp® DNA Blood Mini Kit. We performed a rapid-cycle polymerase chain reaction (Czerwensky et al. 2013) on the LightCycler® 2.0 (Roche, Penzberg, Germany) as described elsewhere (Bui and Liu 2009; Popp et al. 2003).
## Statistics
We analyzed weight gain and BMI increase occurring both during the first 2 weeks (phase I) and weight gain during the entire 8 weeks. This was due to the design of the study (the ‘switch’ in some patients’ medication after 2 weeks) and, as expected, discontinuations of some individuals. For both time periods, we at first looked at all patients combined and in a second step at individuals that received only either one of the SGAs. In addition, we analyzed a ‘first episode’ subpopulation with $$n = 37$$ individuals, though baseline height and therefore baseline BMI of one individual was missing. Relative weight gain in % and relative BMI increase in % are mathematically equal, we therefore reported only on the former. To sum up, in the Results section we [1] reported on Hardy-*Weinberg equilibrium* and [2] baseline characteristics for (2a) all taking part in phase I, for (2b) completers of the trial, and (2c) first episode patients. Then we focused on [3] weight gain occurring within the first 2 weeks in (3a) all medication arms, (3b) those treated with amisulpride, (3c) those receiving olanzapine, and (3d) in first episode patients. In [4] we analyzed weight gain in ‘completers’ of the trial (4a) regardless of medication (hence including those having switched), (4b) in individuals receiving amisulpride only, (4c) in patients treated with olanzapine only, and (4d) in first episode patients. Finally, in [5], we corrected our results for multiple comparisons (for method see below).
Statistical analysis was carried out using IBM SPSS Statistics for Windows, version 21.0 (IBM Corp., Armonk, N.Y., USA). We checked raw data for plausibility, then calculated different variables for further analysis, for example for each individual patient i: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{BMI}}_{\mathrm i}\left[\frac{\mathrm{kg}}{\mathrm m^2}\right]=\frac{\mathrm{Baseline}\;{\mathrm{Body}\;\mathrm{Weight}}_{\mathrm i}\;\left[\mathrm{kg}\right]}{{\mathrm{Height}}_{\mathrm i}\;\left[\mathrm m\right]^2}$$\end{document}BMIikgm2=BaselineBodyWeightikgHeightim2 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{Age}}_{\mathrm i}\;=\;\mathrm{Year}\;\mathrm{of}\;{\mathrm{participation}}_{\mathrm i}\;-\;\mathrm{Year}\;\mathrm{of}\;{\mathrm{Birth}}_{\mathrm i}$$\end{document}Agei=Yearofparticipationi-YearofBirthi Two-tailed $$P \leq 0.05$$ were considered to be statistically significant (α = 0.05). To check if the population was in Hardy-Weinberg equilibrium, the chi-square test was used. Reference data was retrieved from the Allele Frequency Aggregator (ALFA) (Phan et al. 2020) — European sample, release version 20201027095038, minor allele frequency MAFc = 0.231114. Expected frequencies nTT, nTC, and nCC were calculated fTT = (1 − MAFc)2 × (nTT + nTC + nCC) etcetera in accordance with Hardy-Weinberg law. Normal distribution was determined graphically and through the Kolmogorov-Smirnov and Shapiro-Wilk test (see Supplementary Table 1). Absolute weight gain, relative weight gain, absolute increase in BMI, and relative increase in BMI were compared. In case of normal distribution, we used two-tailed T-test (or Welch’s T-test if equal variances were rejected in Levene’s test) for comparison of two groups and analysis of variance for three groups. If normal distribution was violated, we used the Mann-Whitney U test for pairwise comparisons and the Kruskal-Wallis test for groupwise comparisons (see Supplementary Table 1 for normal distribution of variables). To adjust for confounding effects on relative weight gain in % (as done in Czerwensky et al. [ 2013]) and to calculate combined effects, stepwise multiple linear regression analyses were calculated. Possible covariates were entered stepwise into the model and parameters were included when the $95\%$ confidence interval of their standard error did not include zero and the objective function value fell by more than 3.94, equaling a significance (probability) of the F value of 0.05. Although our approach was of exploratory nature, a correction for multiple testing was included to evaluate robustness of statistical findings. Our main research question was the influence of the rs17782313 on AIWG. P-values of baseline characteristics were not corrected as we think they were not of interest in this analysis and can be seen as a mean to find confounding variables. As weight gain and BMI gain can be interpreted as two sides of the same coin and specific differences between them were not our focus, we corrected P-values of weight gain within 2 weeks and weight gain within 8 weeks for both the comparison of TT vs TC vs CC and TT vs Cx in all medication groups, the first episode subpopulation, and the MC4R genotype as a factor in the multiple linear regression analyses for multiple comparison by Holm’s procedure (Holm 1979; Ludbrook 1998). Thus, $m = 25$ P-values were arranged in ascending order for all hypotheses of interest. All Pk, while k was the P-value’s index and α = 0.05, that satisfied \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${P}_k<\frac{\alpha }{m+1-k}$$\end{document}Pk<αm+1-k were considered significant (see Supplementary Table 2).
## Hardy-Weinberg equilibrium
For phase I, we included 252 inpatients that received either amisulpride or olanzapine. For the entire trial that lasted 8 weeks, we included 212 individuals (‘completers’) that were treated with either only olanzapine or amisulpride or in case of a switch with both drugs consecutively. We analyzed the influence of the rs17782313 polymorphism on absolute and relative gain in body weight and absolute BMI increase. The demographic and clinical data can be found in Table 1. The rs17782313 genotype frequencies were in all cases determined successfully. The distribution of the observed genotype was in Hardy-*Weinberg equilibrium* for those in phase I (regardless of medication: χ = 0.604, $$P \leq 0.739$$; amisulpride only: χ = 1.626, $$P \leq 0.443$$; olanzapine only: χ = 0.330; $$P \leq 0.848$$; first episode: χ = 5.628; $$P \leq 0.060$$, one expected cell frequency was below 5) and those completing the entire 8 weeks (regardless of medication: χ = 0.593, $$P \leq 0.743$$; amisulpride only: χ = 1.100, $$P \leq 0.577$$, one expected cell frequency was below 5; olanzapine only: χ = 4.287, $$P \leq 0.117$$, one expected cell frequency was below 5; first episode: χ = 3.334; $$P \leq 0.189$$, one expected cell frequency was below 5).Table 1Baseline characteristicsMedicationBoth antipsychoticsAmisulpride onlyOlanzapine onlyPhase I only/Entire trial (completers)Participants (n)a$\frac{252}{212129}$/$\frac{85123}{75}$Male (% of all participants)$\frac{52}{5053}$/$\frac{5751}{44}$Mean age (years)41.5±$\frac{11.4}{41.7}$±11.442.3±$\frac{11.8}{42.4}$±12.340.7±$\frac{11.1}{41.1}$±11.6Baseline weight (kg)75.19±$\frac{16.42}{75.41}$±16.5375.27±$\frac{16.78}{76.71}$±17.4475.10±$\frac{16.11}{76.37}$±17.24Baseline BMI (kg/m2)a25.78±$\frac{5.40}{25.98}$±5.4525.98±$\frac{5.36}{26.25}$±5.6325.59±$\frac{5.45}{26.30}$±5.76Caucasian descent (% of all participants)$\frac{97.6}{97.296.9}$/$\frac{96.598.4}{98.7}$First episode (n)$\frac{37}{2915}$/$\frac{1222}{13}$Antipsychotic Medicationb Amisulpride only (n)$\frac{129}{85129}$/85-/- Olanzapine only, (n)$\frac{123}{75}$-/-$\frac{123}{75}$ Amisulpride-Olanzapine switch (n)-/25-/--/- Olanzapine-Amisulpride switch (n)-/27-/--/-aThe baseline height was missing for one individual, therefore the analyses regarding BMI include one patient lessb40 of the 252 participants that finished phase I dropped out while in phase II. This explains the missing participants in the second part of this table. In phase I, 129 patients received amisulpride, in phase II 85 of these stuck to amisulpride, 25 switched to olanzapine, 19 dropped out. In phase I, 123 patients received olanzapine, in phase II 75 of these stuck to olanzapine, 27 switched to amisulpride, 21 dropped out
## Baseline characteristics
The average baseline BMI ± SD (standard deviation) of all 252 individuals participating in phase I was 25.8±5.4 kg/m2 (24.6±4.2 kg/m2 for male participants and 27.0±6.2 kg/m2 for female participants). One hundred twenty-nine patients receiving amisulpride during that time span had an average baseline BMI ± SD of 26.0±5.4 kg/m2 (25.2±4.8 kg/m2 for males and 26.9±5.8 kg/m2 for females) while 123 individuals receiving olanzapine in the first 2 weeks had an average baseline BMI ± SD of 25.6±5.4 kg/m2 (24.0±3.5 kg/m2 for males and 27.2±6.6 kg/m2 for females). The average age of all 252 patients was 41.5 years and $52\%$ were males, 37 individuals ($14.7\%$) had their first episode. At baseline, there was a statistically significant difference in body weight in respect to the rs17782313 genotype in all individuals participating in phase I. TT carriers weighed 73.5 kg compared to 79.1 kg in TC carriers and 68.8 kg in CC carriers. The same could be observed in patients treated with amisulpride yet not olanzapine, though in all three groups TC carriers had the highest baseline body weight and CC carriers the lowest (see Table 2).Table 2Analysis of variance for the rs17782313 polymorphism for phase i and the entire trialBoth antipsychoticsTT na = $\frac{155}{130}$TC na = $\frac{84}{70}$CC na = $\frac{13}{12}$P*P**Phase I onlym0 (kg)73.53±16.0479.08±17.2868.76±9.220.0210.077m2 (kg)74.32±15.7479.85±17.0070.14±10.390.0160.043Δm2-0 (kg)0.79±1.900.77±2.851.38±2.440.6700.823relΔm2-0 (%)1.23±2.681.10±3.651.92±3.370.6370.823BMI0 (kg/m2)25.47±5.4526.47±5.4825.09±3.840.3600.192BMI2 (kg/m2)25.74±5.3426.71±5.3325.56±3.930.3300.167ΔBMI2-0 (kg/m2)0.27±0.650.25±0.930.47±0.790.5750.839Entire trialbm0 (kg)74.26±16.5378.75±16.9668.32±9.480.0590.275m8 (kg)75.49±16.1881.09±16.8372.23±13.950.0390.117Δm8-0 (kg)1.23±3.082.34±4.003.91±6.580.1740.063relΔm8-0 (%)1.89±4.233.23±5.245.37±8.100.2560.112BMI0 (kg/m2)25.90±5.6426.36±5.3924.64±3.640.6430.682BMI8 (kg/m2)26.30±5.4427.12±5.1725.93±4.130.4380.308ΔBMI8-0 (kg/m2)0.40±1.050.75±1.301.29±1.990.1790.072Amisulpride onlyTT na=$\frac{83}{55}$TC na=$\frac{41}{26}$CC na=$\frac{5}{4}$PP*Phase I onlym0 (kg)72.68±16.7281.56±16.1166.66±6.790.0080.011m2 (kg)73.27±16.4382.09±15.4768.24±5.600.0050.007Δm2-0 (kg)0.59±1.750.53±2.621.58±2.130.4070.738relΔm2-0 (%)0.95±2.480.85±3.422.58±3.740.4400.883BMI0 (kg/m2)25.39±5.5727.28±4.9325.06±3.850.1210.060BMI2 (kg/m2)25.59±5.4327.46±4.6425.63±3.430.0860.036ΔBMI2-0 (kg/m2)0.20±0.600.17±0.860.57±0.730.3900.824Entire trialbm0 (kg)75.88±18.0780.29±16.5764.80±6.200.2160.557m8 (kg)76.57±17.0382.57±16.0168.40±4.060.1560.278Δm8-0 (kg)0.69±2.962.28±4.163.60±4.220.0630.043relΔm8-0 (%)1.33±4.243.11±5.145.97±7.480.1770.103BMI0 (kg/m2)26.30±6.1926.52±4.6723.71±2.770.6500.974BMI8 (kg/m2)26.52±5.7527.25±4.2824.98±1.550.6810.696ΔBMI8-0 (kg/m2)0.22±1.020.72±1.331.27±1.440.0600.028Olanzapine onlyTT na=$\frac{72}{43}$TC na=$\frac{43}{24}$CC na=$\frac{8}{8}$PP*Phase I onlym0 (kg)74.51±15.2776.71±18.1970.08±10.680.5340.967m2 (kg)75.53±14.9277.70±18.2671.33±12.760.5450.692Δm2-0 (kg)1.03±2.050.99±3.071.25±2.750.9970.936relΔm2-0 (%)1.57±2.861.33±3.881.50±3.320.9640.798BMI0 (kg/m2)25.57±5.3525.68±5.9125.11±4.100.9850.921BMI2 (kg/m2)25.92±5.2526.00±5.8725.51±4.450.9670.801ΔBMI2-0 (kg/m2)0.36±0.710.32±1.000.41±0.870.9890.945Entire trialbm0 (kg)75.76±16.0379.57±20.6770.08±10.680.3830.910m8 (kg)77.55±15.7682.56±21.1374.14±16.920.4860.949Δm8-0 (kg)1.79±3.132.99±4.394.06±7.770.6760.495relΔm8-0 (%)2.60±3.933.92±5.805.08±8.880.7910.566BMI0 (kg/m2)26.61±5.7526.16±6.3625.11±4.100.7160.419BMI8 (kg/m2)27.21±5.6927.13±6.4626.41±5.000.8110.567ΔBMI8-0 (kg/m2)0.60±1.090.97±1.391.30±2.310.7540.544*P-values for the comparison of TT carriers with TC carriers with CC carriers **P-values for the comparison of C allele carriers with TT carriers, anumber of carriers of each genotype for phase I/the entire trial, bBaseline weight and BMI are given for patients finishing phase I and the entire trial separately, P-values in bold are significantm0 baseline weight, mn weight after n weeks, Δmn-0 weight gain after n weeks, relΔmn-0 relative weight gain after n weeks compared to baseline in %, BMI0 baseline BMI, BMIn BMI after n weeks, ΔBMIn-0 BMI gain after n weeks Those individuals completing the entire 8 weeks of the trial (‘completers’) numbered 212 and were on average 41.7 years of age. Baseline BMI and body weight almost did not differ from those finishing at least phase I. While it could be again observed that TC carriers had the highest baseline weight and CC carriers the lowest, there was no statistical significance (see Tables 1 and 2). First episode patients were on average 32.5 years old and had a baseline body weight of 72.3±12.2 kg ($$P \leq 0.285$$ compared to patients who did not have a first episode). TT-carriers weighed 68.9±9.8 kg, TC-carriers had a baseline weight of 74.0±13.2 kg, and the only CC homozygous participant had a baseline weight of 90.0 kg (see supplementary Tables 3 and 4).
## Weight gain in phase I
After 2 weeks of treatment, $$n = 130$$ males gained on average 0.8±2.6 kg and $$n = 122$$ females gained on average 0.8±2.0 kg (combined 0.8±2.3 kg). Patients homozygous for the T allele gained 0.8±1.9 kg, TC carriers gained 0.8±2.9 kg, and CC carriers gained 1.4±2.4 kg ($$P \leq 0.670$$). In those treated with amisulpride, TT carriers gained 0.6±1.8 kg, TC carriers gained 0.5±2.6 kg, and homozygous C carriers had an average weight gain of 1.6±2.1 kg ($$P \leq 0.407$$). Compared to that, patients receiving olanzapine had an average weight gain of 1.0±2.0 kg in TT, 1.0±3.1 kg in TC and 1.3±2.7 kg in CC ($$P \leq 0.997$$) (see Table 2 and Fig. 1). Regarding phase I, stepwise multiple linear regressions with relative weight gain in phase I as dependent variable and MC4R genotype, age, sex, baseline body weight, and smoking status did not show a significant association of the MC4R genotype with relative weight in all three medication groups and in those having their first episode of illness (0.343<$P \leq 0.912$). In patients who had their first episode, TT carriers gained on average 1.9±1.9 kg, TC carriers gained 1.7±3.1 kg and the only homozygous C carrier gained 7 kg (see supplementary Table 4).Fig. 1Weight gain in kg observed within 8 weeks of treatment depending on medication in each genotype. Data are presented as mean+SD. Statistical tests compare TT with C carriers
## Weight gain in ‘completers’
After 8 weeks of treatment, the $$n = 106$$ male subjects had a mean ± SD weight gain of 2.4±4.2 kg and the $$n = 106$$ female subjects gained 1.1±3.1 kg (combined 1.7±3.7 kg). Homozygous carriers of the T allele gained on average 1.2±3.1 kg compared to 2.3±4.0 kg in TC carriers and 3.9±6.6 kg in CC carriers ($$P \leq 0.174$$). Overall, C allele carriers (CC and TC) gained an average of 2.6±4.5 kg ($$P \leq 0.063$$ compared to TT-genotype). In those treated with amisulpride only during the entire trial, TT carriers gained 0.7±3.0 kg, TC carriers gained 2.3±4.2 kg, and CC carriers 3.6±4.2 kg ($$P \leq 0.063$$). When comparing those with TT genotype to C allele carriers, the difference in weight gain (0.7±3.0 kg vs. 2.5±4.1 kg) and in BMI gain (0.2±1.1 kg/m2 vs. 0.8±1.4 kg/m2) reached statistical significance ($$P \leq 0.043$$ respectively $$P \leq 0.028$$). Individuals that received exclusively olanzapine experienced an average increase in body weight of 1.7±3.1 kg (TT), 3.0±4.4 kg (TC) and 4.0±7.8 kg (CC), $$P \leq 0.676$$ (Table 2 and Fig. 1). We then conducted stepwise multiple linear regressions with relative weight gain over the entire 8 weeks as dependent variable and MC4R genotype, age, sex, baseline body weight, and smoking status as possible independent variables. In all ‘completers’ regardless of medication, we identified lower age (β = −0.191; $$P \leq 0.004$$), male sex (β = 0.193; $$P \leq 0.004$$), lower baseline body weight (β = −0.233; $P \leq 0.001$), non-smoking status (β = 0.107; $$P \leq 0.110$$), and the C-MC4R allele (β = 0.201; $$P \leq 0.002$$) as relevant factors. The overall model was statistically significant, F[5, 206] = 8.244, $P \leq 0.001.$ Its R2 was 0.167 (adjusted R2 = 0.147). In patients receiving only amisulpride during the entire trial, we again identified lower age (β = −0.198; $$P \leq 0.049$$), male sex (β = 0.250; $$P \leq 0.015$$), lower baseline body weight (β = −0.408; $P \leq 0.001$), non-smoking status (β = 0.059; $$P \leq 0.529$$), and the C allele of MC4R (β = 0.244; $$P \leq 0.012$$) as relevant factors. The overall model was statistically significant, F[5, 79] = 7.372, $P \leq 0.001.$ Its R2 was 0.318 (adjusted R2 = 0.275). In patients treated exclusively with olanzapine during the entire 8 weeks, no significant factor could be identified ($P \leq 0.129$, MC4R: β = 0.169; $$P \leq 0.144$$), the overall model lacked significance ($$P \leq 0.199$$). Its R2 would have been 0.098, its adjusted R2 0.033. In first episode patients, TT carriers gained on average 2.8±2.6 kg compared to 3.8±3.4 kg in TC carriers. The patient with CC genotype gained 22 kg ($P \leq 0.001$ respectively $$P \leq 0.110$$ for the comparison of TT with C, see supplementary Table 4). A likewise conducted stepwise multiple linear regression analysis was not significant in first episode patients (overall model $$P \leq 0.235$$, C allele of MC4R $$P \leq 0.077$$).
## Multiple comparisons
Twenty-five variables were corrected for multiple comparisons by Holm’s method. The two lowest p values remained significant, these were the analysis of variance of weight gain within 8 weeks in first episode patients ($P \leq 0.001$, corrected α = 0.002) and the MC4R genotype as a factor in the stepwise multiple linear regression analysis of relative weight gain within 8 weeks in all completers ($$P \leq 0.0019$$, corrected α = 0.0021) (see supplementary Table 2).
## Discussion
We observed a statistically significant association between the rs17782313 polymorphism and absolute weight gain and absolute BMI gain after 8 weeks of antipsychotic treatment in the amisulpride subpopulation when comparing carriers of the C allele with TT carriers. The same direction of effect could be seen in the entire population and the olanzapine group, though it failed to reach conventional levels of statistical significance. The analysis indicates that the C allele might lead to a higher weight and BMI gain under SGA treatment.
TC carriers showed a higher baseline body weight and BMI than TT carriers in all populations, as previously shown in the GWAS (Loos et al. 2008) and in other studies (Beckers et al. 2011; Huang et al. 2011; Xi et al. 2012). The homozygotes for the C allele had a lower baseline weight and BMI in all subpopulations except first episode patients, which is contrary to earlier publications. This, however, might be explained by the low number of subjects compared to the other genotypes ($$n = 13$$ in the whole population, $$n = 5$$ in the amisulpride group, $$n = 8$$ in the olanzapine group and $$n = 1$$ in first episode patients). In addition, the high percentage of pretreated patients might have played a role in this, as only $14.7\%$ of participants had their first episode.
Patients in the olanzapine medication group gained a mean ± SD body weight of 2.4±4.2 kg within 8 weeks. A meta-analysis from Allison et al. [ 1999] used a fixed-effects model to calculate an estimated weight change (kg) of 3.51 with a $95\%$ confidence interval (3.29–3.73) after 10 weeks of treatment. Although there is no information on the percentage of drug-naïve patients and other demographic characteristics such as sex, baseline weight and age while the number of pretreated subjects in our study was rather high, these numbers are of comparable size. In the amisulpride subpopulation, a mean ± SD weight gain of 1.3± 3.5 kg occurred. Leucht et al. [ 2004] investigated the weight gain caused by treatment with amisulpride in a meta-analysis. Their estimate after 10 weeks of treatment was 0.80 kg, $95\%$ CI (0.48–1.18) and therefore similar to the weight gain measured in our investigation as well. We observed that olanzapine caused a greater increase in body weight than amisulpride in a double-blind setting. The higher AIWG in patients treated with olanzapine is expected to be due to its affinity to the H1 (histamine 1) receptor and 5-HT2C (serotonin 2C) receptor in contrast to amisulpride binding the D2 and D3 (dopamine 3) receptor (Balt et al. 2011).
In first episode patients, the rs17782313 was significantly associated with weight gain and relative weight gain (compared to baseline) after 8 weeks, even after comparing for multiple corrections, though due to the low minor allele frequency only one individual was homozygous for the C-allele, and that patient happened to experience a multiple times larger increase in body weight. Czerwensky et al. [ 2013] analyzed a first episode subpopulation and discovered a significant association between the rs17782313 and AIWG. However, they reported 9 CC genotypes in 96 first episode patients. Zhang et al. [ 2016] on the other hand failed to reproduce these findings in their larger first episode population ($$n = 567$$, nCC = 14). As schizophrenia is a chronic mental illness with long courses of disease, researchers might struggle to generate large samples of first episode patients, thus adversely affecting transferability of these results.
Patients homozygous for the rs17782313 C allele gained both more absolute and more relative weight than those heterozygous for the C allele or TT carriers in all three medication groups after 2 weeks as well as after 8 weeks of antipsychotic treatment. Despite these differences not reaching significant levels, a correction for known confounders in stepwise multiple linear regressions showed significant p-values in the whole study population and the amisulpride subpopulation for a time span of 8 weeks. We could reproduce the positive association of low baseline body weight, non-smoking and younger age with relative weight gain, which previous studies have also reported (Czerwensky et al. 2013; Gebhardt et al. 2009; Müller and Kennedy 2006). While sex differences in SGA-related weight gain still remain unclear (Gebhardt et al. 2009), our model showed a positive association with the male sex. As potential differences in gene expression related to AIWG between the sexes (Sainz et al. 2019) have been shown, further studies are required to clarify the influence of sex on antipsychotic-induced weight gain. Our regression model had a coefficient of determination R2 of 0.167, indicating that $16.7\%$ of the percent weight variation in all patients can be explained by the factors of baseline weight, age, sex, smoking status, and the MC4R genotype.
As the increased weight gain is attributed to the C allele, the comparison of subjects homozygous or heterozygous for the C allele to those homozygous for the T allele showed a tendency in the whole population and in the amisulpride subpopulation of a significant difference for the entire duration of the trial but not after 2 weeks. Such short intervals might be simply not long enough for the genetic influence on AIWG to be seen. Czerwensky et al. [ 2013] found their significant association after 4 weeks of treatment, Chowdhury et al. [ 2013] saw tendencies in certain groups of patients after 6 weeks, and Zhang et al. [ 2019] could not find a significant correlation after 6 weeks. Temporal dynamics of a possible connection of the rs17782313 and AIWG have not been described so far, and our results in this regard should be interpreted with caution as not all patients participating in phase I finished the entire trial. Furthermore, a ‘ceiling effect’ in patients treated with olanzapine could have played a role here (Kinon et al. 2005).
When comparing the two antipsychotic substances, in patients only treated with amisulpride, the relative weight gain of the CC allele carriers after the full 8 weeks of treatment was 4.5 times higher and of the TC allele carriers 2.3 times higher than that of TT carriers, yet in patients only treated with olanzapine, it was 2.0 times higher in CC carriers respectively 1.5 times higher in heterozygous patients than in those homozygous for the T allele. Furthermore, in the stepwise multiple linear regressions, the influence of the MC4R genotype on relative weight gain was stronger in patients treated with amisulpride than olanzapine (β = 0.244; $$P \leq 0.012$$ vs. β = 0.169; $$P \leq 0.144$$). This might suggest that the rs17782313 polymorphism has different capabilities of enhancing weight gain under SGA treatment depending on the substance given, or rather the interallelic differences in weight gain may depend on the substance. The rs17782313 polymorphism is expected to result in some loss of function of the MC4R (human melanocortin 4 receptor), which would normally lower food intake when stimulated (Balt et al. 2011; Fan and Tao 2009). Therefore, the presence of a C allele would increase energy intake and result in a higher body weight. As mentioned above, olanzapine binds to the H1 receptor and 5-HT2C receptor. Antagonism to the latter results in decreased levels of α-MSH (α-melanocyte stimulating hormone) (Balt et al. 2011), which is the endogenous, stimulating ligand of MC4R. Lower levels of α-MSH therefore should result in a higher food intake. Through this mechanism, treatment with olanzapine might induce weight gain in all three rs17782313 genotypes and show an additive effect in C allele carriers, as proposed by others (Czerwensky et al. 2013). Amisulpride, on the other hand, is not considered to influence the α-MSH level. When treated with amisulpride, the α-MSH level remains unaffected and therefore does not cause extra weight gain in all allele carriers. The additional increase in body weight under treatment with olanzapine in all genotypes through this pathway might ‘conceal’, so to speak, the effect caused by rs17782313 and flatten relative, interallelic differences (Table 2). However, these results must be interpreted carefully as the number of patients homozygous for the C allele was small (for the complete trial $$n = 4$$ in the amisulpride subpopulation and $$n = 8$$ in the olanzapine subpopulation) and further research is needed to support this hypothesis.
We decided to perform Holm’s procedure to correct for multiple comparisons to evaluate robustness of our statistical findings. The MC4R genotype remained significantly associated with weight gain after 8 weeks in first episode patients (TT vs TC vs CC) and as a factor in multiple linear regression in ‘completers’ of the trial. Therefore, our statistical findings should be interpreted carefully. However, we believe that exploratory approaches do not provide strict evidence for certain genetic effects in the first place, but they help investigate possible associations and are necessary for later more rigorous studies. Moreover, even results that have no significant association with a proposed effect but show a tendency should be reported as they would otherwise lead to publication bias.
Some limitations of this study must be mentioned. First, although there were only two different SGAs given and the number of subjects treated with each one is therefore relatively large, the overall study population consisted of only 252 patients for the first 2 weeks and 212 individuals for the entire 8 weeks and is smaller than in the previous analyses (Chowdhury et al. 2013; Czerwensky et al. 2013; Zhang et al. 2019). However, these previous trials included naturalistic samples of patients while the SWITCH study was a multicenter double blinded, controlled study, which makes this analysis to date unique. Second, the number of pretreated patients was rather high, as only $14.7\%$ had their first episode. Nevertheless, as schizophrenia is a chronic disease and many patients receive treatment for long periods of time, the effect in premedicated patients does not necessarily lack relevance for clinical settings. Third, the influence of comedication cannot be fully measured. Some drugs, especially antidepressants, can induce weight gain on their own (Drieling et al. 2005). Nonetheless, the comedication in this study was quite clear, patients received only one SGA, and certain drugs as mood stabilizers or recently initiated antidepressants were not to be given. Third, the duration of this trial was only 8 weeks and some patients dropped out early, which may negatively impact the generalizability of our findings, though previous studies analyzing the influence of the rs17782313 on AIWG were all shorter. In addition to that, the bulk of AIWG associated with olanzapine occurs within the first 12 weeks of treatment (Kinon et al. 2005) and in the case of clozapine, with the patients in the SWITCH study unfortunately not received, within 6 weeks even in pretreated patients (Meltzer et al. 2003). Future investigations on genetic influence on olanzapine-associated weight gain and clozapine-associated weight gain should take those time frames into account. Finally, there is still the possibility that neither the MC4R gene nor the rs17782313 single nucleotide polymorphism have an influence on AIWG at all, but our findings are coincidental. As AIWG most likely has a polygenic nature (Zhang et al. 2016), our candidate gene approach cannot prove a causal link between the SNP and AIWG but show statistically significant association, contributing to the existing and future studies and providing data for meta analyses.
In this study, we report a significant association of amisulpride-induced weight gain after 8 weeks of treatment and the rs17782313 C allele, a polymorphism located in the promoter region of MC4R, in a predominantly Caucasian population and observed a tendency in the overall and olanzapine-treated population. Our findings support the earlier assumption, namely that the rs17782313 polymorphism might play a role in SGA-related weight gain, yet fail to remain significant when corrected for multiple comparisons. To our knowledge, our explanation involving the MC4R for relative, interallelic differences depending on the medication given has not been given so far. Overall, further studies with larger populations are needed to clarify the influence of the rs17782313 polymorphism on SGA-related weight gain, investigate differences regarding different antipsychotics, and set a basis for future meta-analyses.
## Supplementary information
Supplementary Table 1Variables and Normal Distribution. Supplementary Table 2 Holm’s Test for Multiple Comparisons. Supplementary Table 3 Baseline Characteristics of Patients *With a* First Episode. Supplementary Table 4 Analysis of Variance for the rs17782313 Polymorphism for Phase I and the Entire Trial in Patients *With a* First Episode (PDF 201 kb)
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|
---
title: Cerebrovascular Gi Proteins Protect Against Brain Hypoperfusion and Collateral
Failure in Cerebral Ischemia
authors:
- Salvador Castaneda-Vega
- Sandra Beer-Hammer
- Veronika Leiss
- Hanna Napieczyńska
- Marta Vuozzo
- Andreas M. Schmid
- Hang Zeng
- Yi He
- Ursula Kohlhofer
- Irene Gonzalez-Menendez
- Leticia Quintanilla-Martinez
- Johann-Martin Hempel
- Maik Gollasch
- Xin Yu
- Bernd J. Pichler
- Bernd Nürnberg
journal: Molecular Imaging and Biology
year: 2022
pmcid: PMC10006265
doi: 10.1007/s11307-022-01764-8
license: CC BY 4.0
---
# Cerebrovascular Gi Proteins Protect Against Brain Hypoperfusion and Collateral Failure in Cerebral Ischemia
## Abstract
Cerebral hypoperfusion and vascular dysfunction are closely related to common risk factors for ischemic stroke such as hypertension, dyslipidemia, diabetes, and smoking. The role of inhibitory G protein-dependent receptor (GiPCR) signaling in regulating cerebrovascular functions remains largely elusive. We examined the importance of GiPCR signaling in cerebral blood flow (CBF) and its stability after sudden interruption using various in vivo high-resolution magnetic resonance imaging techniques. To this end, we induced a functional knockout of GiPCR signaling in the brain vasculature by injection of pertussis toxin (PTX). Our results show that PTX induced global brain hypoperfusion and microvascular collapse. When PTX-pretreated animals underwent transient unilateral occlusion of one common carotid artery, CBF was disrupted in the ipsilateral hemisphere resulting in the collapse of the cortically penetrating microvessels. In addition, pronounced stroke features in the affected brain regions appeared in both MRI and histological examination. Our findings suggest an impact of cerebrovascular GiPCR signaling in the maintenance of CBF, which may be useful for novel pharmacotherapeutic approaches to prevent and treat cerebrovascular dysfunction and stroke.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s11307-022-01764-8.
## Introduction
Gi proteins are the principal signal transducers of a broad subset of G protein-coupled receptors (termed GiPCRs), including those for acetylcholine, adenosine, and catecholamines that control blood circulation [1–5]. In spite of many studies, the functions of Gi protein-dependent signaling in the brain vasculature have been largely ignored. One major reason for the undefined role of Gi proteins may be the lack of appropriate animal models, since a simultaneous genetic ablation of the major Gαi isoforms (Gαi2 and Gαi3) produces embryonic lethality in mice [6]. On the other hand, the significance of singular knockouts is limited by overlapping functions of these isoforms. Pertussis toxin (PTX) has been used to study Gi protein signaling in the cardiovascular system [7, 8], but the effects on cerebrovasculature have not yet been elucidated.
In vivo, PTX irreversibly and with high specificity blocks Gi-linked GPCR signal transduction — hereafter referred to as non-cerebral GiPCR KO — by catalyzing covalent modification of a C-terminal cysteine residue of cellular Gαi isoforms [9–11]. In arteries and microvessels, PTX inhibits endothelium-dependent relaxation to certain agonists such as β-adrenergic ligands, angiotensin, serotonin, or relaxins and is therefore useful for the study of vasculopathies [2, 7, 8, 12–18]. We have previously demonstrated that PTX, administered in a single peritoneal injection, does not cross the blood–brain barrier (BBB) and does not modify GiPCR signaling in neurons [19]. PTX administration, however, still irreversibly interrupts GiPCR signaling for up to 96 h in cells outside of the CNS, including brain vasculature. Using PTX, in this work, we evaluate the effects in cerebral blood flow caused by permanent GiPCR modification of cells outside of the brain.
MRI provides a powerful set of neuroimaging tools that allow quantification of pathological changes at the functional level in the brain [20–22]. In vivo MRI allows consistent acquisition of multiple tissue characteristics in a single session permitting evaluation of their longitudinal development [20, 22].
In the present study, we focused, first, on the effects of PTX administration in systemic blood flow, cerebral blood flow (CBF), and microvascular patency and, second, on GiPCR-dependent vascular responses after acute vascular occlusion. Our data reveal that injection of PTX severely reduces cerebral perfusion and impedes compensatory mechanisms regulating CBF. As a result, microcirculation collapses during vascular occlusion, contributing to ischemic brain lesions.
## Animal Experiments
The study was carried out in compliance with the ARRIVE guidelines. All experiments were performed according to the EU Animals Scientific Procedures Act and the German law for the welfare of animals and were approved by the local animal ethical committees (Regierungspräsidium Tübingen, PH $\frac{10}{13}$, PH $\frac{1}{11}$, PH $\frac{04}{19}$). C57BL/6 female, 8-week-old, mice were kept under specified pathogen-free conditions, controlled temperature, and humidity in 12-h day/night light cycles, receiving food and water ad libitum. Workflows of all experiments are sketched in the corresponding figures. We induced non-cerebral GiPCR KO using a single dose of PTX (150 μg/kg body weight) (Merck, Darmstadt, Germany) 48 h before intervention (sham or surgery), as previously shown [19, 23, 24]. Details are provided in the “Supplementary Information.”
## Whole-Body Semi-quantitative Perfusion
In order to determine whether PTX produced whole-body systemic organ-perfusion changes, mice weighing 20–23 g were evaluated using dynamic contrast-enhanced (DCE) MRI. Vehicle (phosphate-buffered solution, PBS) or PTX was injected intraperitoneal (i.p.) into 5 mice per group. After 48 h, transversal DCE images focusing on multiple body regions including the lung, kidney, paravertebral muscle, abdominal vessels, heart, and brain were acquired. Details are provided in the “Supplementary Information.”
## Longitudinal Multiparametric MRI—Animals and Treatment
For these experiments, 8-week-old mice were divided into four groups: PBS-pretreated sham-operated, PTX-pretreated sham-operated, PBS-pretreated occluded, and PTX-pretreated occluded. These groups were studied in two main experimental settings: in the first setting, mice were imaged immediately after vessel occlusion using a non-absorbable suture around one common carotid artery (CCA) or a sham surgery. In the second setting, the groups were imaged 1 week before (baseline) and 48 h after a transient occlusion (lasting 30 min) of one CCA or a sham surgery. Further experimental details are shown in the corresponding figures, Table 1, and “Supplementary Information.” PTX (150 µg/kg b.w., i.p.) or PBS were injected 48 h before surgery. Table 1Summary of imaging experimental setup. The table shows the subdivisions of groups and time points. The number of animals used for statistics at every time point and animal group are displayed. Animals were injected with PTX 150 µg/kg b.w. i.p. or PBS 48 h before surgery. Images were obtained from mice in two main experimental settings. One main group was imaged during occlusion or sham surgery (Top). The other main group was examined at baseline (BL) and 48 h after occlusion/reperfusion (unilateral transient CCA) or sham surgery (96 h)Group namenPTXSurgeryImage time pointSham PBS5-Sham0 hSham PTX4 + Sham0 hOcclusion PBS6-Occlusion0 hOcclusion PTX5 + Occlusion0 hSham PBS5-ShamBL / 96 hSham PTX4 + ShamBL / 96 hOcclusion/reperfusion PBS5-Occlusion/reperfusionBL / 96 hOcclusion/reperfusion PTX4 + Occlusion/reperfusionBL / 96 h
## Longitudinal Multiparametric MRI—Acquisitions and Analysis
Multiparametric MRI acquisitions were performed using a ClinScan 7-T small-animal MR scanner, a rat whole-body transmitting coil, and a 4-channel mouse brain surface receiving coil (Bruker Biospin). The imaging protocol consisted of anatomical T2-weighted images (T2WI), diffusion-weighted images (DWI), perfusion-weighted images (PWI), and multi-turbo-spin-echo T2-weighted acquisitions focusing specifically on the Bregma/Interaural 3.82 ± 0.25 mm brain region as previously performed [20–22] and detailed in the “Supplementary Information.”
## Volumes of Interest
All parametric images were coregistered to a common template using Pmod software (Bruker Biospin). Volumes of interest (VOIs) delimiting the striatal and cortical regions on both hemispheres were manually drawn at the above-described Bregma/Interaural brain regions using the T2WI as anatomical reference. The VOIs were overlaid on the PWI, ADC, and T2 maps followed by extraction of raw data. Lesion volumes were drawn on anatomical T2WI which covered the whole brain. Details are provided in the “Supplementary Information.”
## Single-Vessel Multi-gradient Echo (MGE) Imaging Experiment
We performed multi-gradient echo data acquisitions for single-vessel imaging using a 14.1 T / 26 cm magnet (Magnex, Oxford, UK) with an Avance III console (Bruker Biospin) and a 12-cm-diameter gradient providing 100G / cm with 150-µs rise time (Resonance Research, Massachusetts, U.S.A). A home-made RF surface coil (8-mm outer diameter) was used for single-vessel mapping. Details are provided in the “Supplementary Information.”
## Histology and Immunohistochemistry
For characterization of cerebral lesions, samples were stained with antibodies against CD31 (Abcam, Cambridge, UK), GFAP (Clone 6F2, Dako GmbH, Germany), HIF-1α (Clone ESEE 122, Abcam), and EPO (Clone H-162, Santa Cruz Biotechnology, Inc.). H&E staining was also performed. Details are provided in the “Supplementary Information.”
## Statistics
Sample size and power calculations were conducted and approved by the local animal ethical committees. Animal numbers for the main experiments are shown in Table 1. We evaluated non-Gaussian distribution for all experimental datasets previous to statistical testing using the Jarque–Bera test. Whole-body perfusion data presented several datasets that were non-normality distributed; therefore, statistical evaluation was performed using a 2-sided non-parametric Wilcoxon signed-rank test. All other experiments were evaluated using either 2-way or 3-way ANOVA [25], followed by multiple comparison corrections using Tukey’s honestly significant test [26]. Details are provided in the “Supplementary Information.”
## Differential Effects of a Functional Non-cerebral GiPCR KO on Blood Flow to Different Organs and Body Compartments
First, we examined the rough impact of PTX-induced functional non-cerebral GiPCR KO on blood flow in different organs and compartments using whole-body dynamic contrast-enhanced (DCE) MRI. ANOVA determined a statistically significant main group difference between PTX and control animals. To detect differences between the same organs of both groups, Wilcoxon matched-pairs signed-rank test was performed, and a statistically decreased blood flow was only found in the brain of PTX animals (Fig. 1A). As with some other organs and compartments, median blood flow in the ventricle, reflecting ejection fraction in this experimental setting, was lower than in the control group, but without being statistically significant (Fig. 1B–F). Blood flow to the renal cortex after PTX treatment also suggested sustained perfusion of the kidney (Fig. 1C). The contrast agent accumulated in the renal calyceal system of PTX-treated animals, also indicating continued tubular excretion. The systemic results led us to investigate the effects of PTX in the brain using MRI techniques with better spatial resolution. Fig. 1Functional non-cerebral GiPCR KO using PTX induces cerebral hypoperfusion. Whole-body perfusion was measured using dynamic contrast-enhanced imaging in PTX-pretreated and PBS-pretreated animals ($$n = 5$$ per group). A Wilcoxon matched-pairs signed-rank test found significant hypoperfusion in the brain of PTX-injected mice in comparison to PBS-treated animals (* $p \leq 0.05$). B Lung showed normal perfusion, whereas C kidney, D muscle, E abdominal vessels, and F heart yielded a hypoperfusion trend. Shown are median, 1st, and 3rd quartile of data distribution. The whiskers extend to the largest and smallest data point, respectively.
## Reduction of Global CBF Following Functional Systemic Non-cerebral GiPCR KO with PTX
To confirm and extend our observation of decreased CBF, we subjected the mice to an arterial spin labeling (ASL) MRI protocol (for details, see the “Materials and Methods” section and Fig. 2A, B), which shows the distribution of blood perfusion in the brain and provides reliable quantifications of CBF [21, 27, 28]. Coronal cross-sectional perfusion-weighted imaging (PWI) confirmed whole-brain hypoperfusion in PTX-pretreated sham-operated mice (Fig. 2C; yellow arrow). We quantified the CBF for the striatal and cortical regions (Fig. 2D, E). Both ipsi- and contralateral CBF were reduced by more than half in these regions compared to untreated sham-operated controls (Fig. 2D, E). Nevertheless, all of these reduced CBF values were above a range associated with ischemic lesions [29, 30]. Thus, in agreement with the DCE measurements (see Fig. 1), our ASL data clearly reveal a systemic suppressive effect of PTX on CBF.Fig. 2Functional PTX-induced non-cerebral GiPCR KO sensitizes for cerebral ischemia during permanent carotid artery occlusion. A Timeline of PBS/PTX injection, surgery (sham or CCA occlusion), and arterial spin-labeling MRI analysis. B Schematic overview of axial and coronal cross-sections of the mouse brain. The different brain regions of interest used for analysis are indicated. The red line shows the position of the cross-section corresponding to the coronal view. The blue line depicts the limit of ipsi (left)- and contra (right)-lateral brain hemispheres. C Perfusion-weighted images (PWI) indicate hypoperfusion of sham-operated PTX-treated mice (yellow arrow) compared to the sham PBS group. During left carotid artery ligation (occlusion), PBS-treated mice showed hypoperfusion visible in the ipsilateral hemisphere (green arrow), whereas PTX-pretreated mice exhibited global cerebral hypoperfusion, confirming the effects observed in whole-body perfusion analysis. Moreover, the perfusion of PTX-pretreated mice was interrupted in the ipsilateral hemisphere (red arrow) during occlusion, in comparison to animals receiving PBS. Shown are images of one representative mouse per group. Further examples are provided in Suppl. Fig. 1B. Corresponding quantification and statistics of CBF are shown in D for ipsi- and E for contralateral striatum and cortex (for details see Table 1). Statistical analysis was performed using 2-way ANOVA (* $p \leq 0.05$, *** $p \leq 0.001$). Shown are median, 1st, and 3rd quartile of data distribution. The whiskers extend to the largest and smallest data point, respectively.
## PTX Administration Sensitizes to Ischemia
Having established that a functional non-cerebral GiPCR KO with PTX had an effect in CBF per se, we examined the consequences of acute occlusion of one common carotid artery (CCA) in cerebral hypoperfusion (see the “Materials and Methods” section and Fig. 2A, B). As evident from PWI, unilateral CCA occlusion in control animals treated with PBS resulted in a large decrease on CBF ipsilateral to the occlusion (Fig. 2C, Suppl. Fig. 1B; green arrow). This is also reflected in the calculated CBF values, which showed a clear hypoperfusion for the ipsilateral striatum and cortex (Fig. 2D). However, the hypoperfusion did not reach a level described to cause ischemia and necrosis [29, 30]. Of note, blood flow in the contralateral regions remained stable (Fig. 2E), which should allow for potential compensatory blood flow to the hypoperfused regions [31].
In contrast, PTX-pretreated mice showed global cerebral hypoperfusion that was further aggravated ipsilateral to the unilateral CCA ligation resulting in a complete breakdown of perfusion in both the striatum and cortex (Fig. 2C, D; Suppl. Fig. 1B; red arrow). These ipsilateral values were below the threshold at which ischemic injury occurs [29, 30]. On the contralateral side, an extent of reduction occurred that we had already observed in the sham-operated mice pretreated with PTX, and that may impede compensatory blood flow to the hypoperfused ipsilateral regions (Fig. 2E, Suppl. Fig. 1B).
Our findings show that a functional non-cerebral GiPCR KO with PTX suppresses cerebral perfusion, which upon challenge by unilateral CCA occlusion severely disrupts CBF distal to the ligation, i.e., in the ipsilateral hemisphere. We were therefore interested in how perfusion subsequently developed and compared PWI at baseline and 48 h after surgery, which corresponded to 96 h after PTX administration (Suppl. Fig. 2A, B). The CBF in the brain of the PBS-injected mice, sham-operated or transiently CCA-occluded, was invariant from baseline post surgically at 48 h (Suppl. Fig. 2C-F). The corresponding CBF in the non-cerebral GiPCR KO mice was reduced albeit not significantly compared to baseline. Compared with the CBF of non-cerebral GiPCR KO mice during occlusion (see Suppl. Fig. 1B and Fig. 2D, E) the CBF of mice monitored 48 h later, i.e., 96 h after PTX dosing (see Suppl. Fig. 2C-F), indicated a partial recovery. However, there was no difference in CBF in PTX-pretreated mice regardless of whether they were sham-operated or transiently CCA-occluded 48 h before (see Suppl. Fig. 2C-F). This finding was in contrast to the different results in the two PTX-pretreated groups, i.e., sham-operated or transiently CCA-occluded at the time of occlusion (see Suppl. Fig. 1B and Fig. 2D, E). This prompted us to further investigate consequences of collapsed perfusion in non-cerebral GiPCR KO mice after transient unilateral CCA occlusion (Suppl. Fig. 3A).
## Functional Non-cerebral GiPCR KO Together with Transient Unilateral Carotid Artery Occlusion Leads to Cytotoxic and Vasogenic Edema
Diffusion-weighted images (DWI) provide a measurement of diffusion that can be quantified in the apparent diffusion coefficient (ADC) using MRI. ADC restrictions in the brain are the gold standard to identify ischemic stroke lesions, which have been shown to strongly correlate to final infarct lesions in tissue sections [32–35]. Diffusion restrictions have been known to start rapidly after stroke onset, peaking within one day, followed by slow value normalization [36, 37]. Consistent with these previous reports, PTX-pretreated and occluded mice already showed incipient ADC restrictions during occlusion (see Suppl. Fig. 3B-D), which were still evident in the mice imaged at 48 h post-surgery, corresponding to 96 h after PTX administration (Fig. 3B; red arrow, Fig. 3C). These ADC restrictions were clearly demarcated in DWIs of these mice (Suppl. Fig. 3B-D).Fig. 3Cytotoxic and vasogenic edema in non-cerebral GiPCR KO following transient CCA occlusion. A Timeline of baseline MRI acquisition, PBS/PTX injection, surgery (sham or CCA occlusion), and post-operative MRI acquisitions. B Representative images of mouse brains showing the apparent diffusion coefficient (ADC), T2 map, and T2-weighted images (T2WI). Red arrows indicate the ischemic lesions in occluded PTX-pretreated mice consisting of reduced signal intensity of ADC images as well as hyperintensity in T2WI and T2 maps (for more details, see Tables 1, 2, and 3). Corresponding quantification and statistical analysis of ipsilateral ADC (C) and T2 (D) in the striatum. Only PTX-pretreated mice following transient CCA occlusion presented a lesioned striatum with increments in ADC, accompanied by an increased T2 relaxation time. Statistical analysis was performed using 3-way ANOVA (* $p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$). Shown are median, 1st, and 3rd quartile of data distribution. The whiskers extend to the largest and smallest data point respectively. Table 2Contralateral striatum resultsMeasurement (unit)GroupBL0 h96 hADC(× 10–3 mm2/s)Sham PBS0.66 ± 0.040.68 ± 0.150.68 ± 0.13Sham PTX0.64 ± 0.080.62 ± 0.260.66 ± 0.15Occlusion PBS0.65 ± 0.080.55 ± 0.130.65 ± 0.11Occlusion PTX0.65 ± 0.080.62 ± 0.150.64 ± 0.08T2 relaxation time(ms)Sham PBS56.7 ± 2.059.0 ± 3.458.7 ± 11.1Sham PTX59.2 ± 2.455.6 ± 7.860.6 ± 5.5Occlusion PBS61.0 ± 5.653.8 ± 3.656.8 ± 2.6Occlusion PTX58.5 ± 3.856.2 ± 3.056.4 ± 2.4Table 3Contralateral cortex resultsMeasurement (unit)GroupBL0 h96 hADC(× 10–3 mm2/s)Sham PBS0.65 ± 0.120.72 ± 0.200.65 ± 0.12Sham PTX0.65 ± 0.150.65 ± 0.080.68 ± 0.15Occlusion PBS0.66 ± 0.040.58 ± 0.230.67 ± 0.04Occlusion PTX0.60 ± 0.060.60 ± 0.130.67 ± 0.06T2 relaxation time(ms)Sham PBS59.8 ± 7.158.4 ± 4.059.9 ± 7.1Sham PTX57.9 ± 4.757.8 ± 4.461.9 ± 4.7Occlusion PBS59.6 ± 3.956.4 ± 4.459.6 ± 3.9Occlusion PTX60.8 ± 1.657.0 ± 1.457.1 ± 1.6 Moreover, T2 relaxation maps and T2WI representing vasogenic edema, demonstrated hyperintense signals only in the PTX-pretreated occluded animals at the latest timepoint (Fig. 3B, D; Suppl. Fig. 3B, E, F), consistent with previous literature [36, 37]. As the occluded animals pretreated with PTX showed vasogenic edema, we quantified edema volume in relation to their anatomical structures (Suppl. Fig. 4). In contrast, no signs of cytotoxic or vasogenic edema were detectable in both sham-operated groups and the PBS-treated occluded group 48 h after occlusion (Figs. 3C, D).
Because cytotoxic and vasogenic edema developed only in PTX-pretreated animals with transient CCA occlusion, we performed histological and immunohistochemical analyses to confirm the presence of an ischemic stroke phenotype, as we have previously done in other stroke models [20, 35]. Detection of ischemic lesions using hypoxia-inducible factor 1α (HIF-1α) and erythropoietin (EPO) immunohistochemistry has been previously shown to clearly delimit the infarct core and the peri-infarct stroke region [38, 39] (Fig. 4A-D; Suppl. Fig. 5). The immunohistochemical staining showed focal lesions in the PTX-pretreated and CCA-occluded animals demonstrating ischemia ipsilateral to the occlusion, which perfectly colocalized with hyperintense lesions seen in DWIs and T2WIs (Fig. 4). Furthermore, H&E staining and immunohistochemistry for the endothelial markers CD31 and GFAP (Suppl. Fig. 5) revealed prominent lesions with neuronal pallor, vacuolation of the neuropil and edema (H&E) in various regions of the ipsilateral hemisphere, as well as blood vessels (CD31) and reactive gliosis (GFAP). Thus, clear signs of ischemic stroke through in vivo imaging were confirmed in PTX-pretreated transiently CCA-occluded animals using immunohistochemistry and histology. Fig. 4Colocalization of DWIs and T2WIs with immunohistochemical ischemia in occluded PTX-pretreated mice. A Timeline of PBS/PTX injection protocol, surgery, and MRI acquisition. B DWI (b value = 600 s/mm2) and T2WI of animals at 96 h on the coronal projection. The occluded PTX-pretreated mice show hyperintensities in the striatal, hippocampal, and cortical brain regions on DWI and T2WI (orange arrowheads). Animals of the other groups showed no visible lesions. For more details, see Table 1. C HIF-1α is stained in hippocampal stroke regions and marks the infarcted region colocalizing with the DWIs. D. Staining of the hypoxia-inducible cytokine EPO shows a focalized lesion similar to the HIF-1α-positive hypoxic region further confirming an ischemic event. Immunohistochemistry was done in $$n = 4$$ mice per group.
## Functional Non-cerebral GiPCR KO with PTX Reduces Patency of Individual Cortex-Penetrating Microvessels
We investigated whether hypoperfusion was associated with collapsed microvessels. To specifically investigate the immediate response of microvessels to CCA occlusion, we used a multi-gradient echo (MGE) MRI sequence (Fig. 5A) [40–42]. High-resolution MGE-MRI provides a penetrating microvessel-specific measurement of the cortex that allows the estimation of microvascular collapse. Comparison of PBS-treated mice regardless of CCA occlusion revealed no difference in the number of vessels in both hemispheres (Fig. 5), indicating a normal microvascular function. Fig. 5Functional non-cerebral GiPCR KO reduces patency of cortex-penetrating microvessels. A Timeline of PBS/PTX injection and surgery protocol following multi-gradient echo (MGE) MRI acquisition. These experiments were performed during occlusion or sham surgery. Results of quantification of vessel numbers in the ipsi- (B) and (C) contralateral cortex ($$n = 6$$–9). Vessel numbers of PTX-pretreated mice are reduced in both hemispheres, which is further aggravated upon occlusion in the ipsilateral cortex. Statistical analysis was performed using 2-way ANOVA (* $p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$). D Representative pictures from all four groups measured by MGE (upper panel). The black boxes mark the assessed areas, and the red dots are the identified vessels (lower panel). Shown are median, 1st, and 3rd quartile of data distribution. The whiskers extend to the largest and smallest data point, respectively.
In contrast, the PTX-induced functional non-cerebral GiPCR KO provoked a reduction of quantifiable microvessels in the cortex of both hemispheres compared to the PBS groups (Fig. 5). The effect was further aggravated in the PTX-pretreated occluded mice, where an even more prominent number of microvessels collapsed in the ipsilateral cortex (Fig. 5). In combination with our perfusion experiments, these data suggest that PTX does not only cause global cerebral hypoperfusion but also micro-cerebrovascular collapse, which has also been described to occur under low-perfusion pressure in heart vessels [7].
## Discussion
Cerebrovascular functions of GiPCR-driven signaling are still largely unknown. To gain more insight, we employed the highly specific inhibitor PTX in order to specifically disrupt extraneuronal GiPCR signaling. Our results point to previously unrecognized functions of GiPCR signaling in the regulation of CBF and possibly systemic blood flow. Furthermore, extraneuronal functional PTX-induced non-cerebral GiPCR KO in combination with unilateral CCA produces brain lesions with similar imaging characteristics to human ischemic stroke.
One major drawback of the functional non-cerebral GiPCR KO with PTX is the ubiquitous nature of the KO in a multitude of systemic cellular processes. The systemic non-cerebral GiPCR KO may induce alterations in various systems, such as cardiovascular and immune system. In fact, it is used to establish the pertussis toxin-induced reversible encephalopathy dependent on monocyte chemoattractant protein-1 overexpression (PREMO) model, consisting on the injection of *Mycobacterium tuberculosis* and two injections of PTX [43]. We have previously shown that although PTX-sensitive Gi proteins are ubiquitously expressed, a single extraneuronal application of the toxin in vivo does not modify neuronal GiPCR and does not cross through the intact BBB [19]. Therefore, it is possible under this specific setting, to evaluate the effects of PTX in the perfusion of brain vessels and in systemic perfusion, without unwanted effects in neurons. We evaluated systemic hemodynamic effects using whole-body perfusion in order to reveal major possible alterations, and although we found only significant effects in the brain, other effects on systemic hemodynamics cannot be excluded. In fact, it has been demonstrated that PTX induces changes in blood pressure in hypertensive rats [2]. Moreover, it has been reported that in the cardiovascular system, PTX activity induces vessel size-dependent changes in vascular resistance [7], impairs endothelial Ca2+ influx [8], or lowers Ca2+ sensitivity of vasoconstriction in response to noradrenaline [2]. In line with these previous works, our findings now reveal a relevant effect in global cerebral hypoperfusion and microvascular collapse of cerebral vessels. The microvessel dysfunction could be at least partially mediated by interference with vascular Gi protein-mediated signaling affecting nitric oxide, β-adrenergic, angiotensin II type 1, serotonin-1A, or relaxin receptor function [2, 15, 18, 44]. Moreover, PTX has been shown to inhibit endothelium-dependent relaxation in hypercholesterolemic and atherosclerotic arteries [15, 16], which specifically links a disrupted G protein-mediated transduction to microvascular dysfunction. Indeed, chronic hypertension, dyslipidemias, diabetes, and increased age have been correlated to hypoperfusion and microarterial impairment [45–47].
Interestingly, PTX has been recently reported to be neuroprotective due to a reduction of glutamate-induced calcium influx into ischemic neurons [48]. Tang et al. injected PTX as a neuroprotectant at a dose of 40 µg/kg b.w. 30 min after applying a permanent middle cerebral artery occlusion. This occlusion triggered a BBB breakdown, allowing PTX to enter the brain [37, 49]. Consequently, Tang et al. injected PTX at a lower dose and at a time when the ischemic brain had a permeable BBB and could potentially benefit from inhibition of calcium influx. However, no perfusion deficits were observed in this study. In the current study, we administered the toxin again at 150 µg/kg b.w. 48 h before carotid artery occlusion; thus, the BBB was intact at the moment of PTX injection and not able to reach the neurons [19]. The comparison of the work from Tang et al. to our study is an excellent reminder of how timing and dosage of therapeutic interventions, especially in niche compartments, are important for outcome.
The significance of our findings in the clinical field is directly related to the involvement of G protein signaling alterations in the pathogenesis of neurodegenerative and cerebrovascular diseases. G protein signaling is involved with neurotransmitters such acetylcholine, GABA (gamma-aminobutyric acid), and glutamate. Here, for example, the acetylcholine receptor has been associated to formation of Aß peptide and neurofibrillary tangles in Alzheimer’s disease [50]. From a vascular perspective, alterations in G protein signaling involving monoamines such as adrenaline, noradrenaline, serotonin, dopamine, and histamine could be directly associated with cerebral hypoperfusion, a well-known imaging hallmark of neurodegenerative diseases [21, 51, 52]. Cerebral hypoperfusion is also a common risk factor in cerebrovascular diseases such as cerebral microbleeds and stroke [53, 54]. Therefore, GiPCR-driven signaling for the maintenance of CBF may be relevant to identify novel therapeutic targets. The PTX-triggered CBF impairment sensitized the brain to ischemic injury by disabling the mechanisms of blood flow regulation, an interesting effect that requires further mechanistic clarification focusing on the deficiency of specific G protein isoforms. The impaired hemodynamic stability and responsiveness of the cerebrovascular system caused by functional non-cerebral GiPCR KO in mice are reminiscent of observed hypoperfusion and vascular dysfunction in humans with chronic vascular disease, which is also predictive of human stroke severity [47].
Up to now, blocked GiPCR signaling had not yet been linked to the occurrence of cerebrovascular hypoperfusion and vascular collapse. It will be interesting to identify the specific GiPCRs involved in the maintenance of CBF and vascular tone. Furthermore, the effects of hypoperfusion and microvascular collapse induced by functional non-cerebral GiPCR KO may be useful for neuroscience, functional neuroimaging, and neurooncology.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (PDF 3.04 mb)
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|
---
title: A fabricated hydrogel of hyaluronic acid/curcumin shows super-activity to heal
the bacterial infected wound
authors:
- Maryam Khaleghi
- Fakhri Haghi
- Mina Gholami
- Hamdam Hourfar
- Farshad Shahi
- Ali Mir Mousavi Zekoloujeh
- Farhang Aliakbari
- Ebrahim Ahmadi
- Dina Morshedi
journal: AMB Express
year: 2023
pmcid: PMC10006388
doi: 10.1186/s13568-023-01533-y
license: CC BY 4.0
---
# A fabricated hydrogel of hyaluronic acid/curcumin shows super-activity to heal the bacterial infected wound
## Abstract
High risk of acute morbidities and even mortality from expanding the antibiotics resistant infectious wounds force indefinite efforts for development of high performance wound-healing materials. Herein, we design a procedure to fabricate a hyaluronic acid (HA)-based hydrogel to conjugate curcumin (Gel-H.P.Cur). The highlight of this work is to provide a favorite condition for capturing curcumin while protecting its structure and intensifying its activities because of the synchronization with HA. Accordingly, HA as a major component of dermis with a critical role in establishing skin health, could fortify the wound healing property as well as antibacterial activity of the hydrogel. Gel-H.P.Cur showed antibacterial properties against *Pseudomonas aeruginosa* (P. aeruginosa), which were examined by bactericidal efficiency, disk diffusion, anti-biofilm, and pyocyanin production assays. The effects of Gel-H.P.Cur on the inhibition of quorum sensing (QS) regulatory genes that contribute to expanding bacteria in the injured place was also significant. In addition, Gel-H.P.Cur showed high potential to heal the cutaneous wounds on the mouse excisional wound model with repairing histopathological damages rapidly and without scar. Taken together, the results strongly support Gel-H.P.Cur as a multipotent biomaterial for medical applications regarding the treatment of chronic, infected, and dehiscent wounds.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13568-023-01533-y.
## Introduction
Skin is the largest organ in the body and, as a natural barrier, protects humans from pathogens, physicochemical harms, and losing the body’s fluid. Every year, millions of people worldwide suffer from many acute and chronic skin disorders, especially due to impaired wound healing or failing to treat the resistant infectious disease (Wild et al. 2010).
Wound healing is a complex phenomenon, including inflammation, proliferation, and remodeling, which finally leads to skin regeneration (Sushma et al. 2018). The main threat during wound healing is infection with different pathogenic and opportunistic microorganisms such as *Pseudomonas aeruginosa* (P. aeruginosa), *Staphylococcus aureus* (S. aureus), and *Escherichia coli* (E. coli) (Qureshi et al. 2015). Different medicinal approaches have been recommended to protect the injured skin, such as wound dressings (Chen et al. 2018), however, traditional wound dressings (gauze, bandage etc.) has some shortcomings such as low capacity for the wound exudate adsorption, strong adhesion and skin-stripping after frequent replacement of the dressing (Lawton and Langøen 2009, Qureshi et al. 2015). Wound exudates contain proteases that induce more damages in the surrounding epidermis and subsequently prolong the healing process, as well as promote the growth of alkalophilic microorganisms (Greener et al. 2005). Modern medicine tries to develop new strategies for wound dressing by using new gentle materials like hydrogels. While hydrogels have high capacity for absorbing wound exudates, they can provide the essential moisture for the wound healing process (Zhao et al. 2017), protect skin against invasive microorganisms, afford appropriate dynamicity at the treated site, and good permeability to oxygen with its porous structure (Qu et al. 2019).
As a natural polymer, hyaluronic acid (HA) has gained increasing attention to be used in the therapeutic hydrogels, especially for wound dressing because of its biocompatibility, biodegradability, high capacity for adsorption of aqueous solutions, and its natural role in the healing process (Jeong et al. 2018, Frenkel 2014). In addition, the role of HA against microorganisms, particularly its anti-adhesion and anti-biofilm properties, has been of interest (Romanò et al. 2017). Nonetheless, poor mechanical strength in the hydrated state limits the biomedical applications of HA (Jeong et al. 2018). Cross-linking with appropriate cross-linkers is one of the strategies to improve HA’s mechanical properties. Cross-linkers are small and multi-functional molecules that can react with the functional groups of HA (hydroxyl, carboxylic acid, or amino groups) (Additional file 1: Fig. S1A) (Khaleghi et al. 2020). Using pre-functionalized polymers is another strategy that eliminates the use of cross-linkers. Polydimethylsiloxane-diglycidyl ether terminated (PDMS-DG) with epoxy group at its ends (bis-epoxide PDMS) (Additional file 1: Fig. S1B), is a non-toxic, biocompatible, gas permeable, transparent, and flexible polymer that is used to fabricate bio-medical devices (Halldorsson et al. 2015).
It believes that overuse of antibiotics, in addition to antibiotic resistance, can be toxic for the vulnerable and sensitive skin cells growing around the wound and delay the wound healing process. Plants are sources of the safe and affordable remedial compounds that stimulate the healing process (Qureshi et al. 2015). One of the most used herbal compounds in traditional and modern medicine is curcumin or diferuloylmethane [1, 7-bis (4-hydroxy-3-methoxyphenyl)-1, 6-heptadiene-3, 5-dione] with numerous pharmacological effects, including antioxidant, anti-inflammatory, anti-mutagenic, anti-cancer and anti-amyloid fibrillation activities (Tafvizizavareh et al. 2019; Hewlings and Kalman 2017). Curcumin is a natural small molecule belonging to the group of polyphenolic curcuminoids, with a bright yellow color. Curcumin is confronted with a wide range of microbes, including bacteria (both Gram-positive and Gram-negative), viruses, and also fungi (Zorofchian Moghadamtousi et al. 2014). Curcumin is a valuable candidate to use for wound healing because it is safe even at high doses (12 gr/day) and used as a FDA approved drug widely (Zorofchian Moghadamtousi et al. 2014; Hewlings and Kalman 2017). However, due to its low solubility and stability in the free form at physiological condition, some strategies such as using nano carriers, encapsulating within colloidal particles and synthesizing its some derivatives/conjugates have been employed thus far (Zheng and McClements 2020; Taebnia et al. 2016).
In the present study, in order to develop a curcumin-supplied hyaluronic based hydrogel for wound healing purposes we cross-linked PDMS-DG to HA via epoxy-OH mediated polymer–polymer reaction (Khaleghi et al. 2020) and then we successfully ameliorated the wound healing properties of the hydrogel because of loading high amount of curcumin in its native form. Next to analyzing the physicochemical characters of the curcumin-loaded hydrogel (Gel-H.P.Cur), its antibacterial and wound healing potential was examined using the in vitro and in vivo methods.
## Materials
Curcumin was purchased from Acros organic (Geel, Belgium). Polydimethylsiloxane-diglycidyl ether terminated (epoxy terminated PDMS or PDMS-DG, 480282-50ML, average Mn ~ 800, Japan), DMSO and all other chemicals were from Sigma-Aldrich. Dry powder of sodium hyaluronate (average molecular weight = 50000 Da) was purchased from BulkActives (Taiwan).
## Fabrication of the curcumin-loaded hydrogel (Gel-H.P.Cur)
To produce the curcumin-loaded hydrogel (Gel-H.P.Cur), at the first step, the HA-PDMS hydrogel was produced as discussed previously in detail with some modifications (Khaleghi et al. 2020). 100 mg (0.128 mmol) of HA powder dissolved in 0.5 mL distilled water plus 50 µL of NaOH (0.2 N, pH > 10). In another container, the PDMS solution was prepared by adding 200 µL of DMSO and 50 µL of NaOH (0.2 N, pH > 10) to 1 mL of PDMS-DG (1.23 mmol) and mixed thoroughly by mechanical agitation for 60 s and then left for 30 min. After that, the PDMS solution was added to the HA solution, stirred for 120 s, and the mixture incubated at 37 °C for 2 h. After removing the non-reacted part, the resulting hydrogel (Gel-H.P) was kept at 25 °C with an open door for 48 h. Then, 0.5 mL of curcumin solution (10 mg/mL, dissolved in DMSO) was added to the semi-dried hydrogel and the mixture was incubated at room temperature (RT) till no more curcumin adsorbed (measuring the absorbance of solution). Finally, the immersed hydrogel containing curcumin (Gel-H.P.Cur) was stored at RT for further analysis. To ascertain whether DMSO plays a role in the antibacterial effect of Gel-H.P.Cur, semi dried Gel-H.P was immersed in 0.5 mL of DMSO (without curcumin) for 48 h (Gel-H.P.DMSO). Then the Gel-H.P.DMSO was stored at RT for antibacterial assay.
## Nuclear magnetic resonance spectroscopy (NMR)
Gel-H.P dialyzed for 24 h against distilled water and then lyophilized. The lyophilized hydrogel dissolved in D2O and was applied for NMR analysis using a Bruker Avance 250 MHZ spectrometer. Gel-H.P.Cur was immersed in DMSO for 48 h and then the NMR of released curcumin in DMSO was studied. The NMR data was processed using MestReNova software.
## Fourier-transformed infrared (FTIR) and attenuated total reflectance (ATR) FTIR analyses
FTIR spectrum of curcumin was recorded using a Nicolet iS 10 FTIR spectrometer after preparing the sample using the KBr pellet method. ATR-FTIR spectra of Gel-H.P and Gel-H.P.Cur were obtained using the ATR (Bruker, Tensor27, Equinox55). All spectra were recorded at 1 cm−1 resolution in the range of 600–4000 cm−1.
## Scanning electron microscopy (SEM)
The microstructure of the gels (Gel-H.P and Gel-H.P.Cur) were compared using a TESCAN mira2 (the Czech Republic) SEM, according to the following steps: first, the hydrogels were fully swollen in distilled water, then the hydrogels were lyophilized and immediately coated with gold.
## Swelling index determination
The pre-weighted (Wd) dried hydrogel (Gel-H.P.Cur) was immersed in distilled water at 37 °C and at predetermined time intervals, its swollen form was weighted (Ws) after removing the excess water using tissue paper. Swelling measurement was continued until no difference was observed between two consecutive Ws. The degree of swelling obtained using Eq. [ 1]:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Degree of swelling }}\left({\text{\% }} \right) = \frac{{{\text{Ws }} - {\text{Wd}}}}{{{\text{Wd}}}}{ } \times 100$$\end{document}Degree of swelling\%=Ws-WdWd×100
## Bacterial strain and its growth condition
P. aeruginosa PAO1 as the wild type strain was donated from the department of microbiology, school of medicine, Zanjan University of medical science. Cultivation of P. aeruginosa was carried out using Mueller Hinton (MH) or Luria Bertani (LB) broth/agar at 37 °C. P. aeruginosa was stocked in trypticase soy broth (TSB) containing $20\%$ (v/v) glycerol and stored at − 70 °C for further studies.
## Assessing the bactericidal efficiency of the fabricated hydrogels by dilution and disc diffusion methods
Cell growth inhibitory effect of Gel-H.P, Gel-H.P.Cur and Gel-H.P.DMSO against P. aeruginosa was investigated using the broth dilution method, according to the Clinical and Laboratory Standards Institute (CLSI) guidelines (CLSI 2015). Briefly, the ODs of the cultures were recorded at 620 nm (LKB Biochrom 4050, Ultraspec II UV/Vis spectrophotometer) after treating P. aeruginosa (1.5 × 105 colony-forming unit (CFU)/mL) with different concentrations (0.078–5 mg/mL) of the sterilized (by UV irradiation for 30 min) Gel-H.P, Gel-H.P.Cur or Gel-H.P.DMSO for 18 h at 37 °C. The bactericidal activities of the hydrogels were quantified using Eq. [ 2] (OD values were the mean of three measurements). Besides, the cultured bacteria without the hydrogels and the media without any bacteria were considered as positive and negative controls, respectively.2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Bactericidal activities \% }} = 1 - \frac{{{\text{OD}}620{ }\left({\text{treated culture}} \right){ }}}{{{\text{OD}}620{ }\,\left({\text{untreated culture}} \right)}}{ } \times 100$$\end{document}Bactericidal activities \%=1-OD620treated cultureOD620untreated culture×100 In the disc diffusion method, P. aeruginosa with inoculum concentrations of 108 CFU/mL was spread on Mueller Hinton agar petri-dish, and a piece of each hydrogel (5 mg) was placed on the plate. After incubation for 18 h at 37 °C, the zone of growth inhibition was measured using a ruler (Imipenem disc (10 μg, Mast Group Ltd., Merseyside, UK), was used as standard control).
## Assessing the biofilm formation
The effects of Gel-H.P and Gel-H.P.Cur on the biofilm formation of P. aeruginosa monitored by the microtitre plate based on crystal violet assay (Noshiranzadeh et al. 2017). The overnight cultures incubated with different concentrations (2.5, 5, and 10 mg/mL) of Gel-H.P and Gel-H.P.Cur at 37 °C for 18 h (in order to prepare different concentrations of hydrogels, solid hydrogels were dissolved in MH medium). After removing free planktonic cells, the plates were washed three times with phosphate-buffered saline (PBS) and fixed with 150 µL of methanol. The plates were stained with crystal violet ($0.5\%$ w/v) for 10 min. After washing off the excess dye with distilled water, glacial acetic acid was added and incubated for 20 min to solubilize crystal violet. Finally, the absorbance was read at 570 nm in an absorbance microplate reader (Biotek microplate reader ELx-808, USA). Bacteria without hydrogels and the media without any bacteria were considered as positive and negative controls, respectively. OD values were the mean of three measurements that converted to % of inhibition of biofilm formation by Eq. [ 3].3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{\% of inhibition of biofilm formation }} = 1 - \frac{{{\text{A}}570{\text{ of treated PAO}}1}}{{{\text{A}}570{\text{ of untreated PAO}}1}}{ } \times 100$$\end{document}\% of inhibition of biofilm formation=1-A570of treated PAO1A570of untreated PAO1×100
## Effects of the synthesized hydrogels on the pyocyanin production by P. aeruginosa
Pyocyanin production as a virulence factor of P. aeruginosa was measured based on the method described previously (Heidari et al. 2017). In this regard, 500 µL of bacteria (1 × 108 CFU/mL) were cultured in the presence of Gel-H.P.Cur (2.5, 5, 10 mg/mL) or Gel-H.P (10 mg/mL) in a final volume of 10 mL (LB broth medium) at 37 °C for 24 h. To extract pyocyanin, 3 mL chloroform was mixed with 5 mL of the supernatant, after centrifugation of the culture at 8000 rpm for 5 min. After forming two phases, the chloroform phase extracted and transferred to another test tube contained 1 mL HCl (0.2 M). The solution was centrifuged at 12,000 rpm at 4 °C for 10 min and then the absorbance of the supernatant was recorded at 520 nm. The final concentration of pyocyanin was calculated by Eq. [ 4] (Absorbance values were the mean of three measurements).4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left[{{\text{Pyocyanin}}} \right] \, = {\text{ A52}}0 \, \times { 17}.0{7}$$\end{document}Pyocyanin=A520×17.07
## RNA extraction and cDNA synthesis
At the first step using RNeasy Mini Kit (QIAGEN, Hilden, Germany), total RNA was extracted from PAO1 cultivated broth culture at the exponential growth phase in the presence of Gel-H.P, Gel-H.P.Cur (10 mg/mL) or alone (as control). The concentration and purity of the extracted RNA were evaluated with NanoDropTM ND-1000 spectrophotometer (Nano-Drop Technologies, Wilmington, DE) at 260 nm and $\frac{260}{280}$ nm, respectively. To synthesis cDNA, we employed a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA). Reverse transcription (RT) was performed in a reaction mixture with a total volume of 20 mL containing 10 mL of RNA (800 ng), 2 mL of RT buffer (10x), 0.8 mL of deoxynucleoside triphosphate (25x), 2 mL of RT random primers (100 mM) and 1 mL of reverse transcriptase (1 U). The reactions were incubated at 25 °C for 10 min, at 37 °C for 120 min, at 85 °C for 5 min and at 4 °C for 10 min.
## Real-time quantitative PCR (qPCR)
To explore the effect of Gel-H.P and Gel-H.P.Cur (10 mg/mL) on the expression of QS circuit genes including lasI, lasR, rhlI and rhlR, we carried out real-time qPCR using the primers listed in Additional file 1: Table S1 (Bahari et al. 2017).
Each primer was mixed with 10 mL of 2 × SYBR Green PCR Master Mix (Exiqon, Denmark). Assays performed by a Rotor Gene-6000 real-time analyzer (Corbett Research, Qiagen) in triplicate. All data were normalized to the internal standard oprL (encoding the outer membrane protein OprL), and the melting curve analysis demonstrated that the accumulation of SYBR Green-bound DNA was target gene-specific. The negative control with no treatment was included in the experiments. To determine the threshold cycle values (Ct), the Ct for amplification of each gene was normalized to the Ct of the amplified oprL’s gene in the related sample. Then, ΔCt values were compared with the control without any treatment based on Eq. [ 5].5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{gathered} \Delta {\text{Ct }}\,{\text{sample }} = {\text{ Ct }}\,{\text{sample}}\, - {\text{Ct }}\,\,{\text{oprL}}\,\,{\text{ sample}} \hfill \\ \Delta {\text{Ct control }} = {\text{ Ct }}\,{\text{control}} - \,{\text{Ct oprL}}\,{\text{ control}} \hfill \\ \end{gathered}$$\end{document}ΔCtsample=Ctsample-CtoprLsampleΔCt control=Ctcontrol-Ct oprLcontrol
## Wound closure
The wound healing capabilities of Gel-H.P and Gel-H.P.Cur were evaluated using a mouse excisional wound model (BALB/c). Six-eight week-old male mice were randomly divided into 6 groups (Three mice in each group) (Table 1); Group1: treated only with PBS (100 µL), Group 2: treated with Gel-H.P, Group 3: treated with Gel-H.P.Cur, Group 4: infected with 100 µL of P. aeruginosa (0.5 McFarland) without any treatment, Group 5: infected with 100 µL of P. aeruginosa (0.5 McFarland) and also treated with Gel-H.P, Group 6: infected with 100 µL of P. aeruginosa (0.5 McFarland) and also treated with Gel-H.P.Cur. Treatment was performed with 100 µL of the hydrogels (10 mg/mL) dissolved in PBS. After anesthetization deeply by injection of a mixture of ketamine (50 mg/kg of body weight) and xylazine (5 mg/kg of body weight), the dorsum of the mice were shaved, disinfected using $70\%$ ethanol solution, and punched (in diameter of 15 mm) by employing sterile scissors. Treatments (using PBS, Gel-H.P, Gel-H.P.Cur, or P. aeruginosa) applied only on the day of surgery and did not repeat over the next 15 days. On the 3rd, 6th, 9th, 12th, and 15th days after wounding, we took photographs of wounds by a digital camera. The percentage of the wound closure was calculated by Eq. [ 6], in which Ai and At are initial wound area and wound area each day, respectively. The size of the wound area was calculated using ImageJ software.6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Wound closure }}\left({\text{\% }} \right) = \frac{{\left({{\text{Ai }}{-}{\text{ At}}} \right)}}{{\text{Ai }}}{ } \times { }100$$\end{document}Wound closure\%=Ai-AtAi×100Table 1Details of each group of incision wound model miceNumberGroupTreatment agentInfection1PBSPBS–2Gel-H.PGel-H.P–3Gel-H.P.CurGel-H.P.Cur–4PAO1– + 5PAO1 + Gel-H.PGel-H.P + 6PAO1 + Gel-H.P.CurGel-H.P.Cur + Finally, on the day 15th, mice were sacrificed for histopathological analysis.
The animal studies carried out here, were approved by the National Institutes of Genetic Engineering and Biotechnology (NIGEB) Animal Care Committee (IR.NIGEB.EC.1399.4.9.D).
## Histopathological study
For preparation of the histological slides, the wound sites and the surrounding areas (groups 4 and 6, Table 1) were excised and fixed in $10\%$ formalin. The biopsies were embedded in paraffin wax, sectioned by microtome (Leica, RM, 2135, USA), and stained with hematoxylin and eosin (H&E) according to the standard method (Shefa et al. 2020, 2017).
## Statistical analysis
All experiments were carried out in triplicate, and the results presented as means ± SD. The statistical significance within the groups was analyzed using One-way ANOVA. The significance outcome between the groups was also computed using unpaired Student’s t-test (P value < 0.05 was considered significant).
## Appropriate conditions for the fabrication of Gel-H.P with curcumin (Gel-H.P.Cur)
At the first step, we synthesized a stable, biocompatible porous hydrogel composed of HA and PDMS-DG based on our recently developed method with slight modifications which was addressed in the method section (Khaleghi et al. 2020). After soaking the semi-dried hydrogel in the curcumin solution (10 mg/mL, dissolved in DMSO), curcumin absorbed and then permeated into the inner parts of the Gel-H.P, which was accompanied by turning the color of the hydrogel uniformly to yellow (Gel-H.P.Cur). Figure 1a, b schematically shows synthesis steps of Gel-H.P.Cur which consist of the final product in the adequate and inadequate conditions. Curcumin undergoes severe discoloration (brown to black) when exposed to alkaline conditions and is prone to structural degradation (Kharat et al. 2017; Kumavat et al. 2013). Therefore, in this study, the conditions leading to the black hydrogel withdrew and the analyses were followed by the yellow hydrogel. Fig. 1Synthesis of Gel-H.P and Gel-H.P.Cur. a Schematic and b Original materials representation. HA was incubated at basic pH, and the interaction of epoxide groups with the HA hydroxyl groups leads to ether bond formation. This cross-linking reaction created the HA-PDMS 3D hydrogel network. By adsorbing and penetrating curcumin in the Gel-H.P (at appropriate pH condition) the color completely converted to yellow even in the inner parts of Gel-H.P.Cur NMR, FTIR, SEM as well as swelling measurements were employed to identify the properties of the fabricated Gel-H.P.Cur. NMR data for Gel-H.P has been reported previously; however, a summary of the characteristic peaks of NMR has shown in Additional file 1: Table S2 (Khaleghi et al. 2020). Moreover, to declare that curcumin remained in its native form during the synthesis process, the NMR spectra of pure curcumin and the released curcumin from the hydrogel were analyzed (Fig. 2a, b). In NMR spectra, we also detected some traces of hyaluronic acid (CH3 of n-acetyl glucosamine, 1.9 ppm) and PDMS-DG (Si-CH3, -0.1–0.1 ppm) (Fig. 2b, c).Fig. 2Hydrogels characterization using NMR, FTIR, SEM and swelling assay. a 1H NMR spectrum of the pure curcumin in DMSO with specific peaks for the methoxy group (I), hydroxyl group (II) and other protons around the phenyl ring (II). b 1H NMR spectrum of the curcumin released from Gel-H.P.Cur with specific peaks of pure curcumin (I, II, III). Traces of hyaluronic acid (1: CH3 of N-acetyl glucosamine and 2, 3: protons around the sugar ring) and PDMS (a: Si-CH3: and g, h: CH2) were detected in the spectrum. The red numbers in the chemical structure represented the chemical groups detected by NMR. c NMR characteristic peaks of pure curcumin and the curcumin released from Gel-H.P.Cur. d FTIR spectra of Gel-H.P, Gel-H.P.Cur, and Curcumin. Characteristic peaks of HA (~ 1400 cm−1 related to carboxylic acid: O–H bend), PDMS-DG (~ 1260 cm−1 related to Si–C) and curcumin (1231 cm−1: phenolic C–O group and 1498 cm−1: aromatic C=O band stretching vibration) were detected. e–h SEM micrographs of the hydrogels: e and f are micrographs for Gel-H.P, and g and h are for Gel-H.P.Cur. Imaging was performed after coating the lyophilized hydrogels with gold. The detail of magnification and scale bars are demonstrated under each image. i Assessment of the swelling rate of the Gel-H.P.Cur in distilled water at 37 °C. Gel-H.P.Cur showed a high level of water absorption FTIR data showed the typical peaks of HA and PDMS-DG in both hydrogels (~ 1400 cm−1 related to carboxylic acid: O–H bend and ~ 1260 cm−1 related to Si–C) (Fig. 2d). The curcumin characteristic peaks have also appeared in 1231 cm−1: phenolic C–O group and 1498 cm−1: aromatic C=O band stretching vibration.
To verify the microstructure of the hydrogels, SEM imaging was also carried out (Fig. 2e–h). Gel-H.P (Fig. 2e, f) showed a more porous structure than Gel-H.P.Cur (Fig. 2g, h). However, the rate of swelling with absorbing more than $469\%$ (w/w) of water during 330 min indicated that the fabricated composite including curcumin capable to uptake high amount of aqueous materials (Fig. 2i).
## Gel-H.P.Cur exhibited inhibitory effects on the growth of P. aeruginosa
We determined whether there was a significant reduction in the growth rate of P. aeruginosa when treated with Gel-H.P.Cur (treatment with 0.078–5 mg/mL of Gel-H.P.Cur: Reduction of bacteria from the concentration of 16.8 × 108 CFU/mL to 12.3–3.1 × 108 CFU/mL) (Fig. 3a). Note that Gel-H.P and Gel-H.P.DMSO showed no significant inhibitory effect against P. aeruginosa (Fig. 3b, c). We observed about a 2.62 ± 0.59 cm2 zone of growth inhibition in the disk diffusion test around the Gel-H.P.Cur on MHA plate pre-covered with P. aeruginosa (Fig. 3d and f) but not for Gel-H.P (Fig. 3e). These results could reflect the diffusion ability of curcumin in a solid surface even after its entrapping through the fabricated hydrogel. The zone of growth inhibition produced by the imipenem disc, as a standard control, was about 3.5 cm2 (Fig. 3g).Fig. 3In vitro study of antibacterial activity of the hydrogels. a–c Serial dilution method: Inhibitory effect of different concentrations of Gel-H.P.Cur, Gel-H.P and Gel-H.P.DMSO on P. aeruginosa growth in MH broth during 18 h at 37 °C (mean ± SD, * $P \leq 0.05$, ** $P \leq 0.01$, *** $P \leq 0.001$). ( d–g) Disc diffusion method: The zone formation appeared from the growth inhibition around Gel-H.P.Cur (d and f) and imipenem disc (g) but not Gel-H.P (e) on MH agar plate. The zone of growth inhibition was measured (cm.2) and recorded using a ruler and a digital camera, respectively. h Assessment of P. aeruginosa biofilm formation in the presence of the hydrogels, using crystal violet staining standard method. Inhibitory effect of different concentrations (2.5, 5 and 10 mg/mL) of Gel-H.P or Gel-H.P.Cur on biofilm formation of P. aeruginosa was assayed after 18 h incubation in MH broth at 37 °C. *: statistically significant differences between treated and control groups. Ø: sstatistically significant differences between Gel-H.P and Gel-H.P.Cur with the same concentration (2.5, 5 or 10 mg/mL). Ɛ: statistically significant differences between Gel-H.P (2.5 mg/mL) and Gel-H.P (5 mg/mL), Gel-H.P (2.5 mg/mL) and Gel-H.P (10 mg/mL), Gel-H.P (5 mg/mL) and Gel-H.P (10 mg/mL). Ψ: statistically significant differences between Gel-H.P.Cur (2.5 mg/mL) and Gel-H.P.Cur (10 mg/mL), Gel-H.P.Cur (5 mg/mL) and Gel-H.P.Cur (10 mg/mL). ( Mean ± SD, **, ƐƐ, ΨΨ $P \leq 0.01$, ***, ØØØ, ƐƐƐ, ΨΨΨ $P \leq 0.001$). ( i) Inhibitory effect of different concentrations of Gel-H.P (10 mg/mL) or Gel-H.P.Cur (2.5, 5 and 10 mg/mL) on pyocyanin production of P. aeruginosa during 24 h incubation in LB broth at 37 °C. *: statistically significant differences between treated and control groups. Ø: statistically significant differences between Gel-H.P (10 mg/mL) and Gel-H.P.Cur (with different concentrations: 2.5, 5 or 10 mg/mL). Ɛ: statistically significant differences between Gel-H.P.Cur (2.5 mg/mL) and Gel-H.P.Cur (5 mg/mL), Gel-H.P.Cur (5 mg/mL) and Gel-H.P.Cur (10 mg/mL). Ψ: statistically significant differences between Gel-H.P.Cur (2.5 mg/mL) and Gel-H.P.Cur (10 mg/mL). ( Mean ± SD, Ɛ $P \leq 0.05$, ***, ØØØ, ΨΨΨ $P \leq 0.001$). ( j and k) Effect of hydrogels (Gel-H.P and Gel-H.P.Cur) at a concentration of 10 mg/mL on the expression level of quorum sensing (QS) regulatory genes (f: lasI/lasR, g: rhlI/rhlR) in P. aeruginosa during 12 h incubation. Both hydrogels significantly reduced the expression of QS genes compared to the control (untreated) group (mean ± SD, * $P \leq 0.05$, ** $P \leq 0.01$, *** $P \leq 0.001$)
## Biofilm formation, Pyocyanin production as well as the Quorum Sensing (QS) regulatory genes expression in P. aeruginosa decreased by both hydrogels (Gel-H.P and Gel-H.P.Cur)
Both hydrogels (Gel-H.P and Gel-H.P.Cur) decreased the biofilm formation remarkably (Fig. 3h). However, in the presence of curcumin the anti-biofilm effect obviously increased which could reflect the synergistic impacts of HA and curcumin.
Pyocyanin is a non-fluorescent blue-green pigment produced by P. aeruginosa, which has an important role in the infectious potential of P. aeruginosa (Vinckx 2010). Both hydrogels significantly reduced pyocyanin production with a maximum reduction of $89.92\%$ and $30.6\%$ for Gel-H.P.Cur and Gel-H.P, respectively (Fig. 3i).
Quorum sensing (QS) is a cell-to-cell signaling system that controls virulence factors, antibiotic resistance, and biofilm formation in bacteria by regulating gene expression in response to the cell density (Bassler and Losick 2006; El-Mowafy et al. 2014). Pseudomonas has three distinct QS systems including las, rhl and MvfR (PqsR), which are mediated by small signal molecules called autoinducers. The lasI and rhlI products, direct the synthesis of autoinducers called n- (3-oxo-dodecanoyl) -l-homoserin lactone (3-oxo-C12-AHL) and n- (butanoyl) -l-homoserine lactone (C4-AHL), respectively. These diffusible signaling molecules can interact with lasR and rhlR (respectively) to activate the target promoters (El-Mowafy et al. 2014, Venturi 2006). By using real-time PCR, the effects of Gel-H.P and Gel-H.P.Cur on the expression of QS regulatory genes including lasI/lasR and rhlI/rhlR were investigated. In the presence of the hydrogels, the expression of all genes was significantly reduced (Fig. 3j, k).
## Gel-H.P and Gel-H.P.Cur rebuilt the infected wounds
To screen the wound healing activity of the synthesized hydrogels in an extensive skin lesions infected with P. aeruginosa as real samples, we used a full-thickness incision wound model in mice. The wound healing process followed for up to 2 weeks (Fig. 4a). Quantitative data on day 12th revealed that the group treated with Gel-H.P.Cur (without bacterial infection, group 3: Gel-H.P.Cur, Table 1) and the group infected with P. aeruginosa (without hydrogel application, group 4: PAO1, Table 1) had the highest ($89.85\%$ ± 4.35) and the lowest ($54.51\%$ ± 2.2) wound closure, respectively (Fig. 4b). Both HA and curcumin might have remediation roles on the wounds. Fig. 4Macroscopic and microscopic photographs of wound healing. a Wound healing appearances in all control and treated (Gel-H.P or Gel-H.P.Cur/infected or non-infected) groups (Three mice in each group) on days 0, 3, 6, 9, 12 and 15 were recorded using a digital camera. All of the treatments were performed only once on wounding day (day 0) and the healing process was followed until day 15. b Wound closure rate (%) of 6 groups on the day 12 next to post wounding. Wound area was measured using Image J software and the ratio of the wound size on the 12th to the zero days was calculated (mean ± SD, * $P \leq 0.05$, ** $P \leq 0.01$, *** $P \leq 0.001$). c–n Histopathological evaluation of the wounds using H&E staining on day 15 post-wounding. c, d, e, f, g and h are the control group (Table 1, group 4, P. aeruginosa: infected with 100 µL of P. aeruginosa (0.5 McFarland) without any treatment), while i, j, k, l, m and n are the treated group (Table 1, group 6, PAO1 + Gel-H.P.Cur: infected with 100 µL of P. aeruginosa (0.5 McFarland) + treated with Gel H.P.Cur). EP epidermis, DE dermis, HYP hypodermis, SG sebaceous glands, yellow arrows: EP, DE or HYP, blue arrows: hair follicles, green arrows: blood vessels *Histopathological analysis* indicated that treatment with Gel-H.P.Cur improved the restoration of natural tissue structure in the injured skin on the day 15th post-wounding (Fig. 4c–n). Epidermis (EP: yellow arrow) and dermis (DE: yellow arrow) were traceable in untreated and treated skin with Gel-H.P.Cur; however, hypodermis (low density tissue but contains a lot of fat) was only seen in treated skin (HYP: yellow arrow). Although some of cutaneous annexes such as sebaceous glands (SG) and many immature hair follicles (blue arrows) observed in the untreated skin, SG, adult hair follicles (blue arrows) and blood vessels (green arrows) were generated in the treated skin.
## Discussion
Turmeric is definitely one of the oldest herbal extracts with well-known anti-inflammatory, antimicrobial, antioxidant, chemopreventive and chemotherapeutic properties (Sharifi-Rad et al. 2020; Zorofchian Moghadamtousi et al. 2014). This traditional spice has been used in the treatment of various diseases in many Asian countries for thousands of years (Ammon and Wahl 1991). After the discovery of curcumin as the main active ingredient of turmeric, studies have shifted on its activities. For topical treatment of cutaneous wound healing however, combining curcumin with the materials that provide expanded surfaces, like bandages and films are highly recommended (Mohanty and Sahoo 2017). Also, hydrogels with characteristics such as porous three-dimensional structure, permeability to oxygen, swelling or liquid absorption, mechanical flexibility, and no secondary damage in the wound can be a suitable choice for the new wound dressings development (Liu et al. 2022). Recently, biomedical researchers have focused on the synthesis of antibacterial hydrogels to develop effective wound dressings (Rahmani et al. 2021; Shang et al. 2022). Therefore, in this study for the cutaneous wound healing purpose, we chose cross-linked HA hydrogel. We have already shown that applying an epoxy compound (Polydimethylsiloxane-diglycidyl ether terminated) provided a porous HA-based hydrogel with high rate of swelling (up to $500\%$), with a very good resistance versus enzymatic and chemical degradations (Khaleghi et al. 2020). However, during combination with curcumin, the color of curcumin converted to dark brown due to the fabrication condition. It is indicated that curcumin mostly is in its enolate form at alkaline condition which is in labile and unstable tautomeric form and its color rapidly converts from bright yellow to dark brown (Bhatia et al. 2016). By modifying the synthesis process of HA-PDMS hydrogel and condition for combining of curcumin, the color of the composite remains in light yellow color.
NMR and FTIR data confirmed that the structure of curcumin remained stable during the incorporating process. The specific peaks of curcumin including methoxy (− OCH3, 3.8 ppm), hydroxyl (−OH, 7.5 ppm) and side protons around the phenyl ring (C–H, 6.6–8.4 ppm) were observed in the NMR spectra of free curcumin as well as when it released from Gel-H.P.Cur. Moreover, the FTIR spectra indicated the characteristic peaks of HA and PDMS-DG as well as curcumin. The data fortified the assumption that the incorporation of curcumin in Gel-H.P probably is a weak chemical or just physical adsorption with no considerable changes in the main peaks of the all three components. For our related purpose, it is better that the incorporated compound does not interact strongly with the hydrogel matrix and is easily released to the media. SEM micrographs showed the porous and net-like topology of the hydrogels. The pores of the Gel-H.P surface were much clearer and more regular, however, in the case of Gel-H.P.Cur, the pores were less regular which could be due to the filling with curcumin. It should be noted, while curcumin releases, the pores could absorb the wound secretions instead. It should be noted that the effect of the cross-linking process on the porosity and swelling properties of the hydrogel has been explained in detail in the other study (Khaleghi et al. 2020). High potential to trap the aqueous materials is another critical factor for wound dressings mainly in two respects including adsorption of the excess wound exudates and prevention of the secondary infections (Wahid et al. 2016; El-Kased et al. 2017). Besides, hydrogels with high swelling values (such as HA-based hydrogels) provide a hydrated microenvironment similar to natural milieu for arranging the migration of dermal cells (i.e., fibroblasts, keratinocytes, or endothelial cells) in the wound bed and subsequently inducing wound closure and the tissue regeneration (Zhu et al. 2006, Chen 2002). Furthermore, the capacity of hydrogels for entrapment of drugs directly relates to their swelling capability (Marulasiddeshwara et al. 2020). We determined that Gel-H.P.Cur had a high degree of swelling (up to $500\%$ w/w) which was mostly similar to Gel-H.P’s swelling rate.
To examine the antibacterial activities of Gel-H.P and Gel-H.P.Cur in the in vitro models, P. aeruginosa was chosen, since it is one of the most abundant Gram-negative bacteria in the infections such as bloodstream infections, pneumonia, urinary tract infections, and skin infections especially surgical wound infections that can cause severe difficulties and even death, particularly in the immunosuppressed patients (Moore and Flaws 2011; Paprocka et al. 2022). Antibiotic selection for P. aeruginosa treatment is complicated and has become a major concern for the word-wide health issues because it involves many multidrug-resistant strains (Amini and Namvar 2019). In this study, we observed the inhibitory effect of Gel-H.P.Cur on the growth of P. aeruginosa using liquid culturing and disk diffusion methods, whereas we did not perceive the obvious effect of Gel-H.P on the growth of the bacteria. Disk diffusion method revealed that curcumin can diffuse in media from the fabricated hydrogel, however, according to some studies, it may also work even in its incorporated state (Taebnia et al. 2016). The antibacterial activity of HA/Cur composite has also been investigated in a recent study. Using disc diffusion and minimal inhibitory concentration assay methods, Snetkov et al. showed HA/Cur composite nanofibers have an inhibitory effect on a variety of microbial strains, especially Gram-positive bacteria (Snetkov et al. 2022).
Although Gel-H.P did not attenuate the growth of P. aeruginosa considerably, we saw that it could boost curcumin performances against some activities of P. aeruginosa such as biofilm formation, pyocyanin production, and the quorum sensing (QS) regulatory genes expressions, all of which are the pathogenicity and invasion factors in bacteria. The formation of biofilm causes bacterial resistance to the host immune system, antimicrobial agents, UV light exposure, chemical disinfectants, and temperature stress (Dastgheyb et al. 2015). Blocking or even disturbing biofilm formation may be a valuable and significant step in the treatment of microbial infection. In this study, Gel-H.P without curcumin showed anti-biofilm impact. The anti-biofilm activity of HA as the major part of Gel-H.P, has already been determined (Drago et al. 2014). There are some studies indicating that some wound healing dressings are prone to contamination (Lei et al. 2019). However, as an achievements of our study, no contamination of Gel-H.P.Cur for long time (7 months) was detected suggesting that the presence of curcumin may amplify anticontamination property of the fabricated hydrogel (Khaleghi et al. 2020).
We also determined the positive effect of Gel-H.P on the curcumin activities against production of pyocyanin by P. aeruginosa. Pyocyanin is a water-soluble greenish-yellow secondary metabolite that stimulates P. aeruginosa to resist humane immune responses (Ran et al. 2003, Vinckx, 2010). Curcumin has shown the inhibitory effect on the biosynthesis of pyocyanin. It has been shown that treatment with curcumin (1.5–3 µg/mL) reduced the pyocyanin production in P. aeruginosa by $80\%$ (Rudrappa and Bais 2008). Here we found for the first time that Gel-H.P (without curcumin) significantly inhibited pyocyanin production in a dose dependent manner. It is a very important activity regarding P. aeruginosa pathogenicity as an opportunistic bacterium. One of strategies that P. aeruginosa employs is producing these kinds of metabolites to sweat out the opponents (usually local natural flour) and weakening the host immune responses.
Another strategy that guarantees the survival and spread of P. aeruginosa is its advanced communication system, quorum sensing (QS) system that can induce biofilm or pyocyanin production, and therefore, inhibiting QS can be considered as a versatile strategy to control the infections. Here using real time-PCR, the expression of two groups of genes related to QS activity including lasI/lasR and rhlI/rhlR were measured. Gel-H.P resulted in a significant decline of the gene expression and the presence of curcumin amplified this effect. We observed slightly different activity of Gel-H.P and Gel-H.P.Cur on the QS system, which can link to HA and curcumin effects. Since biofilm production is one of the processes controlled by the QS system, the effect of HA on the inhibition of biofilm production might be related to its effect on the QS system (Bahari et al. 2017). The potential of curcumin to inhibit biofilm formation in uropathogens such as E. coli and P. aeruginosa by inhibiting the QS signaling cascade has already been identified (Packiavathy et al. 2014). Moreover, the susceptibility of uropathogens to common antimicrobials increased in the presence of curcumin (Packiavathy et al. 2014). Bahari et al. also examined the synergistic effect of curcumin with antibiotics such as azithromycin and gentamicin. In their study, the combination of sub-MIC concentrations ($\frac{1}{4}$ × MIC) of curcumin with some antibiotics significantly reduced the expression of QS regulatory genes. They emphasized that such results could lead to the development of new combination therapies against P. aeruginosa (Bahari et al. 2017).
In the last part of our study, the regenerative effect of Gel-H.P and Gel-H.P.Cur on the real skin which had infectious injury was explored. Logically, its biological role in extracellular matrix formation and maintaining, HA based biosystems should involve in regeneration of the skin damages which has been demonstrated in a number of reports (Dicker et al. 2014; Jiang et al. 2005). On the other hand, adding curcumin to Gel-H.P (Gel-H.P.Cur) improved the wound healing ability, and also rendered the high antibacterial activity. Curcumin possesses several features to make wound healing progress, such as anti-inflammatory/antioxidant activities, promoting fibroblasts migration toward the wound bed, and improving the contraction process in the wound (Akbik et al. 2014). Another advantage of using Gel-H.P.Cur in the wound healing process is leaving minimal scarring (Fig. 5). Numerous documents have shown that in infants the wound healing often completes without scarring because of a HA-enrichment ECM (Samuels and Tan 1999). This event suggests that exogenous HA can also promote wound healing process scarlessly. Although on the day 12th, the wound closure in the group treated with Gel-H.P.Cur ($72.92\%$ ± 3.53) (Fig. 4b, PAO1 + Gel-H.P.Cur, group 6: Table 1) is lower than that of the group treated with Gel-H.P ($83.01\%$ ± 3.51) (Fig. 4b, PAO1 + Gel-H.P., group 5: Table 1), it is worth mentioning that the extent of the wound on the day of wounding (day 0) in the first one is bigger than the second (Fig. 4a). This is while the amount of hydrogel (100 µL) was the same for all of the groups. Therefore, it is logical that a larger wound needs more time to close and heal. Also, the closure of the wound from the day 12th to the day 15th is quite relatively evident in the group treated with Gel-H.P.Cur (Fig. 4a).Fig. 5Schematic illustration of promoting wound healing process in the presence of Gel-H.P.Cur. a Thickness wound formation in the skin, and attacking of opportunistic bacteria. Due to the local inflammation and swollen, the wound starts to expand. b Treating with Gel-H.P.Cur promotes the healing processes. For instance, bacteria are prevented from entering the wound bed, and gradually the swelling and local inflammation decreases and also exudates are absorbed by the hydrogel. Proliferation of fibroblasts and angiogenesis are stimulated by both HA and curcumin. c After treatment with Gel-H.P.Cur the wounded tissue is completely regenerated and a slight scar is seen on the wound surface Wound healing phenomenon is a complex biological process that comprises cross talks between keratinocytes, fibroblasts, and immune cells (Pastar et al. 2014). This process involves four overlapping stages including homeostasis, inflammation, proliferation, and remodeling (Clark 2013). The combination of HA and curcumin in Gel-H.P.Cur likely provided numerous healing properties. For instance, we observed that after one regime of application, the infected wound closure properly with advanced signs of skin regeneration. The studies demonstrated that HA has immunostimulatory, pro-inflammatory, and antioxidant activities (as a free radical scavenger) (Aya and Stern 2014; Jiang et al. 2011). HA also plays an efficient role in cell proliferation and angiogenesis (Frenkel 2014, Slevin et al. 2002). Angiogenesis is an essential phenomenon in the wound healing process for improving nourishment of the wound bed. Wound treatment with a healer composed of HA/Cur increases vascular endothelial growth factor (VEGF)-positive cells in the healing wound bed, as reported recently (Zhou et al. 2021).
Interestingly, in a bilateral activity, on one side HA stimulates the initiation of inflammation, and on the other side, curcumin modulates the inflammatory process. In fact, the skin-repair process is in harmony with inflammatory responses (Vikram Choudhary 2018). Nyman et al. showed that the treating of wounds with exogenous HA in the human model of deep skin lesions, accelerated re-epithelialization (Nyman et al. 2019). The role of HA in re-epithelialization may be important to avoid scarring as mentioned above (Hu et al. 2003; Mahedia et al. 2016). In addition, the antimicrobial properties of HA and curcumin can power the hydrogel to encounter infections appropriately.
Our results on the synergistic action of HA and curcumin for wound healing are consistent with recent studies. In 2021, researchers showed, the application of curcumin-loaded HA-pullulan injectable hydrogel on a rat model resulted in $90\%$ diabetic wound closure. The authors have emphasized the role of curcumin-containing hydrogel in inhibiting inflammatory cells and enhancing tissue regeneration and angiogenesis (Shah et al. 2021).
Several studies have shown that Low molecular weight HA (LMW-HA) and high molecular weight HA (HMW-HA) can have a different effects on the expression of genes and, as a result, the physiological activity of the critical cells participate in the wound healing process (including fibroblasts and macrophages) (Maharjan et al. 2011; Rayahin et al. 2015). Although HMW-HA contributes to the clot formation process in the first moments of wounding (via binding with fibrinogen), the range of functions of LMW-HA in the wound healing process is much vaster (Huang et al. 2019). For example, it has been reported that LMW-HA has a crucial roles to elaborate the processes such as inflammatory responses, activation of macrophages, expression of chemokine and removal of free radicals (D’Agostino et al. 2015). Also, some researchers showed that LMW-HA prevents the differentiation of fibroblasts and collagen deposition in the early stages of wound healing, and consequently, induces the migration of macrophages toward the wound site with the aim of phagocytizing the cell corpses and also infectious cells (D’Agostino et al. 2015; Maharjan et al. 2011).
## Supplementary Information
Additional file 1:Figure S1. Chemical structure of HA (a) and PDMS-DG (b). HA consists of repeating di-saccharide units: N-acetyl glucosamine and D-glucuronic acid. Two functional groups, hydroxyl and carboxyl, are shown by green and pink spheres. Also, an amino group can be recovered by deacetylation of the N-acetyl group (orange sphere). Polydimethylsiloxane is a kind of silicon that has two methyl groups attached to its silicon structure. PDMS-DG has two epoxy groups in its ends (blue cones). The chemical structures present here have been drawn by ChemBioDraw Ultra 12.0. Table S1. The primers that were used to determine the effect of Gel-H.P and Gel-H.P.Cur on the expression of QS circuit genes (lasI, lasR, rhlI and rhlR) employing real-time qPCR. Table S2. NMR characteristic peaks of HA, PDMS-DG and Gel-H.P
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---
title: Genetic variation in salt taste receptors impact salt intake and blood pressure
authors:
- Noushin Mohammadifard
- Faezeh Moazeni
- Fatemeh Azizian-Farsani
- Mojgan Gharipour
- Elham Khosravi
- Ladan Sadeghian
- Asieh Mansouri
- Shahin Shirani
- Nizal Sarrafzadegan
journal: Scientific Reports
year: 2023
pmcid: PMC10006406
doi: 10.1038/s41598-022-23827-0
license: CC BY 4.0
---
# Genetic variation in salt taste receptors impact salt intake and blood pressure
## Abstract
So far, few studies have examined the effect of salt taste receptors genetic variation on dietary intake in the Iranian population. We aimed to evaluate associations between single nucleotide polymorphisms (SNPs) in salt taste receptors’ genes with dietary salt intake and blood pressure. A cross-sectional study was carried out among 116 randomly selected healthy adults aged ≥ 18 in Isfahan, Iran. Participants underwent sodium intake determination by 24-h urine collection, as well as dietary assessment by semi-quantitative food frequency questionnaire and blood pressure measurement. Whole blood was collected to extract DNA and genotype of SNP rs239345 in SCNN1B and rs224534, rs4790151 and rs8065080 in TRPV1 gene. Sodium consumption and diastolic blood pressure were significantly higher in carriers of the A-allele in rs239345 compared to subjects with the TT genotype (4808.4 ± 824.4 mg/day vs. 4043.5 ± 989.3 mg/day; $$P \leq 0.004$$) and 83.6 ± 8.5 mmHg vs. 77.3 ± 7.3 mmHg; $$P \leq 0.011$$), respectively. The level of sodium intake was lower in the TT genotype of TRPV1 (rs224534) than the CC genotype (3767.0 ± 713.7 mg/day vs. 4633.3 ± 793.5 mg/day; $$P \leq 0.012$$). We could not find any association between genotypes of all SNPs with systolic blood pressure as well as genotypes of rs224534, rs4790151 and rs8065080 with diastolic blood pressure. Genetic variations can relate with salt intake and consequently may associate with hypertension and finally cardiovascular disease risk in the Iranian population.
## Introduction
Approximately, 1.7 million cardiovascular disease (CVD) deaths in 2010 were attributed to high dietary sodium consumption, accounting for $10\%$ of all CVD deaths. Furthermore, in a recent study by Messerli, it has been shown that sodium intake correlates positively with life expectancy and inversely with all-cause mortality worldwide and in high-income countries claims against dietary sodium intake are a reason of decreasing life span or a risk factor for premature deaths 1,2. Both individual- and population-based studies have shown that genetic and environmental factors significantly influence sodium consumption and consequently, the blood pressure (BP) level 3. It has been shown that excessive sodium consumption can be attributed to higher preferences for salty foods, which may be linked to genetic factors such as salt taste receptor function 4.
The amiloride-sensitive (AS) fibers using the ENaC protein moderate sodium taste preference, which typically shows a lower taste concentration threshold. However, the amiloride-insensitive (AI) fibers and hence TRPV1 can regulate aversive responses to salt concentrations 5. Therefore, AS and AI fibers structures facilitate salt taste transduction. It has been shown that SNPs in the SCNN1B gene polymorphism of AA/AT, rs239345, coded for ENaC β subunit and TRPV1 genes including rs239345, rs3785368, rs8065080 are associated with differences in salt taste perception and BP among adults and children 6,7. In addition, inter-individual variation sources such as environmental and cultural determinants of dietary intake, may have an important controversial role in salt preference, as well 8,9. Consequently, understanding the genetic variation in taste perception may lead to new personalized dietary approaches, which can reduce the risk of CVDs 10. There are many controversies on the association of various SNPs in the SLC4A5, SCNN1B and TRPV1 genes like SLC4A5 rs7571842, SLC4A5 rs10177833, SCNN1B rs239345 and TRPV1-rs8065080 with the salt intake, preference and sensitivity along with health markers like SBP and DBP 6,11–17. However, studies have mostly been conducted in Caucasians from Europe, US or Canada 11–14 and few studies have examined the effect of genetic variations on the sodium intake in diverse populations such as Middle East countries like Iran. Therefore, more research is needed to determine the effect of single nucleotide polymorphisms (SNPs) in different taste receptor genes on taste sensitivity. This study aimed to examine the effect of a set of SNPs on sodium intake, food contribution in sodium intake and BP among Iranian adults.
## Design and subjects
The present cross-sectional study consisted of 116 adults aged > 18 living in Isfahan, Iran. Exclusion criteria included diagnosis of diabetes, renal insufficiency, having special dietary regimen, fasting or menstruation (for women) on the day of sampling, using diuretics (because of 24-h urine collection to estimate salt intake) and oral contraceptives, the women who are pregnant and lactating, participants with impaired taste, excessive sweating during an unusually hot day or unusual physical activity and incomplete 24-h urine collection.
Participants were selected using multi-stage random cluster sampling method. One adult person aged > 18 was selected from each household. Considering the minor allele frequency of 26.42 for SCNN1B (rs239345), sample size calculated by 106 and after predicting $10\%$ non-response rate, final sample size for this study was 116. They were referred to Isfahan Cardiovascular Research Institute (ICRI) for data collection. Our participation rate was $92\%$. We obtained written informed consents from all participants. All methods were carried out in accordance with relevant guidelines and regulations (e.g. Helsinki declaration). This study was approved by ethical committee of National Institute for medical research Development (IR.NIMAD.REC.1397.346).
## Data collection
Trained health professionals conducted the detailed interviews to obtain information about participants’ socioeconomic status including education, occupation, demographic characteristics and smoking habit. Physical activities were assessed by means of International Physical Activity Questionnaire (IPAQ) 18.
## Anthropometrics and blood pressure measurements
Trained health professionals measured standing height without shoes and recorded to the nearest 0.5 cm. Body weight was measured for subjects wearing light clothes, without shoes and recorded to the nearest 0.5 kg. Body mass index (BMI) was calculated as weight divided by height squared (kg/m2). While subjects were standing, waist circumference (WC) was measured midway between the lower ribs and the iliac crest in a horizontal plane. Hip circumference was also measured at the point yielding the maximum circumference over the buttocks using a non-elastic meter.
BP was measured (twice in each arm) manually with a mercury sphygmomanometer according to standard protocols 19, after resting for 5 min in a seated position. The first Korotkoff sound was recorded as the systolic BP (SBP) and the disappearance of the sounds (V phase) was considered as the diastolic BP (DBP). We used the mean BP from the arm with the highest value 16.
## Dietary assessment
Dietary intake was assessed using validated 136-item semi-quantitative food frequency questionnaire (FFQ) 20. This FFQ was prepared to determine the amount of sodium intake and food contribution in sodium intake. We calculated the discretionary salt which was added at table and in cooking through questioning about the weight of salt packages, the number of households, and the period of time that each salt package is used 20. This FFQ was validated for assessment of sodium intake against two reference methods including twelve 24-h dietary recall which were completed monthly during a year among 113 heathy adults aged ≥ 19 in Isfahan, Iran. They administered two FFQ at the beginning and after 1 year to evaluate the reproducibility. The deattenuated Spearman correlation coefficient between the contribution of food sources of sodium based on the FFQ and 24-h dietary recalls varied from 0.39 for canned food to 0.53 for added salt ($P \leq 0.001$). The deattenuated Spearman correlation coefficient between the FFQ and 24-h dietary recall for total sodium intake was 0.65 ($P \leq 0.001$). Intraclass correlation coefficient ($95\%$ CI) between two FFQ ranged from 0.20 (0.005–0.37) ($$P \leq 0.031$$) for sauces to 0.49 (0.28–0.69) for bread. According to the Bland–Altman plots, we observed an acceptable level of agreement between the two methods for sodium intake 20.
All participants were asked to answer how often they had consumed food items over the past year in 9 options including never or rarely (less than 1 per month), 1–3 per month, 1 per week, 2–4 per week, 5–6 per weak, 1 per day, 2–3 per day, 4–5 per day and 6 or more per day. The Iranian Food Consumption Program (IFCP) 21 was used to calculate sodium intake and food group servings for all foods reported in the FFQ using the Iranian Food Composition Table 22. The FFQ contained four questions about discretionary salt consumption including the salt used at the table; the weight of the salt package used; time taken to consume each salt package and the number of family members 23.
## Urine collection
The morning urine samples were collected on 2 days at 7:00 a.m. (the first sample of the first day was excluded) and was poured into a sterile plastic container labeled with the participants’ ID and a special code. The samples with low volume or lack of proper collection were excluded. The urine samples were collected at home for those that the delivery of their samples was impossible for any reason. Fasting venous blood samples were taken to measure serum biochemical parameters including, fasting blood glucose (FBG), serum albumin level, and lipid profile. Each participant provided two samples for the urinary sodium, potassium and chloride. The mean of 24-h urine sodium excretion was used to estimate daily salt intake. In order to obtain the whole 24-h urinary Na (24hUNa), we multiplied Na concentration by the volume in liters. The urinary sodium, potassium and chloride were measured by emission flame photometry and creatinine (Cr) was measured by the Jaffe method (Technical SMA 12–60) 24 in 24-h urine samples. The 24-h urine samples’ completeness was evaluated through the following criteria: Total 24-h urine volume sample ≥ 500 mL, missing no more than 1 void during collection, and collection of ≥ 20-h and 24-h urine creatinine (24hUCr) ≥ 20 mg/dL per kg of body weight in men and ≥ 15 mg/mL per kg of body weight in women aged < 50 and 24hUCr ≥ 10 mg/dL per kg of body weight in men and ≥ 7.5 mg/mL per kg of body weight for men and women aged ≥ 50 25.
## Single nucleotide polymorphisms selection and genotyping
A PubMed SNP search was conducted for the following genes associated to taste detection. The selected SNPs were filtered by global minor allele frequency (MAF), and SNPs with a minor allele frequency below $5\%$ were removed. The resulting SNPs were filtered using HaploView 4.2 software to obtain tag SNPs (tSNPs). Each tSNP was considered independent due to low linkage disequilibrium (r2 < 0.05). SCNN1B rs239345 (MAF = 0.2642) and TRPV1 gene rs4790151 (MAF = 0.2138), rs224534 (MAF = 0.378) and rs8065080 (MAF = 0.3177). Dias et al. ’s study showed that TRPV1 receptor might have an important role in salt preference 6. Rs8065080, in the TRPV1 gene, affect functional activity of TRPV1 receptors and be involved in different risk factors and pain conditions 26. González-Mercado et al. ’s study showed that four SNPs including rs224534 and rs8065080 are located in this gene as haplotype 24. Previous studies reported the role of TRPV1-rs8065080 polymorphism in ion channel function, rs224534 and two other variants in higher sodium intake 27,28. There is little evidence about rs3785368 variant to suggest its role in salt intake. Therefore, due to lack of budget, we selected the most important polymorphism which could have role and other variants will be studied in the future. Genotyping design of this variant is based on reported variant in dbSNP database.
## Genotyping analysis
DNA was isolated from peripheral blood lymphocytes using the standard salting out method 29. Genotyping was carried out using ARMS method for allelic discrimination, and validated by capillary sequencing (AB3730, Applied Biosystems). Primers and annealing temperature used in the study are listed in Supplementary Appendix 1. The reaction details are as follows: PCR using an Eppendorf gradient type master cycler (Eppendorf, Germany) with a total volume of 16 µL (8 µL Taq PCR Master Mix, 0.4 µL each outer primer (10 mM), 0.2 µL each inner primer (10 mM), 1 µL genomic DNA and 3.2 µL H2O). After initial denaturation (95 °C 3 min) 30 cycles (95 °C 45 s, annealing temperature (Supplementary Appendix 1) 45 s, 72 °C 15 s) of amplification were performed, followed by an extension (72 °C 15 s) and a final elongation step (72 °C 1 min).
## Statistical analysis
Kolmogorov–Smirnov test was used for assessing the data distribution in all datasets. Data were reported as mean ± standard deviation (SD) for quantitative variables and number (percentage) for qualitative variables. A chi-square (χ2) test was performed to determine whether distributions of the genotypes of the study polymorphisms were in Hardy–Weinberg equilibrium. One-way analysis of variance (ANOVA) and then Bonferroni as post hoc method (for correcting significance level due to multiple tests) were utilized to compare means in variables with normal distribution. Kruskal–Wallis test and then Mann–Whitney test as post hoc method were used to evaluate means of continuous variables across different genotypes of each SNP, when the assumptions of one-way analysis of variance were not met. Distribution of categorical variables across quartiles of different genotypes of each SNP were assessed using Chi-square test. We considered 2-tailed P values of less than 0.05 to be statistically significant. Analyses were conducted using SPSS statistical software version 19.0 for windows (SPSS Inc., Chicago, USA).
## Ethics approval and consent to participate
This study was funded by National Institute for Medical Research Development (NIMAD, Grant Number 977549). Written informed consents were obtained from adult participants and the parents of children.
## Results
Totally, 116 healthy adults ($56\%$ male and $44\%$ female) were chosen for the study. Participants’ characteristics are summarized in Table 1. Participants had a mean age of 35.4 ± 7.7 years, BMI of 27.3 ± 4.9 kg/m2, total physical activity of 774.1 ± 667.3 METs minute per week, SBP of 122.6 ± 16.3 mmHg, DBP of 80.3 ± 14.7 mmHg and $11.2\%$ of subjects were currently smokers. The 24UNa mean as a surrogate of sodium intake was 4651.0 ± 1647.7 mg/day. Table 2 shows the dietary intake in participants based on sex. The mean energy intake was 2080.5 ± 1656.5 kcal/day and the percentage of energy from carbohydrate, protein, total fat, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) were 54.8 ± 8.7, 15.4 ± 2.5, 30.6 ± 7.4, 12.4 ± 5.9, 8.9 ± 2.5 and 8.2 ± 3.1, respectively and the mean fiber intake was 22.2 ± 10.7. The genotypic frequencies of the polymorphisms of rs239345 in the SCNN1B and rs4790151, rs224534 and rs8065080 in theTRPV1 in the participants of our study were consistent with the Hardy–*Weinberg equilibrium* (Table 3). Table 4 shows genotypes and alleles distributions of the SNP rs239345 in the SCNN1B and SNPs rs4790151, rs224534, rs8065080 in theTRPV1 genes. Table 1Basic characteristics of participants based on sex in 116 randomly selected adult participants of Isfahan city, Iran. CharacteristicsTotalN = 116MaleN = 65FemaleN = 51Age (year)35.7 ± 7.735.8 ± 7.136.1 ± 9.0Body mass index (kg/m2)27.3 ± 4.926.5 ± 4.028.8 ± 6.1Waist circumference (cm)100.4 ± 12.4100.5 ± 12.3100.1 ± 12.8Total physical activity (METs/minute per week)774.1 ± 667.3869.6 ± 690.4652.4 ± 537.5Systolic blood pressure (mmHg)122.6 ± 16.3121.6 ± 16.0125.5 ± 17.7Diastolic blood pressure (mmHg)80.3 ± 14.781.0 ± 13.180.0 ± 18.3Fasting blood glucose (mg/dL)86.0 ± 17.286.5 ± 18.984.9 ± 15.5Total cholesterol (mg/dL)189.2 ± 40.4188.8 ± 35.7192.9 ± 49.2Triglyceride (mg/dL)191.2 ± 113.7194.5 ± 103.2191.0 ± 137.3Low density lipoprotein (mg/dL)102.8 ± 24.9102.2 ± 23.6105.5 ± 28.4High density lipoprotein (mg/dL)47.0 ± 10.247.4 ± 9.846.7 ± 11.324-h urine sodium (mg/day)4651.0 ± 1647.74223.2 ± 1718.54532.0 ± 15.93.9Urine creatinine (mg/dL)1813.2 ± 1087.52003.0 ± 1318.01445.9 ± 576.0Urine volume (mL/day)1197.2 ± 551.91351.3 ± 637.21033.6 ± 361.6Education n (%)Illiterate and primary school7 (6.1)2 (3.1)5 (9.8)Guidance and high school67 (57.7)36 (55.4)31 (60.8)University42 (36.2)27 (41.5)15 (29.4)Current smoker n (%)13 (11.2)13 [20]0 [0]Table 2Mena and standard deviation of dietary energy and nutrients intake of participants based on sex in 116 randomly selected adults participants of Isfahan city, Iran. NutrientTotalN = 116MaleN = 65FemaleN = 51Energy (Kcal/day)2080.5 ± 1656.52117.6 ± 1083.52019.7 ± 2327.7Carbohydrate (% of energy)54.8 ± 8.755.3 ± 8.654.1 ± 8.9Protein (% of energy)15.4 ± 2.515.1 ± 2.515.7 ± 2.5Total fat (% of energy)30.6 ± 7.430.9 ± 8.030.0 ± 6.3SFA (% of energy)12.4 ± 5.912.4 ± 6.512.4 ± 4.9MUFA (% of energy)8.9 ± 2.58.9 ± 2.69.1 ± 2.4PUFA (% of energy)8.2 ± 3.18.1 ± 3.28.3 ± 3.0Fiber (g/day)22.2 ± 10.722.7 ± 11.421.4 ± 9.7Table 3Conformation of the genotypes distributions of the study single nucleotide polymorphisms with Hardy–Weinberg equilibrium. Hardy–WeinbergOur studyP valuers239345T allele0.740.646 < 0.001A allele0.260.3540.011rs8065080T allele0.6830.849 < 0.001C allele0.3170.151 < 0.001rs224534C allele0.630.60.028T allele0.370.40.028rs4790151C allele0.790.63 < 0.001T allele0.210.36 < 0.001Table 4The frequency of genotypes and alleles distributions of the study single nucleotide polymorphisms in the ENaC and TRPV1 genes in 116 randomly selected adult participants of Isfahan city, Iran.rs8065080rs239345rs224534rs4790151n%n%n%n%Genotype frequenciesTT8371.6TT5345.7CC3832.8CC3731.9TC3126.7TA4438CT6253.4CT7363CC21.7AA1916.3TT1613.8TT65.1Allele frequenciesT19784.9T15064.6C13859.5C14763.4C3515.1A8235.4T9440.5T8536.6
## Single nucleotide polymorphisms and their association with sodium sources intake
The mean sodium intake from its major sources according to genotypes of studied SNPs are shown in Table 5. The intake of added salt in subjects with AA genotype (SNP rs239345, $$P \leq 0.010$$) was significantly higher than those with TT genotype. However, there was no significant association between various genotypes and mean intake of dietary sources of sodium in other SNPs. Table 5The mean and standard deviations of sodium intake from different food sources according to different genotypes of the study single nucleotide polymorphism in 116 randomly selected adult participants of Isfahan city, Iran. Sodium sources (g/day)rs239345P*1rs4790151P2rs8065080Prs224534P2AAATTTCCCTTTTTCTCCCCCTTTAdded salt2507.5 (623.6)2352.3 (976.7)2001.3 (899.7)10.0102704.9 (668.7)2352.0 (917.2)2588.8 (1545.9)0.2282234.5 (912.7)2478.3 (947.2)1722.2 (864.2)0.3182457.7 (928.3)2228.8 (931.3)2119.2 (870.2)0.358Bread143.0 (126.2)161.9 (146.7)202.2 (169.0)0.317163.9 (163.7)188.0 (155.3)149.1 (89.3)0.714175.0 (153.9)168.9 (155.3)382.7 (59.5)0.165168.0 (154.7)179.6 (137.3)193.6 (227.7)0.882Cheese14.9 (13.5)14.9 (14.9)12.7 (12.1)0.72413.5 (12.5)13.6 (14.0)18.5 (10.8)0.72814.8 (12.6)12.3 (15.1)4.0 (5.6)0.41613.8 (12.5)13.6 (13.9)14.9 (13.6)0.955Other salty dairies121.7 (106.1)101.3 (108.2)101.6 (98.7)0.767131.9 (132.9)91.5 (79.3)83.5 (94.7)0.178103.3 (94.17)110.4 (122.9)90.7 (116.1)0.936133.3 (120.2)94.4 (96.9)78.9 (59.3)0.159Fast food10.8 (17.9)15.0 (22.4)12.8 (18.5)0.76818.3 (22.0)11.1 (17.9)6 (12.7)0.10915.3 (21.9)8.2 (13.2)13.6 (8.6)0.29011.8 (15.9)14.6 (22.0)10.5 (19.2)0.731Salty snack3.5 (6.7)3.3 (6.3)3.6 (7.7)0.9785.0 (8.6)2.6 (5.8)3.2 (7.1)0.2983.3 (7.4)3.3 (6.0)10.6 (0.0)0.3513.8 (8.8)3.4 (6.4)2.7 (3.7)0.889Canned food2.2 (6.6)3.7 (11.1)4.1 (14.7)0.8630.9 (4.0)4.6 (13.9)10.8 (24.2)0.1632.54 (10.5)6.5 (15.8)0.0 (0.0)0.3242.5 (10.6)4.5 (13.8)2.9 (10.1)0.770Salty nut and seed10.6 (21.4)13.1 (18.0)11.0 (21.9)0.91311.8 (21.7)12.9 (20.3)2.8 (2.7)0.5699.7 (16.6)12.6 (21.0)17.7 (12.9)0.2357.9 (12.0)15.2 (22.1)8.9 (27.5)0.247Sauce7.7 (7.5)3.2 (3.1)3.5 (3.3)0.1405.9 (12.8)3.3 (4.0)2.5 (2.7)0.3343.1 (3.3)6.3 (14.1)8.5 (0.0)0.1693.2 (3.5)4.9 (10.6)3.4 (3.7)0.607Kruskal–Wallis test.*P P value.1P value = Significant difference with AA genotype: Post hoc analysis.2P value = Significant difference with CC genotype: Post hoc analysis.
## Single nucleotide polymorphisms and their association with salt intake and blood pressure
The comparison of mean sodium intake, SBP and DBP between related genotypes of different SNPs are shown in Table 6. There were significant differences between AA and TT genotypes of SNP rs239345 for sodium intake (4808.4 ± 824.4 mg/day vs. 4043.5 ± 989.3 mg/day; $$P \leq 0.004$$) and DBP (83.6 ± 8.5 mmHg vs. 77.3 ± 7.3 mmHg; $$P \leq 0.011$$). In addition, the mean sodium intake and DBP was significantly higher in A vs. T allele in SNP rs239345 ($$P \leq 0.035$$). The sodium intake was higher in the CC genotype of rs224534 in the TRPV1 than the TT genotype (4633.3 ± 793.5 mg vs. 3767.0 ± 713.7 mg/day/day; $$P \leq 0.012$$). It was also higher in C than T allel in SNP rs224534 ($$P \leq 0.029$$). There was no relationship between all SNPs and SBP, also SNPs including rs224534, rs4790151 and rs8065080 with DBP. Furthermore, non-significant differences were found in dietary sodium intake, with all genotypes of rs4790151 and rs8065080 SNPs in the TRPV1 gene. Table 6The mean and standard deviations of sodium intake and blood pressure according to different genotypes of the study single nucleotide polymorphisms in 116 randomly selected adult participants of Isfahan city, Iran. Variablesrs239345P*1rs4790151P2rs8065080P2rs224534PAAATTTCCCTTTTTCTCCCCCTTTSodium intake (mg/day)4808.4 (824.4)4344.8 (979.4)4043.21 (989.3)0.0044148.5 (967.9)4461.5 (1015.1)4564.8 (643.4)0.5144471.7 (1009.9)4075.2 (918.7)4351.1 (194.4)0.1714633.3 (793.5)4356.2 (1097.0)3767.02 (713.7)0.012SBP (mmHg)123.9 (15.4)121.9 (16.2)120.6 (11.3)0.670124.4 (13.7)120.3 (14.5)120.5 (6.7)0.306122.7 (14.1)119.0 (14.1)120.5 (0.7)0.469122.8 (14.3)121.3 (14.1)120.3 (13.7)0.805DBP (mmHg)83.6 (8.5)80.9 (9.1)77.3 (7.3)0.01180.7 (9.0)79.1 (8.4)82.0 7.1)0.79880.0 (8.7)79.4 (8.1)77.5 (6.4)0.87680.8 (7.7)80.4 (8.7)75.1 (8.4)0.064Analysis of variance test.*P P value.1Significant difference with AA genotype: Post hoc analysis.2Significant difference with CC genotype: Post hoc analysis.
## Discussion
The current study examined the association between variations in taste detection genes and sodium intake as well as BP for the first time in Iran and Eastern Mediterranean region. The findings of this study showed that among four SNPs which were studied, only two SNPs including rs239345 and rs224534 were significantly related to sodium intake and only rs239345 was involved in DBP level.
Regulation of salt or sodium intake is partly due to genes variation related to homeostatic sodium regulation and to hedonic responses to the salt taste 6,30. In this study, it was shown that individuals with SCNN1B gene polymorphism of AA/AT, rs239345, coded for SCNN1B and the TRPV1 gene polymorphism of CC, rs224534 had higher sodium intake. Moreover, individuals with AA genotype of SNP rs239345 had higher consumption of added salt, as one of main source of salt intake 31. In addition, individuals with AA genotype of SNP rs239345 had higher DBP level than those with TT genotype. Cheilat’s cross-sectional study on 70 families including children and parents in Canada showed that these SNPs were involved in sodium intake, BP and CVD, as A allele carriers had higher sodium intake and DBP. However, as Cheilat’s study read the reverse strand, it found this association with T allele 32. Moreover, Chamoun’s study on Canadian young adults and preschool children illustrated associations between rs4790522 and rs222745 SNPs in the TRPV1 salt taste receptor gene and salt taste sensitivity in young adults and salt taste preference in children 33. Pilic’s study among young Caucasians in the UK revealed that the subjects with AA genotype in SNP SLC4A5 rs7571842 had the highest increase in SBP and DBP; however, SNP rs10177833 (SLC4A5), rs239345 (SCNN1B) and rs8065080 (TRPV1) had no statistically significant effects on the BP response to dietary Na manipulation and with the increasing number of A alleles in SNP SLC4A5 rs10177833, sodium intake increased 12. In addition, Barragan’ study among Caucasians aged 18–80 in Spain indicated that those with AA genotype had the highest salty taste intensity rate 13. Conversely, the SNP rs239345 (SCNN1B) was not significantly associated with salt sensitivity or salt taste thresholds in Hungarian Roma and young Caucasian subjects 11,12,17.
It is well established that sodium consumption is associated with elevated BP in multiple populations 34. In the present study, in line with Cheilat’s study, it has been shown that high DBP belongs to the carriers of A allele. Genetic polymorphisms or acquired over-activity of the ENaC is accompanied with arterial hypertension 35, despite the fact that the link between sodium intake CVD events was controversial in subjects without hypertension 36. Research on the alpha subunit of ENaC suggests a potential implication on BP regulation in mice 37. Furthermore, the rs239345 polymorphism on the beta subunit of ENaC) may be associated with another SNP, perhaps, the same one that related to BP in the alpha subunit of the ENaC in rats 38.
The taste responses to salt can be obstructed by the ENaC blocker amiloride without similar effect on other taste manners in mice 39. However, this mechanism can inhibit about $20\%$ of salt taste perception in humans. Thus, to some extent, salt taste responses are regulated by ENaC in humans 40. However, other markers related to sodium intake were studied including salt taste thresholds and learned responses 17,41.
Similar to our findings, Ferraris et al. implied that there were no significant associations between the rs8065080 SNP in TRPV1 gene with an individual’s salt intake, SBP and DBP 14. However, the studies by Dias et al., Dioszegi et al. and Pilic et al., showed that T allele carriers perceived salt solutions significantly stronger than those homozygous for the C allele 6,11,17. An isoleucine (C) to valine (T) amino acid replacement (585 position of the TRPV1 protein, missense mutation) leads to rs8065080 (C > T) polymorphism. Contrary to our results, Pilic et al. 17 reported that TRPV1 rs8065080 T allele carriers had higher sodium intake than C allele carriers in a small sample of young predominantly Caucasian participants. The potential reason of these contradiction might be due to TRPV1 rs8065080 missense mutation and hence altering one amino acid 42. Moreover, age difference between the studies could be another reason, since age is a factor in phenotypical variance in genetic expression 43.
However, owing to the restricted research in the salt taste genotypes, the comparison of findings are limited, and therefore, highlighting the necessity of performing further well-designed studies.
## Strengths and limitations
To the best of our knowledge, it was the first genetic study on salt intake in the Eastern Mediterranean region. In addition, we examined sodium intake by the precise method of 24-h urine sodium measurement and food contribution with validated FFQ in our population. However, we also had some limitations including limited coverage of polymorphisms within the most important SCNN1B -associated gene and TRPV1, low sample size and lack of sequencing possibility for all samples and the fact that collecting a single 24-h urine was not enough to reflect a true customary intake. Although it has been proposed that some other SNPs might be associated with sodium intake, because of financial limitation, we did not examine them. Finally, not adjusting age, sex and BMI was another limitation. It might alter the association of salt intake and blood pressure with the SNPs.
## Conclusion
This study demonstrated that SNPs in the SCNN1B and TRPV1 genes associated with sodium intake and BP level. Therefore, genetic variations can relate with salt intake and consequently may associate with hypertension and finally CVD risk in the Iranian population. Further studies with larger sample size are warranted to replicate these results in order to better understand the genetic basis for salt taste and hypertension risk and also examine the potential effect of interactions between the diverse SNPs with the valid urine collection method can be effective in accurate estimation of sodium intake.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-022-23827-0.
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|
---
title: A Kinase Interacting Protein 1 (AKIP1) promotes cardiomyocyte elongation and
physiological cardiac remodelling
authors:
- Kirsten T. Nijholt
- Pablo I. Sánchez-Aguilera
- Harmen G. Booij
- Silke U. Oberdorf-Maass
- Martin M. Dokter
- Anouk H. G. Wolters
- Ben N. G. Giepmans
- Wiek H. van Gilst
- Joan H. Brown
- Rudolf A. de Boer
- Herman H. W. Silljé
- B. Daan Westenbrink
journal: Scientific Reports
year: 2023
pmcid: PMC10006410
doi: 10.1038/s41598-023-30514-1
license: CC BY 4.0
---
# A Kinase Interacting Protein 1 (AKIP1) promotes cardiomyocyte elongation and physiological cardiac remodelling
## Abstract
A Kinase Interacting Protein 1 (AKIP1) is a signalling adaptor that promotes physiological hypertrophy in vitro. The purpose of this study is to determine if AKIP1 promotes physiological cardiomyocyte hypertrophy in vivo. Therefore, adult male mice with cardiomyocyte-specific overexpression of AKIP1 (AKIP1-TG) and wild type (WT) littermates were caged individually for four weeks in the presence or absence of a running wheel. Exercise performance, heart weight to tibia length (HW/TL), MRI, histology, and left ventricular (LV) molecular markers were evaluated. While exercise parameters were comparable between genotypes, exercise-induced cardiac hypertrophy was augmented in AKIP1-TG vs. WT mice as evidenced by an increase in HW/TL by weighing scale and in LV mass on MRI. AKIP1-induced hypertrophy was predominantly determined by an increase in cardiomyocyte length, which was associated with reductions in p90 ribosomal S6 kinase 3 (RSK3), increments of phosphatase 2A catalytic subunit (PP2Ac) and dephosphorylation of serum response factor (SRF). With electron microscopy, we detected clusters of AKIP1 protein in the cardiomyocyte nucleus, which can potentially influence signalosome formation and predispose a switch in transcription upon exercise. Mechanistically, AKIP1 promoted exercise-induced activation of protein kinase B (Akt), downregulation of CCAAT Enhancer Binding Protein Beta (C/EBPβ) and de-repression of Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 4 (CITED4). Concludingly, we identified AKIP1 as a novel regulator of cardiomyocyte elongation and physiological cardiac remodelling with activation of the RSK3-PP2Ac-SRF and Akt-C/EBPβ-CITED4 pathway. These findings suggest that AKIP1 may serve as a nodal point for physiological reprogramming of cardiac remodelling.
## Introduction
The heart continuously adapts to fluctuations in metabolic demands of peripheral tissues1,2. In response to sustained or repetitive increases in workload such as exercise or pressure overload, the heart responds by increasing muscle mass, a process known as cardiac hypertrophy1. The increase in muscle mass provides mechanical advantages as it reduces ventricular wall stress and increases contractile performance1,3. Although cardiac hypertrophy provides early functional compensation, it can also be characterised by maladaptive changes in the histological, molecular, biochemical, and metabolic composition of the myocardium that can ultimately cause heart failure (HF)1,3,4.
There are, however, important distinctions between cardiac hypertrophy that occur in response to physiological stimuli such as exercise and pregnancy and pathological stimuli such as pressure overload1,2. Most importantly, physiological and pathological cardiac hypertrophy are governed by distinct signal transduction pathways with distinct histological, biochemical and molecular signatures. For instance, pathological cardiac hypertrophy is accompanied by a predominant increase in cardiomyocyte width, relative reductions in capillary density, re-expression of the foetal cardiac genes, mitochondrial dysfunction, and fibrosis1,3–5. In contrast, physiological cardiac hypertrophy induced by endurance exercise is characterized by a preferential increase in cardiomyocyte length (eccentric hypertrophy), expansion of the capillary network and improvements in mitochondrial quantity and quality1,2,4,6–8.
While intense efforts have been devoted to the identification of pathways underlying pathological hypertrophy, relatively little is known about physiological hypertrophy. Identifying key factors regulating physiological cardiac hypertrophy should allow the design of therapies to shift pathological hypertrophy towards a more physiological end of the spectrum.
An important distinction between physiological and pathological hypertrophy is that they are governed by distinct growth factors and signal transduction pathways1. Our group recently identified the signalling adaptor protein A Kinase Interacting Protein 1 (AKIP1) as a pro-hypertrophic gene9, the myocardial expression of which is increased in response to exercise10 as well as in a transgenic mouse model of physiological hypertrophy11. In cultured cardiomyocytes, overexpression of AKIP1 improved mitochondrial function12 and induced a physiological type of hypertrophy through activation of the Akt-mTOR pathway13. Accordingly, we tested the hypothesis that cardiomyocyte-specific overexpression of AKIP1 in mice promotes cardiac hypertrophy in response to voluntary exercise in vivo.
## Ethical approval
The Animal Ethics Committee from the University of Groningen approved the animal experiments (DEC6237F, 199105-01-005), which were performed following the protocols from Directive $\frac{2010}{63}$/EU of the European Parliament. The study was reported in accordance with the recommendations of the ARRIVE guidelines.
## Animal model
A total of 44 adult male mice (8–12 weeks old) were included in the study population. This study population included mice with cardiomyocyte-specific overexpression of AKIP1 (AKIP1-TG) and their wild type (WT) littermates. AKIP1-TG mice were generated as described previously14. To validate whether there was AKIP1 overexpression in the heart of AKIP1-TG mice, mRNA and Western blot analysis was performed (Supplementary Fig. S1).
## Experimental model
AKIP1-TG mice and their WT littermates were caged individually in the presence or absence of a running wheel for four weeks. This resulted in two running groups (WT Run, AKIP1-TG Run) and two sedentary groups (WT Sed, AKIP1-TG Sed) (Fig. 1A). All mice had ad libitum access to food and water and were housed in 12:12 h dark–light cycles. Figure 1Exercise parameters during four-week period of voluntary wheel running, ($$n = 9$$–11/group). Shown are, (A) experimental design including voluntary wheel running protocol, (B) daily running distance in kilometres per day (km/day), (C) average daily running distance in kilometres per day (km/day), (D) running time in hours per day (h/day), (E) average running time in hours per day (h/day), and (F) average speed in kilometres per hour (km/h). Mice included were aged 8–12 weeks old. WT = wild type mice, AKIP1-TG = AKIP1 transgenic mice. Graphs represent mean ± standard error of the mean (SEM). Statistical analysis was performed with Student’s t-test, < 0.05 was considered statistically significant. Part of the illustration in panel (A) contains images from Servier Medical Art by Servier, licensed under a Creative Commons Attribution 3.0 unported license.
## Exercise performance
Exercise performance was recorded using a cyclometer connected to the running wheel15. Mice that were housed with a running wheel in their individual cage, had a sensor and cyclometer attached to the running wheel. The sensor on the running wheel automatically detected running activity as soon as mice started performing exercise. Using the sensor and cyclometer, we were able to record exercise parameters including running speed, distance and time. To monitor and determine exercise capacity, the cyclometer was read out on a daily basis, every 24 h.
## Cardiac function parameters and haemodynamics
After completion of the running wheel protocol, cardiac magnetic resonance imaging (MRI) using 9.4 T 400 MR system (Bruker Biospin, Ellingen, Germany) was performed as previously described14. Manual analysis was performed using Circle Cardiovascular Imaging (CCI, version 42, Canada) software. Thereafter, aortic and intracardiac pressure parameters were obtained using a pressure volume system with a Millar catheter (Micro-Tip 1.4 French; SPR, -839, Millar Instruments, USA) as performed before16. Analysis was performed with Labchart 7 software (ADInstruments, LtD, Dunedin, New Zealand). Cardiac function was determined under anaesthesia of $2\%$ isoflurane and continuous monitoring of breathing, heart rate and temperature was performed.
## Organ and body measurements
Following cardiac function assessment, animals were euthanised by cardiac puncture under anaesthesia of $2\%$ isoflurane, after which blood was drawn, hearts were excised and weighed. Atria, right ventricle (RV) and left ventricle (LV) were separated and snap frozen in liquid nitrogen for molecular analysis.
## Histology
Mid-papillary sections of LV were preserved for histological purposes. Sections were fixated in $4\%$ paraformaldehyde for 24 h, which was followed by dehydration steps and embedding of samples in paraffin (Leica, TP 1020, Germany). For cryopreservation, LV samples were fixated in TissueTec and slowly snap frozen in liquid nitrogen.
## Cross sectional area
4 μm sections were deparaffinized and stained with 4′,6-diamidino-2-phenylindole (DAPI) (ab H-1200, Vector laboratories, USA) and wheat germ agglutinin-FITC (WGA) (ab L4895, Sigma-Aldrich, USA) to determine cardiomyocyte cross sectional area, as a parameter for cardiomyocyte size17. Fluorescent microscopy (Leica, ctr 600, Leica Microsystems, Germany) was performed to obtain images of transverse sections at 20× magnification, which were quantified using Fiji-ImageJ software (Fiji- ImageJ version of Java 6, National Institutes of Health, USA). In total, 30–40 cardiomyocytes were manually selected per animal.
## Cardiomyocyte length and width
10 μm sections were deparaffinized and stained with DAPI, WGA and an antibody that recognizes Desmin (ab SC-7559 Santa Cruz, USA). Fluorescent microscopy (Leica, ctr 600, Leica Microsystems, Germany) was performed to obtain images of longitudinal 3D Z-stacks at 20× magnification (Supplementary Fig. S2). The cardiomyocyte length and width were measured at the mid-nuclear level of individual cardiomyocytes, using the desmosomes as landmarks using Fiji-ImageJ software (Fiji- ImageJ version of Java 6, National Institutes of Health, USA). In total, 30–40 cardiomyocytes were randomly selected per animal.
## Fibrosis
4 μm sections were deparaffinized and stained with Masson Trichrome staining to determine the level of interstitial fibrosis present18. Images were generated at 40× magnification with Hamamatsu (Hamamatsu Photonics, Japan) and quantified using Aperio ImageScope software (Version 11, Leica Microsystems, Germany).
## Capillary density
Cryosections were incubated with an antibody against endothelial antigen CD31 to stain for capillaries as a measure of angiogenesis (DIA-310, Dianova, Germany)19. Quantification of capillaries was performed by selecting random fields at 40× magnification (Leica, ctr 600, Leica Microsystems, Germany).
## Electron microscopy
To evaluate the compartmental localization of AKIP1 in the cardiomyocyte, we performed large-scale electron microscopy (EM), known as nanotomy (for nano-anatomy)20. Detailed description of the methodology was reported previously20. In brief, following sacrifice, LV samples were sliced and fixated immediately in $2\%$ glutaraldehyde and $2\%$ paraformaldehyde solution in 0,1 M sodium cacodylate and postfixed in $1\%$ osmiumtetraoxide and $1.5\%$ potassium ferrocyanide. Samples were dehydrated, embedded in EPON epoxy resin, and sectioned. Ultrathin Sections (80 nm) were contrasted using $4\%$ neodymium acetate. In addition, sections were immunolabelled as described previously21. In brief, samples were etched with $1\%$ periodic acid for 10 min, followed by a 30-min blocking step: $1\%$ bovine serum albumin (BSA; Sanquin, the Netherlands) in tris-buffered saline (TBS), pH 7.4. Next, AKIP1 primary antibody (Supplementary Table S2) was incubated for 2 h followed by washing and subsequent incubation for 1 h with biotinylated secondary antibody (goat-anti-rabbit; 1:400, Dako, Denmark), followed by washing steps. Finally, streptavidin conjugated QD655 (1:1000, Life Technologies, United States) was incubated for 1 h. Sections were imaged using scanning and transmission electron microscopy (STEM) (Zeiss Supra55, Oberkochen Germany). Images were processed into a nanotomy ‘map’ using an external scan generator ATLAS5 (Fibics, Canada). TIFF files were then exported to html files available at www.nanotomy.org. To identify elemental composition of structural changes observed on EM images, we performed additional analysis using the energy-dispersive X-ray (EDX) detector X-max (Oxford Instruments, United Kingdom)20.
## Quantitative real-time polymerase chain reaction (qRT-PCR)
To determine alterations in mRNA expression levels after exercise in AKIP1-TG mice, qRT-PCR was performed. Total RNA was isolated from snap frozen LV tissue using Trizol reagent (Invitrogen Corporation, USA), as performed before19. Using Nanodrop software (ThermoFisher, USA), RNA concentrations were quantified and tested for purity. Next, equal RNA quantities (500 ng) were reverse transcribed to cDNA with Quantitect Reverse Transcription kit (Qiagen, the Netherlands, no. 205313). cDNA was amplified with qRT-PCR for specific primers, as presented in Supplementary Table S1. Standard running protocol was performed: 3 min at 95 °C, followed by 35 cycles of [1] 15 s at 95 °C, [2] 30 s at 60 °C; followed by a dissociation step and melting steps (Bio-Rad CFX384, USA). Results were analysed using the ddCT method normalizing for housekeeping gene 36b4 and WT Sed control group.
## Western blot
To assess physiological alterations in AKIP1-TG mice on protein level, we performed Western blot. Total protein was isolated from snap frozen LV tissue using radioimmunoprecipitation assay (RIPA) buffer with phosphatase and protease inhibitors (Sigma-Aldrich, USA), as previously described22. Protein concentrations were quantified using BCA protein assay reagent (Pierce. No. 232250, ThermoFisher, USA). Samples with similar proteins concentrations of 10 μg/15 μl, including 5X loading buffer and RIPA buffer, were boiled at 99 °C for five minutes before loading onto sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE). After finalization of SDS-PAGE, proteins were transferred by semi-dry blotting onto polyvinylidene difluoride (PVDF) membranes. Following blotting and blocking, membranes were incubated with specific primary antibodies at 4 °C overnight: a list of primary antibodies used can be found in Supplementary Table S2. After one hour secondary antibody incubation, proteins were detected in ImageQuant machine with enhanced chemiluminescence solution (Pierce, ThermoFisher, USA). Analysis of images was performed in Fiji-ImageJ software (Fiji- ImageJ version of Java 6, USA) and quantifications were normalized for GAPDH or α-Tubulin as loading controls.
## Statistical analysis
Data are presented with mean and standard error of the mean (SEM). Statistical analysis of two groups was performed with Student’s t-test, if data was normally distributed. If data was not normally distributed, statistical analysis of two groups was performed with Mann–Whitney U test. A p value < 0.05 was considered statistically significant. When comparing multiple groups, Two-way ANOVA with post hoc Tukey’s test was performed. Two-way ANOVA was chosen over one-way ANOVA to determine the interactive effects of both genotype and exercise. If the Two-way ANOVA showed a statistical difference (p value < 0.05), post hoc Tukey’s test was performed to observe differences for each group14. Statistical analysis was performed using GraphPad Prism software (Version 7, USA).
## Exercise performance in AKIP1-TG mice is not affected
AKIP1 transgenic mice were phenotypically normal, with the exception of a mild reduction in left ventricular systolic blood pressure and left ventricular ejection fraction which we have described before14, cardiac structure and function were comparable between sedentary AKIP1-TG and WT groups. Heart rate, body and lung weight were also comparable between sedentary AKIP1-TG and WT mice (Supplementary Table S3). In this study, we will assess several molecular markers associated with exercise-induced cardiac hypertrophy. To ensure that these markers were not altered in sedentary mice, we also performed analysis of these markers in mice that did not perform exercise, which remained unchanged (Supplementary Fig. S3).
Wild type mice ran an average distance of 7.85 ± 0.75 kms, during 4.96 ± 0.31 h per night, at an average speed of 1.53 ± 0.07 km per hour. All these indices of running performance were comparable between AKIP1-TG and WT mice, indicating that both exercise capacity and workload during the study were comparable (Fig. 1A–F).
## Physiological cardiac hypertrophy is increased in AKIP1-TG mice, but initial cardiomyocyte size analysis depicts no differences
We next assessed the effect of AKIP1 on exercise-induced changes in cardiac structure and function using cardiac MRI and pressure–volume measurements after four weeks of voluntary wheel running. Cardiac volumes and left ventricular ejection fraction determined with MRI were comparable between AKIP1-TG and WT mice after exercise (Supplementary Table S4). Similarly, cardiac pressures were also comparable between groups upon exercise, with again the exception for a mild reduction in LV systolic blood pressure (Supplementary Table S4). Exercise-induced increases in LV mass on MRI, were, however, markedly increased in AKIP1-TG compared to WT mice (Fig. 2A,B). These increases in LV-mass detected with MRI were paralleled by a similar increase in heart weight to tibia length ratio (Fig. 2C).Figure 2Cardiac hypertrophy parameters and cardiomyocyte size after voluntary wheel running in both WT and AKIP1-TG mice. Shown are, the first three panels as indicators for cardiac hypertrophy, ($$n = 9$$–11/group), in (A) left ventricular mass (LV Mass) detected from MRI shown in milligrams (mg) with its typical examples in panel (B) and in (C) the heart weight to tibia length ratio (HW/TL) is shown in milligrams per millimetres (mg/mm). In panel (D), cardiomyocyte cross sectional area in micrometres squared (μm2) is shown, ($$n = 9$$–11/group), (E) shows typical examples for cardiomyocyte cross sectional area, scale bars indicate 50 μm. In panel (F), cardiomyocyte width in micrometres (μm), ($$n = 9$$–10/group), the latter three panels present initial cardiomyocyte size analysis. Mice included were aged 8–12 weeks old. WT = wild type mice, AKIP1-TG = AKIP1 transgenic mice, Run = Running. Graphs represent mean ± standard error of the mean (SEM). Statistical analysis was performed with Student’s t-test or Mann–Whitney U test, < 0.05 was considered statistically significant. WT Run vs. TG Run: p* < 0.05, p**** < 0.0001.
To determine whether the increase in heart weight corresponded with an increase in cardiomyocyte size, cardiomyocyte cross sectional area was determined by performing a wheat germ agglutinin staining. Surprisingly, cardiomyocyte cross sectional area was similar after exercise in AKIP1-TG and WT mice (Fig. 2D,E). In accordance with cardiomyocyte cross sectional area, cardiomyocyte width did not differ between the running groups (Fig. 2F).
## AKIP1-induced cardiac hypertrophy is dependent upon cardiomyocyte elongation mediated by dephosphorylation of serum response factor
As adaptive cardiomyocyte growth in response to endurance exercise is predominantly eccentric, we hypothesized that the increase in cardiac mass in AKIP1-TG mice could be explained by cardiomyocyte elongation1,23. To test this hypothesis, we analysed cardiomyocyte length to width ratio using 3D Z-stacks on myocardial sections stained with DAPI, WGA and Desmin to identify cardiomyocyte borders at the desmosome (Supplementary Fig. S2). Intriguingly, cardiomyocyte length was significantly increased in response to exercise in AKIP1-TG mice compared to WT mice (WT Run 72.72 ± 2.66 μm vs. AKIP1-TG Run 99.48 ± 2.18 μm, $p \leq 0.0001$), resulting in a significant increase in the cardiomyocyte length to width ratio (Fig. 3A–C). These findings suggest that the exercise-induced increase in cardiac mass observed in AKIP1-TG mice is predominantly explained by elongation of cardiomyocytes. Figure 3Cardiomyocyte length analysis and its regulation after voluntary wheel running in both WT and AKIP1-TG mice. Shown are (A) cardiomyocyte length in micrometres (μm), ($$n = 9$$–10/group), (B) cardiomyocyte length to width ratio ($$n = 9$$–10/group), and panel (C) represents typical examples for cardiomyocyte length and width measurements, scale bars indicate 25 μm. In panel (D) mRNA expression level of RSK3 is shown, ($$n = 8$$–11/group). Protein level of PP2Ac normalized for α-*Tubulin is* presented in panel (E), phosphorylation to total SRF (pSRFSer103/tSRF) in panel (F) and representative images of Western blots are presented in panel (G), ($$n = 5$$–6/group). Representative Western blot images are taken from the same blot, but not always contiguous; full and original Western blots are included in Supplementary Figures. Mice included were aged 8–12 weeks old. WT = wild type mice, AKIP1-TG = AKIP1 transgenic mice, Run = Running, RSK3 = p90 ribosomal S6 kinase 3, PP2Ac = protein phosphatase 2A catalytic subunit, α-Tubulin = alpha-tubulin, SRF = serum response factor. Graphs represent mean ± standard error of the mean (SEM). Statistical analysis was performed with Student’s t-test or Mann–Whitney U test, < 0.05 was considered statistically significant. WT Run vs. TG Run: p* < 0.05, p** < 0.01, p*** < 0.001, p**** < 0.0001.
It has recently been shown that dephosphorylation of serum response factor (SRF) is a prerequisite for eccentric growth of cardiomyocytes. Dephosphorylation of SRF in this context is regulated by downregulation of p90 ribosomal S6 kinase 3 (RSK3) and upregulation of protein phosphatase 2A catalytic subunit (PP2Ac)24,25. In support of this mechanism, we observed a significant reduction in mRNA levels of RSK3 and a significant increase of protein expression of PP2Ac, accompanied by dephosphorylation of SRF in AKIP1-TG mice compared to WT mice after exercise (Fig. 3D–G).
## Exercise-induced cardiac hypertrophy in AKIP1-TG mice was associated with physiological angiogenesis and unaltered fibrosis
We next aimed to corroborate whether the augmented exercise-induced cardiac hypertrophy in AKIP1-TG mice was adaptive in nature. An important distinction between adaptive and maladaptive hypertrophy is the reduction in capillary density and the development of cardiac fibrosis in the latter condition1–3. Interstitial fibrosis detected with Masson Trichrome staining did not differ between WT and AKIP1-TG running mice (Fig. 4A,B). Cardiac expression of common molecular markers of fibrosis, collagens 1 and 3 (Col1a1 and Col3a1 respectively), were also comparable between groups (Fig. 4C). Similarly, capillary density detected with CD31 staining and the expression of angiogenic markers hypoxia-inducible factor 1-alpha (HIF1-α) and vascular endothelial growth factor A (VEGFA) were similar between AKIP1-TG-Run and WT Run mice (Fig. 4C–F). Together, these data suggest that the marked increase in cardiac hypertrophy observed in AKIP1-TG mice is adaptive in nature (Fig. 4A–F).Figure 4Fibrosis and angiogenesis levels to determine the presence of additional aspects of cardiac hypertrophy. Shown are, (A) fibrosis levels from Masson staining ($$n = 9$$–10/group) with its typical examples in (B), in which scale bars indicate 2 mm in the left panels and 50 μm in the right panels. mRNA levels of collagens are shown in panel (C) ($$n = 8$$–9/group). In panel (D) and (E), capillary density ($$n = 9$$–10/group) and typical examples are shown, with scale bars indicating 50 μm. Panel (F) shows mRNA levels of markers for angiogenesis ($$n = 9$$/group). Mice included were aged 8–12 weeks old. WT = wild type mice, AKIP1-TG = AKIP1 transgenic mice, Run = Running, Col1a1 = collagen type 1 alpha 1 chain, Col3a1 = collagen type 3 alpha 1 chain, HIF1-α = hypoxia inducible factor 1 alpha, VEGFA = vascular endothelial growth factor A. Graphs represent mean ± standard error of the mean (SEM). Statistical analysis was performed with Student’s t-test or Mann–Whitney U test, < 0.05 was considered statistically significant.
## AKIP1 localises in clusters in the cardiomyocyte nucleus
We then determined the localisation of AKIP1 in the cardiomyocyte, which could potentially explain a switch in transcription factors underlying the exercise-induced cardiac hypertrophy. Therefore, we performed electron microscopy (EM) imaging of WT and AKIP1-TG mice. First, we qualitatively assessed EM images and interestingly, we observed structural changes in the nuclear compartments of AKIP1-TG mice (Fig. 5A). To address whether the content of the structural changes in the nuclei did have the fingerprint of protein, DNA or lipid clusters, we performed elemental composition with EDX mapping on the EM scans. These EDX data maps revealed that the structures are rich in nitrogen but not in phosphorus, hinting to protein clusters (Fig. 5B). To confirm whether these protein clusters contain AKIP1, we subsequently performed EM labelling with AKIP1 antibody. Indeed, presence of AKIP1 was observed in the dense nuclear areas in EM scans of AKIP1-TG mice and in WT mice this nuclear labelling was much less, as expected (Fig. 5C). These data suggest that AKIP1 localizes in clusters in the nucleus, potentially aiding in signalosome formation that predisposes for a switch in transcription in response to exercise. Figure 5AKIP1 mega clusters localise in the cardiomyocyte nucleus of AKIP1-TG mice. Electron microscopy (EM) mapping to identify various cellular compartments in baseline WT and AKIP1-TG mice. Shown are, in (A) a selection of a nucleus and surrounding cytoplasm of WT (left) and AKIP1-TG (right) mice. Note the atypical clusters inside the nucleus. In (B) electron image and EDX maps of elements as indicated to identify content of nucleus structures observed in AKIP1-TG mice. Note that the aggregates (red ROI and graph) are enriched in nitrogen (arrow) compared to heterochromatin (blued ROI and graph). ( C) EM ultra-sections were labelled with anti-AKIP1 antibody. Raw data with zoomable files at high resolution are accessible at www.nanotomy.org. All scale bars indicate 2.5 μm. Mice included were aged 8–12 weeks old. WT = wild type mice, AKIP1-TG = AKIP1 transgenic mice, C = carbon, N = nitrogen, O = oxygen, P = phosphorus, S = sulphur, Os = osmium.
## AKIP1 activates the physiological Akt-C/EBPβ-CITED4 growth pathway upon exercise
Next, we sought to determine the switch in molecular mechanisms underlying AKIP1-induced physiological cardiac hypertrophy, focused on the Akt-C/EBPβ-CITED4 pathway. Previous studies suggest that AKIP1 regulates protein kinase B (Akt) in association with physiological cardiac hypertrophy11,13. Metacore transcription factor analysis also suggested a transcriptional regulation of CCAAT Enhancer Binding Protein Beta (C/EBPβ) by AKIP1 in sedentary mice. C/EBPβ is a transcription factor that has been established as a negative regulator of exercise-induced physiological growth26,27. Specifically, C/EBPβ is downregulated by phosphorylated protein kinase B (Akt), resulting in the de-repression of downstream genes that promote physiological growth26,27. Simultaneously, reduction of C/EBPβ induces de-repression of Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 4 (CITED4)26–28. CITED4 has recently emerged as a nodal point in the physiological adaptation to both physiological and pathological cardiac stress and intriguingly also regulates cardiomyocyte elongation29–31. Mechanistically, CITED4 controls the regulation of mTOR signalling through the inhibition of the DNA-damage-inducible transcript 4-like (DdiT4L)30–33.
We next determined whether the AKIP1-induced eccentric cardiac hypertrophy was in fact associated with activation of this pivotal exercise-associated Akt-C/EBPβ-CITED4 growth signalling pathway. As expected, phosphorylation of Akt after exercise was significantly increased in AKIP1-TG mice compared to WT mice (Fig. 6A). Furthermore, C/EBPβ protein expression was significantly reduced in AKIP1-TG mice (Fig. 6B) and CITED4 mRNA and protein levels were significantly increased in AKIP1-TG mice compared to WT mice after exercise (Fig. 6C–E). Finally, we also assessed the potential downstream factor of C/EBPβ and CITED4, DdiT4L, which is known to suppress the mTOR activity. In corroboration with our previous findings, we observed a significant inhibition of DdiT4L in AKIP1-TG mice after exercise (Fig. 6E). The data are summarized in Fig. 7.Figure 6Molecular analysis of the physiological growth pathway: Akt-C/EBPß-CITED4 activation. mRNA and protein level analysis of the exercise-induced physiological growth pathway in AKIP1-TG mice. Shown are, (A) ratio of phosphorylated Akt to total Akt (pAktSer473/tAkt) at protein level, ($$n = 6$$–7/group), (B) C/EBPß protein levels normalized for GAPDH, ($$n = 5$$–6/group), (C) CITED4 protein levels normalized for GAPDH, ($$n = 8$$–9/group), (D) typical examples of the described Western blots and (E) CITED4 and DDiT4L mRNA levels, normalized for housekeeping gene 36B4, ($$n = 8$$–10/group). Representative Western blot images are taken from the same blot, but not always contiguous; full and original Western blots are included in Supplementary Figures. Mice included were aged 8–12 weeks old. WT = wild type mice, AKIP1-TG = AKIP1 transgenic mice, Run = Running, Akt = protein kinase B, C/EBPß = CCAAT Enhancer Binding Protein Beta, CITED4 = Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 4, DDiT4L = DNA-damage-inducible transcript 4-like. Graphs represent mean ± standard error of the mean (SEM). Statistical analysis was performed with Student’s t-test or Mann–Whitney U test, < 0.05 was considered statistically significant. WT Run vs. TG Run: p* < 0.05, p** < 0.01.Figure 7Graphical abstract depicting the molecular signal transduction pathways associated with elongation-mediated physiological cardiac hypertrophy. Increased levels of AKIP1 in combination with an exercise regimen induces activation of the RSK3-PP2Ac-SRF and Akt-C/EBPβ-CITED4 pathway. It remains unknown whether AKIP1 exerts direct or indirect effects, nonetheless increased AKIP1 in cardiomyocytes activates phosphorylation of Akt, which in turn de-represses C/EBPß. The C/EBPß downstream factor CITED4, is subsequently upregulated which results in inhibition of DDiT4L. Consequentially, activation of this pathway in turn leads to established physiological cardiac hypertrophy. Simultaneously, AKIP1 overexpression in concordance with exercise reduces levels of RSK3 and enhances levels of PP2Ac. This synchronized programming dephosphorylates SRF, which entails a molecular switch towards physiological elongation of cardiomyocytes. AKIP1 = A Kinase Interacting Protein 1, Akt = protein kinase B, C/EBPß = CCAAT Enhancer Binding Protein Beta, CITED4 = Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 4, DDiT4L = DNA-damage-inducible transcript 4-like, RSK3 = p90 ribosomal S6 kinase 3, PP2Ac = protein phosphatase 2A catalytic subunit, SRF = serum response factor.
## Discussion
To define the contribution of AKIP1 to physiological cardiac hypertrophy, we compared the cardiac response to four weeks of voluntary wheel running exercise between AKIP1-TG and WT mice. While cardiac structure and function were comparable between sedentary AKIP1-TG and WT mice, exercise-induced cardiac hypertrophy was substantially enhanced by AKIP1. Intriguingly, the increase in cardiac mass was predominantly associated with cardiomyocyte elongation. Accordingly, with molecular analysis we observed that AKIP1 reduced mRNA expression of RSK3, resulting in PP2Ac-mediated dephosphorylation of SRF, consistent with increased signalling through the PP2Ac-SRF signalosome. Subsequently, we determined that AKIP1 localizes in clusters in the cardiomyocyte nucleus, causing a potential predisposition for signalosome formation to stimulate a switch in transcription upon exercise. Furthermore, we observed mechanistic changes involving that AKIP1 promoted exercise-induced phosphorylation of Akt, downregulation of C/EBPβ and subsequent de-repression of CITED4. The changes in C/EBPβ and CITED4 expression was accompanied by canonical changes in their downstream target DdiT4L. In conclusion, our studies revealed that the signalling adaptor AKIP1 is a novel regulator of cardiomyocyte elongation and physiological cardiac hypertrophy, which is associated with the activation of two growth signalling pathways (summarized in Fig. 7). AKIP1 may therefore serve as a nodal point for cardiomyocyte elongation and physiological reprogramming of cardiac muscle.
AKIP1 was first discovered as a breast cancer associated gene (BCA)34, but was soon also found in several normal tissues, including the heart9. Myocardial expression of AKIP1 is increased in physiological hypertrophy10,11 and overexpression of AKIP1 induces hypertrophy in cultured cardiomyocytes that resembles physiological cardiac hypertrophy in vivo12,13. Furthermore, AKIP1 did not influence cardiac remodelling in response to various forms of pathologic cardiac stress, indicating that its role in the pathological setting is limited14. Our discovery that AKIP1 stimulates physiological cardiac growth in vivo, by activating two distinct but complementary physiological signalling pathways, provides strong evidence that AKIP1 is a bona fide regulator of physiological cardiac hypertrophy.
While cardiac hypertrophy can be classified as physiological or pathological depending on the underlying stimulus and the associated loading conditions, it can also be subdivided as concentric or eccentric depending on ventricular geometry1,2. Concentric hypertrophy is characterized by asymmetrical growth of cardiomyocytes in width, whereas eccentric hypertrophy is characterized by preferential myocyte elongation1,2. Endurance exercise has mostly been associated with eccentric hypertrophy and our finding that AKIP1 promotes cardiomyocyte elongation fits this paradigm1,4,35,36. The mechanisms responsible for these distinct growth patterns were recently found to be governed by a mAKAPβ-SRF signalosome, which allows close interaction between SRF and its co-factors, RSK3 and PP2Ac24,25. Our finding that AKIP1 promotes dephosphorylation of SRF, associated with repression of RSK3 and upregulation of PP2Ac, confirms that AKIP1 promotes cardiomyocyte elongation by interacting with this signalosome. Of note, in our study, cardiomyocyte elongation is not associated with changes in cardiac function and cardiac dilatation. In fact, left ventricular end diastolic volume (LVEDV) was numerically lower in AKIP1-TG mice after exercise, suggesting that the observed eccentric cardiac hypertrophy is adaptive in nature.
Similar to what has been observed in cultured cardiomyocytes, AKIP1 promoted Akt phosphorylation after exercise, and this study provides additional evidence for association with canonical activation of the downstream C/EBPβ-CITED4-DdiT4L pathway13. This exercise-induced physiological growth pathway has recently been discovered to govern beneficial adaptation to various forms of physiological and pathological stress26–31,37. Firstly, this pathway is associated with generation of physiological growth and is associated with beneficial cardiac remodelling26–31,37. Secondly, CITED4 has been found to regulate cardiomyocyte elongation31, suggesting that the cardiomyocyte elongation observed in our study may be explained by the coordinated dephosphorylation of SRF and activation of CITED4. The fact that AKIP1 activates two distinct pathways responsible for cardiomyocyte elongation and remodelling, strongly suggests that AKIP1 represents a nodal point in the regulation of cardiomyocyte reprogramming.
Regulation of Akt activity is a multifactorial process, that involves many direct as well as indirect interactions1,6. As AKIP1 has been identified as a signalling adaptor protein, it is possible that AKIP1 directly interacts with Akt, its activators or its phosphatases. Recent evidence suggests that microRNA-222 (miR-222) regulates the exercise-induced Akt-C/EBPβ-CITED4 pathway28,38. Potentially, this could also involve interaction with AKIP1. Also, miR-223 has been associated with physiological cardiac hypertrophy, in a study in which AKIP1 levels were also determined to be upregulated11. Hence, further investigation into whether AKIP1 modulates the expression of miR’s including miR-222 and miR-223, would potentially offer further insight into AKIP1 as a therapeutic target focused on activating cardiac remodelling. An additional mechanistic question is whether the RSK3-PP2Ac-SRF and Akt-C/EBPβ-CITED4 pathways are interrelated. Intriguingly, a link between the two pathways has been described previously, involving a direct interaction between SRF and C/EBPβ27. Therefore, these pathways may indeed be complementary, resulting in dual elongation pathways for physiological cardiac remodelling, a possibility that requires future study.
The functional role of AKIP1 may expand beyond its role in elongating cardiomyocytes upon exercise training. In particular, AKIP1 may also regulate mitochondrial adaptations integral to the physiological cardiac growth response. We previously showed that overexpression of AKIP1 in cultured cardiomyocytes improves mitochondrial respiration, accompanied by enhanced ATP production and reductions in mitochondrial ROS12. AKIP1 also appears to localize to mitochondria14, and two independent studies demonstrated that AKIP1 protected against myocardial ischemia/reperfusion injury, at least in part by mitigating mitochondrial cell death pathways14,39. These studies suggest that AKIP1 could influence mitochondrial adaptations to physiological stress, resulting in a contribution to physiological hypertrophy. To therefore determine whether AKIP1 can indeed serve as a therapeutic target and regulate physiological cardiac hypertrophy, future studies should focus on the role of AKIP1 on mitochondrial function.
## Strengths and limitations
A major strength of our analysis is the detailed myocardial phenotyping using cardiac MRI, pressure volume loop analysis as well as state of the art histological and molecular analysis. The primary limitation of our study is the fact that we employed voluntary exercise. Although this represents a well-established method to study physiological hypertrophy, the total workload is typically lower than observed with forced exercise and many factors can potentially influence the amount of exercise performed15,40. In our study exercise performance and total workload were similar for both groups, allowing a fair comparison of the hypertrophic response. Nevertheless, exercise-induced hypertrophy was very subtle in wild type mice and the results may have been different if forced exercise would have been employed. In addition, we used an animal model with marked overexpression of the protein of interest and we therefore cannot exclude off-target effects or the ability to fully recapitulate the human situation. The development of a cardiomyocyte specific knock-out mouse model would aid in bypassing this limitation.
## Future perspectives
We have provided evidence that AKIP1 activates the RSK3-PP2Ac-SRF and Akt-C/EBPβ-CITED4 pathway in response to exercise accompanied by enhanced physiological hypertrophy. However, to further explore and potentially pursue towards development of therapeutic therapies, it is of essence to further understand how AKIP1 activates these pathways. The role of AKIP1 may be mediated through factors upstream of Akt such as the IGF1 receptor or PI3K, but it may also interact directly with Akt or its phosphatases. Of note, the Akt phosphatase PP2Ac was found to be upregulated in our study. Alternatively, AKIP1 may act upon the direct interaction between these two pathways, possibly at the C/EBPβ-SRF link. Additionally, it should be further validated whether changes in these signalling pathways are causing physiological cardiac hypertrophy, or whether these changes are resulting from physiological cardiac hypertrophy. Future research attempts involving in vitro and knock-out mouse models should provide more in-depth mechanistic insights into the mechanisms responsible for AKIP1-mediated activation of these signalling pathways.
## Conclusion
Taken together, we provide the evidence that AKIP1 promotes cardiomyocyte elongation and physiological cardiac remodelling with activation of the RSK3-PP2Ac-SRF and Akt-C/EBPβ-CITED4 pathway. This study provides evidence that AKIP1 may be a potential therapeutic target to shift pathological remodelling towards a more physiological phenotype.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30514-1.
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|
---
title: Impact of high altitude on composition and functional profiling of oral microbiome
in Indian male population
authors:
- Manisha Kumari
- Brij Bhushan
- Malleswara Rao Eslavath
- Ashish Kumar Srivastava
- Ramesh Chand Meena
- Rajeev Varshney
- Lilly Ganju
journal: Scientific Reports
year: 2023
pmcid: PMC10006418
doi: 10.1038/s41598-023-30963-8
license: CC BY 4.0
---
# Impact of high altitude on composition and functional profiling of oral microbiome in Indian male population
## Abstract
The oral cavity of human contains bacteria that are critical for maintaining the homeostasis of the body. External stressors such as high altitude (HA) and low oxygen affect the human gut, skin and oral microbiome. However, compared to the human gut and skin microbiome, studies demonstrating the impact of altitude on human oral microbiota are currently scarce. Alterations in the oral microbiome have been reported to be associated with various periodontal diseases. In light of the increased occurrence of HA oral health related problems, the effect of HA on the oral salivary microbiome was investigated. We conducted a pilot study in 16 male subjects at two different heights i.e., H1 (210 m) and H2 (4420 m). Total of 31 saliva samples,16 at H1 and 15 at H2 were analyzed by utilizing the 16S rRNA high-throughput sequencing, to explore the relationship between the HA environment and salivary microbiota. The preliminary results suggesting that, the most abundant microbiome at the phylum level are: Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Interestingly, 11 genera were identified at the both heights with different relative abundances. In addition, the salivary microbiome was more diverse at H1 compared to H2 as demonstrated by decreased alpha diversity. Further, predicted functional results indicate that microbial metabolic profiles significantly decreased at H2 as compared to H1, including two major metabolic pathways involving carbohydrates, and amino acids. Our findings show that HA induces shifts in the composition and structure of human oral microbiota which can affect host health homeostasis.
## Introduction
Human oral microbiota is the second most complex and diverse microbial community after gut, inhabited principally by Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes and Fusobacteria1. As per the Human Oral Microbiome Database more than 1100 different taxa are reported2, out of which genera Streptococcus, Veillonella, Neisseria, and Actinomyces are associated with the core microbiome, shared by most healthy individuals3. Presence of various niches inside the oral cavity and the nasopharyngeal areas provide suitable environment for microorganisms to grow2.
Composition of oral microbiota is influenced by host genetics4, geography5, diet6, age7, and environment8, suggesting that periodontal health or disease depends on the interface between the host and the microbial community as a whole. To be specific, oral microbial diversity is a strong indicator of oral health and overall human health. Dysbiosis, or an imbalance in the oral microbiome, has been related to various local and systemic human disorders, such as dental caries, obesity, diabetes, and cardiovascular disease2,9–11.
Studies demonstrating the impact of altitude on human oral microbiota are currently scarce. However, much recent evidence accumulated from Tibetan plateau shows that diversity in oral microbiota gets altered living at different altitudes and the ecological mechanisms associated with it respond differently as compared to low altitude natives. Recent studies on animal models exposed to chronic hypoxia showed increased risk of periodontitis development due to increased oxidative stress and inflammatory parameters in sub-mandibular glands12,13. A study conducted at an elevation of 3550 m reported a prevalence of dental problems such as gingival bleeding, dental pain, lost fillings and dental fractures in $23.2\%$ of trekkers14. A significant decrease in salivary flow15 has also been reported during prolonged stay at HA areas16 which is known to increase the risk of caries17. Studies conducted at Tibetan plateau showed that oral microbiota is much more diverse at low altitude as compared to the HA Zhang population (living at an altitude of 3000–4000 m)18,19. The study also found high abundance of *Porphyromonas gingivalis* in people living at HA19. Porphyromonas gingivalis is closely related to the occurrence of periodontal diseases and is one of the main microbes detected in the saliva of periodontitis patients20. Studies have also established a po+sitive correlation between ecosystem stability and species diversity. One recent study at HA area of Qinghai-Tibet plateau (average altitude of 4000 m) has shown that alpha diversity decreases with altitude21, which might be responsible for increased occurrence of dental caries at HA (above 3500 m). The study also revealed an increased relative abundance of Prevotella, many species of which are prominent periodontal-pathogen. Understanding what constitutes microbial communities in oral cavity, is crucial as human mouth, the portal of entry to both the gastrointestinal (GI) and respiratory tract is in direct contact with the external environment and hence external environment plays a vital role in framing the oral microbiome.
In this study we assessed changes in oral microbiota composition in Indian male subjects of the same ethnicity who ascended to extreme altitude. To understand this, we used high-throughput 16S ribosomal RNA (rRNA) gene amplicon sequencing to characterize oral microbial diversity. It is already established that oral microbiota shares a close and intricate relationship with various health problems, the primary objective of this pilot study was to investigate the impact of HA on oral microbiota composition and functional prediction from healthy individuals ascending to HA.
## Subject recruitment
From a group of HA sojourners who had not ascended higher than 3000 m in the previous six months, sixteen participants of North Indian origin were selected. All the participants were males in the age group of 22–55 years, had normal weight (BMI = 20–24 kg/m2) and undergone thorough medical and psychological examinations for any diseases to ensure a healthy population. On the first day of examination, information on medication status was obtained by questionnaire and interview. No oral diseases, no recent antibiotic usage (within three months), and no eating or chewing gum two hours prior to sample collection were the inclusion criteria. Due to the harsh extreme environmental condition and logistic challenges, there was no enough choice of food items. The only food available was what the sojourners carried with themselves and hence everyone consumed similar type of food.
## Ethical statement
All participants understood the nature of the study and gave their written informed consent. Ethics approval was obtained from Research Ethics Committee of Defence Institute Physiology and Allied Sciences. All other study protocols were in accordance with Helsinki’s approved guidelines.
## Sample collection
Approximately 2 ml of passive saliva samples were collected in sterile vials between 7 and 9 am in the morning. All subjects were requested to refrain from drinking, eating, and oral hygiene activities (including rinsing with mouthwash) for at least 1 h before sample collection. The first sample collection was performed at sea level H1 (210 m) and second and final at H2 (4420 m) after staying for 6 months. Total of 31 saliva samples from 16 subjects at H1 and 15 subjects at H2 were analyzed for oral microbiota composition. The samples were placed on ice, and a protease inhibitor cocktail was added at a ratio of 100 ml per ml of saliva. Immediately after the addition of protease inhibitor, samples were frozen at −22 °C without culturing and finally transferred to −80 °C freezer till further processing.
## Sample preparation and DNA extraction
Two ml saliva collected in sterile vial was diluted with 4 ml PBS and centrifuged at 1800×g for 5 min. Genomic DNA was isolated from the pellet using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). The quantity and quality of isolated DNA were measured using Nano Drop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and agarose gel electrophoresis (BioRad, USA), respectively. DNA extracted from samples were normalized and then stored at −20 °C until further use.
## Amplification of V3-V4 region of 16S rRNA, library preparation and sequencing
16S rRNA sequencing was conducted on Illumina MiSeq platform. To amplify and sequence the V3-V4 hyper-variable regions of the 16S rRNA gene, the 341F and 805R universal primers were used targeting a region of approximately 464 bp encompassing variability22.
The V3–V4 primer and the adapter details are as mentioned below.
V3- Illumina_16S_341F 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG V4-Illumina_16S_805R 5′GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC
In accordance with the Human Microbiome Project (USA)23, this region provides sample information for the categorization and identification of microbial communities from specimens related with human microbiome investigations. The final amplified amplicon libraries were purified using AMPure XP beads (Beckman Coulter Genomics, Denver, MA, USA) and the size of the amplicon library wereassessed on the bioanalyser (Agilent technologies, USA). The library quantification kit for Illumina (Kapa Biosciences, Woburn, MA, USA) was used to assess the quantity of the amplicon. Paired-end (250 bp) sequencing was carried out at the Illumina MiSeq platform.
## Bioinformatic analysis of 16S rRNA gene amplicon sequences
Quality of the raw data was checked using FastQC software. Raw paired-end Illumina reads were trimmed using fastx toolkit (version 0.0.13). R1 and R2 paired-end reads were assembled, using PANDAseq and average read of ~ 464 base pairs was generated24. Sequencing of saliva samples for oral microbiota generated approximately a total of 1973 MB data for 31 saliva samples (approximately 63.6 MB data/sample) (Table S1). QC stats of the data have been shown in Table S2. Sequences were grouped into OTUs on the basis of similarity to known bacterial sequences (at $97\%$ sequence similarity cut-off) available in the Greengenes databases (version 13.8; https://greengenes.secondgenome.com/)25 using QIIME 1.9.126. Unmatched sequences were further clustered de novo based on pair-wise sequence identity. CSS normalization was applied to correct biases. Statistical analyses were executed using the Microbiome Analyst pipeline27.
## Functional profiling
High-quality sequencing data and sample information was used to predict the functional profiling of microbiome samples, using PICRUSt (version1.1.3)28 pipeline followed by STAMP (version 2.1.3) software to calculate functionally differential KEGG Pathways (www.kegg.jp/kegg/kegg1.html) between H1 and H2 group using Welch's test.
## Statistical analysis
31 Saliva samples (16 at H1 and 15 at H2) were analyzed by 16S rRNA sequencing. The alpha diversity was calculated by T-test and determined by Chao1, Shannon and Simpson’s. Beta diversity was determined by PERMANOVA (Permutational multivariate analysis of variance) and PCoA plots based on Bray–curtis dissimilarity distance were plotted (D-05 Unifrac for robust trade-offs between rare and abundant lineages). PERMANOVA was applied to identify statistical significance of beta diversity between groups (at $5\%$ p value significance at phylum and genus level). Significantly differential microbiota at Phylum and genera level were mined using the EdgeR package of the language R and then visualized with a volcano plot. Multiple testing was corrected with FDR correction of the p-value at a $5\%$ threshold.
## Results
Present study demonstrates the sequencing of V3-V4 regions of 16S rRNA from 31 saliva samples. In total, 1,881,630 sequences were obtained from the 31 samples, with an average sequence length of 251 bp (Table S1). The rarefaction curve of all samples calculated had reached a plateau, suggesting that the sequencing was sufficient to represent its true diversity (Fig. S1). Cumulative sum scaling (CSS) normalized relative abundance was calculated at the genus and phylum level (Tables S5 and S6). The mean sequence length was 251 base pair. The clustering of qualified sequences at $97\%$ identity resulted in 1,292 OTUs (Table S7). The analysis was performed using QIIME1.9.1, PICRUSt 1.1.4 and Microbiome analyst (https://www.microbiomeanalyst.ca/), rarefaction curve of all samples reached a plateau, suggesting that the sequencing was deep enough at species level (Fig. S1).
## Oral microbiota composition
A total of 31 phyla, 59 classes, 95 orders, 136 families, 146 genera, and 48 species were detected from all the 31 samples. Microbial community, analyzed by 16S rRNA sequencing, at phylum level (Fig. 1a and b, and Tables S5) depicted that oral microbiota at H1 was dominated by Firmicutes ($48\%$), followed by Proteobacteria ($23\%$), Bacteroidetes ($12\%$) and Actinobacteria ($12\%$). After staying at H2 for six months there was a marginal decrease in Firmicutes ($43\%$), Bacteroidetes ($11\%$) and Actinobacteria ($11\%$) and an increase in Proteobacteria ($29\%$) (Fig. 1a), however, none of the changes reached significant. Out of the top 13 phylum (with relative abundance $0.1\%$), TM7 ($p \leq 0.0001$, FDR q < 0.0001) and Tenericutes ($p \leq 0.05$, FDR q < 0.05) showed a significant alteration in their abundance at H2 (Fig. 1a).Figure 116S sequencing analysis of the variation in the oral microbiome at H1 and H2. ( a) Bar chart depicts average relative abundance of the bacterial taxonomic hits at the phylum level in the saliva samples at H1 and H2 (**, $P \leq 0.01$, ****, $P \leq 0.0001.$ For TM7, $$p \leq 0.0000002$$; for Tenericutes, $$p \leq 0.010$$). ( b) Bar chart depicts average relative abundance of the bacterial taxonomic hits at the genera level in the saliva samples at H1 and H2 (****, $P \leq 0.0001.$ For Selenomonas, $$p \leq 0.00002$$).
At H1 time point, the most prevalent genera were Streptococcus ($30.06\%$), Haemophilus ($9.58\%$), Prevotella ($8.96\%$), Neisseria ($8.83\%$), Actinomyces ($6.35\%$), Veillonella ($4.79\%$), Rothia ($4.01\%$), Porphyromonas ($2.22\%$), Fusobacterium ($2.03\%$), Granulicatella ($1.49\%$), Staphylococcus ($1.49\%$), Aggregatibacter ($1.13\%$) and others with relative abundance less than $1\%$ (Fig. 1b and Table S6). The predominant bacteria were largely consistent at both the heights, with different average relative abundances at H1 and H2. After stay at H2, the average relative abundance of *Streptococcus decreased* to $28.25\%$, followed by Neisseria ($10.29\%$), Prevotella ($9.04\%$), Rothia ($4.30\%$) and Fusobacterium ($2.18\%$) which increased but could not reach significant. On the contrary, genera Haemophilus ($6.79\%$), Actinomyces ($5.46\%$), Veillonella ($3.62\%$), Porphyromonas ($2.06\%$), Granulicatella ($1.16\%$), Staphylococcus ($0.75\%$), and Aggregatibacter ($0.31\%$) were reduced. Comparing relative abundance of the 20 richest genera between two heights, a significant higher abundance of Selenomonas was observed at H1 as compared to H2 ($p \leq 0.0001$, FDR q < 0.0001) (Fig. 1b).
## Variations in oral microbiota diversity with altitude
To understand the structural aspects of the microbial community various bacterial diversity metrices were employed. Three indices (Chao1, Shannon Index and Simpson), were employed to estimate the alpha diversity at different altitudes. A significant change was observed between H1 and H2 after the stay, according to Chao1 index at phylum and genus level ($$p \leq 0.007$$ and 0.012 respectively). On the other hand, neither the Shannon nor Simpson diversity indices reflected any significant difference (Fig. 2a, b, and Table S3).Figure 2Comparison of alpha diversity between H1 and H2 at phylum and genus level. ( a) Boxplots showing the differences in the alpha diversity indices (Chao 1, Shannon and Simpson) at phylum level at H1 and H2. ( b) Boxplots showing the differences in the alpha diversity indices (Chao 1, Shannon and Simpson) at genus level at H1 and H2.
Beta diversity analysis was performed to assess the composition of the microbial communities between samples from the two heights. Based on the Bray–Curtis distances of the 16S ribosomal DNA sequencing profiles at phylum and genus level, PERMANOVA analysis and PCoA plot was generated. Results demonstrated separate clusters, suggesting some differences in the communities (significant differential distribution of oral microbiota at $$p \leq 0.05$$), between altitudes H1 and H2 (Fig. 3a, b, and Table S4).Figure 3Principal *Coordinate analysis* (PCoA) on the distance matrix of Bray–curtis at H1 and H2 from 16S rRNA. PCoA of the oral microbiome at H1 and H2 at phylum (a) and genera level (b).
## Predicted functional profiling of oral microbiota at H1 and H2
To evaluate the effect of altitude on oral microbiota, high-quality reads from all samples were assembled and annotated for protein-coding genes by PICRUSt and STAMP for investigating functional potential at levels 1, 2 and 3 (Table S8).
Comparative analysis of microbial metabolic profiles at H1 and H2 demonstrate a significant decrease in functional genes, including two major metabolic pathways involving carbohydrates, and amino acids (Fig. 4). More specifically, in carbohydrate metabolism, butanoate ($p \leq 0.05$), propanoate ($p \leq 0.05$), inositol phosphate ($p \leq 0.01$), and C5-branched dibasic acid metabolism ($p \leq 0.05$) were majorly affected. Figure 4PICRUSt predicted Functional profile of Kyoto Encyclopedia of Genes and Genomes (KEGG) categories for oral microbiota with significantly different KEGG pathways at H1 and H2 using Welch’s t-test. Kyoto Encyclopedia of Genes and Genomes, ($95\%$ Confidence Interval (CI). Bar plots displayed the mean proportion of each KEGG pathway. P values were adjusted and corrected for the false discover rate. “*” and “**” indicate the significance level at 0.05 and 0.01, respectively.
In amino acid metabolism, degradation of valine, leucine, isoleucine, and lysine, biosynthesis of phenylalanine, tyrosine, tryptophan, and lysine, amino acid related enzymes, metabolism of histidine, cyanoamino acid, and tryptophan, the urea cycle were significantly affected (Fig. 4). In addition to these primary essential metabolic pathways, other pathways affected were, metabolism of terpenoids and polyketides (e.g., limonene, pinene and geraniol degradation), xenobiotics biodegradation and metabolism (e.g., benzoate, and aminobenzoate degradation).
## Differential abundance analysis
The analysis revealed 5 phylum and 16 genera differntially abundant between the two groups. Phylum namely, Tenericutes was highly abundant at H2 as compared to H1 while Phylum TM7, Chloroflexi, Cyanobacteria, and Armatimonadetes showed a lower abundance in the H2 group (Fig. 5a).Figure 5Volcano plot showing differential microbiota at (a) Phylum and (b) Genera level between H1 and H2 groups. Log-transformed fold change in expression is plotted on the x-axis and log-transformed false discovery rate-adjusted p-values plotted on the y-axis. The differential microbiota between the H1 and the H2 group were analyzed using the EdgeR package of the language R, in accordance with corrected P value < 0.05 and fold-change ≥ ± 2 and volcano plot was generated.
On the other hand, at genus level, 7 genera: Pseudomonas, Comamonas, ML110J_20, Micrococcus, Gallibacterium, Hydrogenophaga, and Moraxella showed a higher abundance at H2 group; and whereas 9 genera, namely, Selenomonas, Peptoniphilus, Azoarcus, Acinetobacter, Paenibacillus, DA101, Rhodoplanes, Nocardioides, and Agrobacterium showed a lower abundance in the H2 group (Fig. 5b).
## The potential link between taxonomy and functional pathways
We identified the correlation between several microbial genera with differential abundant functional pathways. Spearman’s rank correlation coefficient of microbial genera and predictive function pathways based on PICRUSt. The value $r = 1$ or close to 1, represents a strong positive correlation and the value r = − 1 or close to − 1, represents a strong negative correlation. p value < 0.05 was considered statistical significant.
Interestingly, the functional pathways of oral microbiota were found to have more and stronger correlations with microbial genera, (Tables 2 and S9). The majority of the pathways showed positive correlation ($r = 0.086$–0.621) with several groups of genera which implies that the influence of oral microbiota on the functional profile was more likely through the combinatorial effects of multiple bacteria, or microbial consortium, rather than individual microbial genera.
## Discussion
Microbes co-exist in and on the human body and greatly impact human health. The oral cavity which directly communicates with the external environment being portal of entry29,30, is one of the most significant factors impacting the oral microbiota31. Local oral environment and socio-environmental/economic variables have been ambiguous about the impact in on the makeup of the salivary microbiome which in turn reflects the integrity of periodontal health. In oral homeostasis, the microflora and host immunity have a symbiotic relationship32, as this balance promotes immunity in the oral cavity and enhances systemic immunity to prevent oral diseases11. However, when this balance is disrupted, it increases inflammation and may initiate several oral or systemic diseases9,10,32,33.
In the present pilot study, we characterized the salivary microbiome of 16 HA sojourners and evaluated variations caused by the environmental factors at HA. Such changes may lead to more dysbiosis in the oral microbiome, resulting aberrant inflammatory responses. Through the analysis, we found few significant changes in oral microbiota between the two heights. In terms of the composition of the oral microbiota, the abundance of Firmicutes, Bacteriodetes, and Actinobacteria decreased at H2. On the contrary, the abundance of Proteobacteria increased at H2 as compared to H1. Consistent with the results of other studies, a decreasing trend of Firmicutes with altitude (4500 m) was observed21. In addition, Fig. 1 shows that at phylum level, microbial communities at H2 were characterized by decreased abundance of TM7 and increased abundance of Tenericutes, though their relative abundance was very low at sea level. *At* genera level, the abundance of Streptococcus, Haemophilus, Actinomyces, Veillonella, Porphyromonas, and Granulicatella decreased at H2 as compared with H1 and the abundance of Neisseria, Rothia, and Fusobacterium increased at H2. The cumulatively identified bacterial genera, were well represented in the human oral microbiome database and previously identified as members of the human salivary microbiome2,34,35. All of these genera are considered to be closely related to human health. For instance, *Porphyromonas is* not only associated with the occurrence of periodontitis, but it is also a driving factor for developing tumors in the gastrointestinal tract36.
In our study at genus level, the genera Prevotella occupies $8.9\%$ at H1 and increased to $9.04\%$ at H2. Though, it is a very minute and insignificant difference, but similar kind of pattern of increment with altitude has been reported by another study at 4500 m21. Prevotella is the predominant genus found in gut and oral microbiome at HA42. While the presence of Streptococci and Prevotella species is common in the general population, recent research has suggested that these bacteria may be associated with increased inflammation and other changes that could potentially contribute to the development of progression of oral cancer37. Reduction in salivary secretion at HA often gives a feeling of dry mouth and tongue15, and reduced salivary flow is directly correlated with altered oral microbiota composition38,39. Because the host is unable to balance the acidic environment, caries forming bacteria can flourish in this setting. In this study, we also report changes in bacterial genera including Streptococcus, Actinomyces, Veillonella, Aggregatibacter, and Fusobacterium which are engaged in the development and maturation of dental biofilms. However, it is important to note that the relationship between these bacteria and oral diseases at HA is not fully understood, and further research with a large sample size is needed to establish any causal links.
In addition, of these dominant genera in the oral cavity are not pathogenic in a healthy state, but they may cause diseases that affect the host’s health during compromised immune system and altered oral microbiota. HA has been known to cause alterations in different immune cells associated with innate and adaptive immunity. Mucosal immune system which is an important branch of adaptive immune response is also vulnerable to invading pathogens and infections at HA which further needs investigation40,41.
The temperature of the environment has an impact on the human microbiota, and thus, is a key determinant in bacterial abundance42. The ideal temperature of 37 °C, is considered a favorable temperature for growth of most parasitic microorganisms on the human body43. For example, compared to a German population living in a warmer climate, Alaskan humans living in a colder environment have much less alpha diversity of the oral microbiota44. In consistent with our study, a recent study also revealed that alpha diversity significantly decreased with increase in altitude21. Other studies have also shown that bacterial composition altered with the temperature drop45,46. Thus, it can be speculated that low temperature at HA is one of the contributing factors affecting the oral microbiome diversity (Table 1).Table 1Environmental features. H1 (Meerut)H2 (Karakoram ranges)Geographical location28°57′ to 29°02′ N32.89° to 33.12° N77°40′ to 77°45′ E77.53°E to 77.78° EAltitude210 m4420 mTemperature during the stay period10 °C (Lowest)− 0.3 °C (Lowest)25 °C (Highest)6.4 °C(Highest) The functional prediction of oral bacterial populations from 16S marker sequences was elucidated by PICRUSt. The technique is frequently used, although it has certain drawbacks, including the requirement for OTUs as input sequences produced using closed-reference OTU-picking against the Greengenes database. Regarding microorganisms and their function in stepwise processes in a particular pathway, PICRUSt does not offer any clarity. However, adopting PICRUSt for functional prediction in this exploratory pilot study was done to establish the impact of altitude on functional potential of oral microbiome.
Decreased carbohydrate metabolism at H2 correlates with decreased abundance of saccharolytic genera *Streptococcus and* Lactobacillus. Another pathway, ABC transporters also showed decreased expression at H2. ABC transporter systems plays important biological roles in transport of oligosaccharides, including melibiose, raffinose, stachyose and maltodextran47 and its decreased activity leads to reduced carbohydrate metabolism and eventually reduced growth of saccharolytic bacteria. Butyrate and propionate are the most common SCFA (Short Chain Fatty Acids) after acetate. Reduced SCFAs level were reported to be responsible for increased oral mucosal TH2 immunity leading to risk of pro-inflammatory response in subjects with peanut allergy. This suggests that SCFAs can regulate the inflammatory response and may represent a link between the microbiota and the immune system48,49.
Furthermore, Xenobiotic biodegradation and metabolism correlates with the process of detoxification, where microbes play important role by degrading xenobiotics, which are usually caused by the release of industrial pollutants. In our findings, it is not surprising to find a decreased metabolic activity of xenobiotic biodegradation at HA, with minimal industrial pollution. We also emphasized on the relationship between genus and altered pathways using Spearman correlation (Table 2). PICRUSt datasets with 3rd tier functional classification (Fig. 4) were employed to determine genus abundance contributing to altered functional pathways between the two heights. Interestingly, most of the genus showed a strong correlation with functional pathways, suggesting their involvement the functional traits. It was clearly evident genera such as *Pseudomonas and* Acinetobacter which were differentially abundant at H2 (Fig. 5b) were also found to be associated with metabolism pathways (Table 2). But at the same time genera consist of many species can negatively or positively regulate a pathway and show low taxonomical resolution in 16S rRNA sequencing reads is one of the limiting factors. Table 2Potential links between oral microbiota genera and functional pathways using Spearman’s rank correlation coefficient.1st Tier2nd Tier3rd TierSpearman (r)p valueGenusMetabolismAmino acid metabolismLysine biosynthesis0.3960.028Campylobacter, Citrobacter, Enterobacter, EscherichiaAmino acid metabolismLysine degradation− 0.0920.622Citrobacter, Enterobacter, Escherichia, FusobacteriumCarbohydrate metabolismGalactose metabolism0.4180.019Citrobacter, Enterobacter, Escherichia, FusobacteriumCarbohydrate metabolismPentose phosphate pathway0.4460.012Acinetobacter, Citrobacter, Enterobacter, EscherichiaEnergy metabolismNitrogen metabolism0.0860.645Acinetobacter, Citrobacter, Enterobacter, EscherichiaEnergy metabolismSulfur metabolism0.5150.003Citrobacter, Enterobacter, Escherichia, NeisseriaGlycan biosynthesis and metabolismLipo-polysaccharide biosynthesis0.1330.475Acinetobacter, Citrobacter, Enterobacter, EscherichiaGlycan biosynthesis and metabolismPeptidoglycan biosynthesis0.4510.011Acinetobacter, Campylobacter, Citrobacter, EnterobacterLipid metabolismFatty acid biosynthesis0.6210.000Acinetobacter, Campylobacter, Citrobacter, EnterobacterLipid metabolismGlycero-phospholipid metabolism0.1760.342Fusobacterium, Pseudomonas, StreptococcusMetabolism of cofactors and vitaminsFolate biosynthesis0.3610.046Citrobacter, Enterobacter, Escherichia, HaemophilusNucleotide metabolismPurine metabolism0.4250.017Acinetobacter, Campylobacter, Citrobacter, EnterobacterNucleotide metabolismPyrimidine metabolism0.4470.012Acinetobacter, Campylobacter, Citrobacter, EnterobacterXenobiotics biodegradation and metabolismBenzoate degradation-0.1700.362AcinetobacterXenobiotics biodegradation and metabolismToluene degradation0.1470.431PseudomonasGenetic information processingReplication and repairDNA replication0.2740.136Citrobacter, Escherichia, HaemophilustranslationAminoacyl-tRNA biosynthesis0.4520.011Acinetobacter, Campylobacter, Citrobacter, Enterobacter The relationship between metabolic pathways and the genera responsible for has not been explored yet. Most of the pathways with significant alterations at 1, 2 and 3 tier are positively correlated to metagenome with r score more than 0. Some species differentially express pathways in association with other genera to produce active molecules, precursors, enzymes, hormones or metabolites to regulate host metabolism. However, dysbiosis may lead to alterations in the concerned metabolic pathways. Therefore, it needs an in depth analysis to fully elucidate the mechanistic interaction by which a group of genera participate in a metabolic pathway.
The results of this pilot study represents the exploratory description of the oral microbiome in individuals exposed to HA. Analysis of the salivary microbiome from the subjects residing at HA (4420 m above sea level) was compared with sea level controls for the first time and the findings of our study will provide preliminary baseline information for future research. Finally, our findings suggest that altitude has an impact on the oral microbiota's microbial composition, diversity, community structure, and function. Furthermore, future work should explore the relationship among altitude, oral microbiome, and periodontal health in large cohort is warranted to mitigate the problems encountered by the sojourners after travelling to or stay at HA.
## Limitations and merits
The main aim of this pilot study was to analyze the effects of extreme environmental conditions on the oral microbiota of subjects ascending to HA from sea level. However, some limitations were associated with the study such as small subject size which eventually limits the statistical power. But because of logistic issues, the availability of a small subject size is unavoidable. Additionally, the study was restricted to male participants exclusively. Due to the individuals' unavailability and unwillingness to provide follow-up samples after returning to 210 m, no follow-up analysis was feasible. Expanding the investigation of the oral microbiome using a combination of shotgun metagenomic, meta-transcriptomic, and metabolomics in a bigger cohort of population is warranted.
## Supplementary Information
Supplementary Tables. Supplementary Figure S1. The online version contains supplementary material available at 10.1038/s41598-023-30963-8.
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|
---
title: 'Effect of high-fat diet and morning or evening exercise on lipoprotein subfraction
profiles: secondary analysis of a randomised trial'
authors:
- Trine Moholdt
- Evelyn B. Parr
- Brooke L. Devlin
- Guro F. Giskeødegård
- John A. Hawley
journal: Scientific Reports
year: 2023
pmcid: PMC10006421
doi: 10.1038/s41598-023-31082-0
license: CC BY 4.0
---
# Effect of high-fat diet and morning or evening exercise on lipoprotein subfraction profiles: secondary analysis of a randomised trial
## Abstract
We investigated the effect of a high-fat diet (HFD) on serum lipid subfractions in men with overweight/obesity and determined whether morning or evening exercise affected these lipid profiles. In a three-armed randomised trial, 24 men consumed an HFD for 11 days. One group of participants did not exercise ($$n = 8$$, CONTROL), one group trained at 06:30 h ($$n = 8$$, EXam), and one group at 18:30 h ($$n = 8$$, EXpm) on days 6–10. We assessed the effects of HFD and exercise training on circulating lipoprotein subclass profiles using NMR spectroscopy. Five days of HFD induced substantial perturbations in fasting lipid subfraction profiles, with changes in $\frac{31}{100}$ subfraction variables (adjusted p values [q] < 0.05). Exercise training induced a systematic change in lipid subfraction profiles, with little overall difference between EXam and EXpm. Compared with CONTROL, exercise training reduced serum concentrations of > $20\%$ of fasting lipid subfractions. EXpm reduced fasting cholesterol concentrations in three LDL subfractions by ⁓$30\%$, while EXam only reduced concentration in the largest LDL particles by $19\%$ (all q < 0.05). Lipid subfraction profiles changed markedly after 5 days HFD in men with overweight/obesity. Both morning and evening exercise training impacted subfraction profiles compared with no exercise.
## Introduction
High levels of circulating low-density lipoprotein (LDL) cholesterol is a major risk factor predisposing to atherosclerotic cardiovascular disease (ASCVD), and the primary target for lipid-lowering therapies1,2. Furthermore, the inverse relationship between plasma high-density lipoprotein (HDL) cholesterol and ASCVD risk is among the most robust and reproducible associations in observational epidemiology3. Thus, HDL cholesterol is included as a critical component in ASCVD risk prediction guidelines from both the European Society of Cardiology and the American Heart Association4,5. However, lipid fractions in the blood vary in particle size, density, and in concentrations and composition of lipoproteins. Conventional measures of circulating lipids cannot differentiate between the various subfractions, many of which may have contrasting relationships with risk of ASCVD. For example, small dense LDL particles are associated with incident ASCVD, independent of traditional risk factors including total LDL cholesterol concentrations6. Moreover, only the largest subclasses of HDL, and not small HDL, were inversely associated with risk of myocardial infarction in the China Kadoorie Biobank7.
Lifestyle modification is the cornerstone of ASCVD prevention. Guidelines for lipid modification to reduce cardiovascular risk advocate diets low in saturated fat with a focus on wholegrain products, vegetables, fruit, and fish4. However, the results of several systematic reviews and meta-analyses8,9, as well as one of the most extensive studies in recent years on the association of fats and carbohydrate intake with ASCVD and mortality (PURE)10, do not support the guidelines that advocate low consumption of total saturated fats. Furthermore, a recent systematic review and meta-analysis reported that dietary interventions which restricted carbohydrate intake (and were high in dietary fat) decreased the numbers of total and small LDL particles11.
A physically active lifestyle is associated with substantially reduced ASCVD mortality12. Aerobic exercise training can improve lipid profiles, inducing reductions in overall concentrations of LDL and triglycerides, and increased HDL concentrations13, but current evidence is equivocal14. We reported substantial alterations in lipid-related serum metabolites and elevations in LDL cholesterol after a short-term high-fat diet (HFD) intervention in men with overweight/obesity, and showed that some of these alterations were reversed after daily exercise training performed in the evening for just 5 days15. In this secondary analysis of a randomised trial, we determined the lipoprotein subclass profile using nuclear magnetic resonance (NMR) spectroscopy after 5 days of HFD, and assessed whether exercise training undertaken in the morning or in the evening would modulate the effects of the HFD on lipoprotein subclass profiles.
## Participants
Twenty-four of the 25 participants completed the full protocol (Fig. 1). Participants were aged 36 ± 4 years and had a body mass index (BMI) 31.2 ± 2.3 kg/m2 at baseline. Table 1 shows baseline characteristics of participants, according to group. Data collection commenced in March 2017 and was completed in August 2017, with the lipid NMR analyses undertaken in February 2021. Primary findings from the trial are published elsewhere15. There were no unintended or adverse effects of the intervention. Figure 1Flowchart of participants. Table 1Baseline characteristics of participants included in analyses, according to group. Lipids measured by clinical chemistry. Data are mean ± SD.Morning exercise ($$n = 8$$)Evening exercise ($$n = 8$$)Control group ($$n = 8$$)Age, years35 ± 436 ± 536 ± 4Body mass, kg103.1 ± 16.199.3 ± 8.194.5 ± 10.4BMI, kg/m231.9 ± 1.931.0 ± 2.830.7 ± 2.3Fat mass, kg36.1 ± 7.032.9 ± 5.331.3 ± 3.0Fat mass, %36.0 ± 3.134.1 ± 3.433.6 ± 3.6Fat-free mass, kg67.2 ± 10.166.7 ± 3.965.4 ± 9.0Visceral fat mass, g2013 ± 14241480 ± 5731969 ± 495Systolic blood pressure, mm Hg126 ± 10131 ± 9129 ± 8.6Diastolic blood pressure, mm Hg81 ± 584 ± 785 ± 8.6Peak oxygen uptake, mL·kg−1·min−128.5 ± 6.131.2 ± 4.429.0 ± 4.3Cholesterol (mmol/L)5.0 ± 0.84.3 ± 0.64.9 ± 0.8HDL-cholesterol (mmol/L)1.01 ± 0.21.10 ± 0.20.99 ± 0.2LDL-cholesterol (mmol/L)3.3 ± 0.62.7 ± 0.53.1 ± 0.8Triglycerides (mmol/L)1.63 ± 0.71.23 ± 0.61.78 ± 0.8
## Changes in lipoprotein profiles after 5 days of high-fat diet
Supplementary Table 1 shows the correlation coefficients of total cholesterol, LDL, HDL, and triglycerides concentrations as measured by clinical biochemistry and NMR spectroscopy. Principal component analysis (PCA) trajectories from before to after 5 days of HFD showed systematic changes in lipoprotein profiles after initiating the HFD. In the fasting samples, there was a tendency of increase along PC1, while these changes were even more evident in the postprandial samples, with a clear increase along PC3 (Supplementary Figure 1). When removing the between-subject variation in a multilevel partial least squares discriminant analyses (PLS-DA), we observed substantial changes in the lipoprotein subfraction profiles after 5 days of HFD (Fig. 2).Figure 2Scores and loading plots from multilevel partial least squares discriminant analyses for discriminating lipoprotein profiles at baseline (habitual diet) from those after 5 days of high-fat diet. ( a, b) in fasted samples (c, d) in postprandial samples. LV = latent variable, A1 = apolipoprotein A1, A2 = apolipoprotein A2, AB = apolipoprotein B100, CH = cholesterol, TG = triglycerides, VLDL = very-low-density lipoprotein, FC = free cholesterol, PL = phospholipids, IDL = intermediate-density lipoprotein, LDL = low-density lipoprotein, HDL = High-density lipoprotein. 1: Total Serum A1, 2: Total Serum A2, 3: Total Serum AB, 4: Total Serum CH, 5: Total Serum TG, 6: VLDL AB, 7: VLDL CH, 8: VLDL FC, 9: VLDL PL, 10: VLDL TG, 11: VLDL1 CH, 12: VLDL-1 FC, 13: VLDL-1 PL, 14: VLDL-1 TG, 15: VLDL-2 CH, 16: VLDL-2 FC, 17: VLDL-2 PL, 18: VLDL-2 TG, 19: VLDL-3 CH, 20: VLDL-3 FC, 21: VLDL-3 PL, 22: VLDL-3 TG, 23: VLDL-4 CH, 24: VLDL-4 FC, 25: VLDL-4 PL, 26: VLDL-4 TG, 27: VLDL-5 CH, 28: VLDL-5 FC, 29: VLDL-5 PL, 30: VLDL-5 TG, 31: IDL AB, 32: IDL CH, 33: IDL FC, 34: IDL PL, 35: IDL TG, 36: LDL AB, 37: LDL CH, 38: LDL FC, 39: LDL PL, 40: LDL TG, 41: LDL-1 AB, 42: LDL-1 CH, 43: LDL-1 FC, 44: LDL-1 PL, 45: LDL-1 TG, 46: LDL-2 AB, 47: LDL-2 CH, 48: LDL-2 FC, 49: LDL-2 PL, 50: LDL-2 TG, 51: LDL-3 AB, 52: LDL-3 CH, 53: LDL-3 FC, 54: LDL-3 PL, 55: LDL-3 TG, 56: LDL-4 AB, 57: LDL-4 CH, 58: LDL-4 FC, 59: LDL-4 PL, 60: LDL-4 TG, 61: LDL-5 AB, 62: LDL-5 CH, 63: LDL-5 FC, 64: LDL-5 PL, 65: LDL-5 TG, 66: LDL-6 AB, 67: LDL-6 CH, 68: LDL-6 FC, 69: LDL-6 PL, 70: LDL-6 TG, 71: HDL A1, 72: HDL A2, 73: HDL CH, 74: HDL FC, 75: HDL PL, 76: HDL TG, 77: HDL-1 A1, 78: HDL-1 A2, 79: HDL-1 CH, 80: HDL-1 FC, 81: HDL-1 PL, 82: HDL-1 TG, 83: HDL-2 A1, 84: HDL-2 A2, 85: HDL-2 CH, 86: HDL-2 FC, 87: HDL-2 PL, 88: HDL-2 TG, 89: HDL-3 A1, 90: HDL-3 A2, 91: HDL-3 CH, 92: HDL-3 FC, 93: HDL-3 PL, 94: HDL-3 TG, 95: HDL-4 A1, 96: HDL-4 A2, 97: HDL-4 CH, 98, HDL-4 FC, 99 HDL-4 PL, 100: HDL-4 TG.
The classification models separated samples from before to after 5 days of HFD with high classification accuracy ($79\%$ and $100\%$ accuracy for fasting and postprandial samples, respectively). In the fasting samples, increased concentrations of several LDL-related variables and decreased concentrations of VLDL- and HDL-related variables were evident after HFD. The postprandial samples showed some deviations from the fasting samples, with reduced concentrations of variables related to LDL-5 and increased concentrations of several HDL-related variables (Fig. 2). Univariate analyses further confirmed significant changes after 5 days of HFD. The HFD induced significant (q < 0.05) changes in 31 of the 100 lipid variables in the fasting samples (Supplementary Table 2) and in 41 variables in the postprandial samples (Supplementary Table 3). Figure 3 shows the percentage change in all lipid subfraction variables from before to after the HFD. Fasting total serum VLDL cholesterol concentrations were reduced by ~ $25\%$ ($q = 0.039$), with significant reductions in cholesterol only in the larger VLDL particles (VLDL-1–3). The HFD also decreased enrichment of triglycerides in VLDL-2 and VLDL-3, as well as in IDL, LDL-6, HDL-3, and HDL-4 in the fasting samples (Supplementary Table 2). VLDL profiles changed differently in the postprandial samples, showing increased free cholesterol concentration in the smaller VLDL particles (VLDL-4 and VLDL-5), as well as elevated triglycerides in VLDL-5 (Supplementary Table 3). In the postprandial samples, there was also increased enrichment of triglycerides in some LDL subfractions (LDL-2 and LDL-3), otherwise the enrichment of triglycerides in subfractions showed a similar pattern as in the fasted samples. Figure 3Change in lipoprotein subfractions from baseline to after 5 days of high-fat diet. In (a) fasted samples, and (b) postprandial samples. Symbols show median percentage change and error bars show interquartile range. TS = total serum, VLDL = very low-density lipoprotein, IDL = intermediate-density lipoprotein, LDL = low-density lipoprotein, HDL = high-density lipoprotein, CH = cholesterol, FC = free cholesterol, PL = phospholipids, TG = triglycerides, Apo-B = apolipoprotein B100, Apo-A1 = apolipoprotein A1, Apo-A2 = apolipoprotein A2.
The HFD increased total fasting serum LDL cholesterol concentration, due to increased cholesterol in larger LDL particles, with significant increases in LDL-2 and LDL-3 (Fig. 4). The HFD induced a change in the distribution of fasting Apo-B concentrations in small versus large LDL subfractions, with increased concentrations in LDL-2 and LDL-3, and a concomitant decrease in LDL-6. These changes in Apo-B distribution within LDL subfractions were seen without any significant change in total LDL Apo-B (Fig. 4).Figure 4Cholesterol and Apolipoprotein-B100 (Apo-B) in LDL measured in the fasted state. Data from participants ($$n = 24$$) before (habitual diet) and after 5 days of high-fat diet. ( a) *Total serum* cholesterol, (b) Cholesterol in LDL subfraction 1–6, (c) *Total serum* Apo-B, (d) Change in Apo-B in LDL subfraction 1–6, as percentage of total Apo-B in LDL, after 5 days of high-fat diet. Bars show means, error bars are SD, and symbols show individual values. * $p \leq 0.05.$
In the postprandial samples, total serum concentrations of cholesterol and Apo-A1 increased after the HFD (Supplementary Table 3). There was no change in total serum LDL cholesterol concentration in the postprandial samples, but significantly increased LDL-1 cholesterol and reduced LDL-5 cholesterol concentrations after 5 days of HFD (Supplementary Figure 2). Several other alterations in subfractions were evident postprandially after the HFD, with increased Apo-B concentrations in IDL and LDL-1, increased concentrations of free cholesterol and phospholipids in LDL-1, as well as triglyceride enrichment in LDL-2 and LDL-3. Although total serum HDL cholesterol concentration did not change significantly in the postprandial samples after 5 days of HFD, cholesterol concentrations in HDL-1–3 were elevated, whereas the opposite was true for the smallest HDL subfraction (HDL-4). Triglycerides enrichment in the smallest HDL particles (HDL-3–4) was reduced after 5 days of HFD (Supplementary Table 3).
When comparing the effect of continued HFD from 5 days (at Visit 2) to 11 days (Visit 3) we observed a decreased PC1 score at the last assessment (Fig. 5). Continued HFD for 11, compared with 5 days, induced an increase in the variables with negative loadings and a decrease in variables with positive loadings. Figure 5Changes after continued high-fat diet with and without exercise training. Scores and loading plots from repeated measures ANOVA simultaneous component analysis for discriminating lipoprotein profiles after exercise/no exercise for 5 days (Visit 3) from after 5 days of high-fat diet (Visit 2), in the fasted state. ( a) Scores for changes between Visit 2 and Visit 3 in the control group (CONTROL), (b) Loadings for changes between Visit 2 and Visit 3 in the control group, (c) Scores for changes after morning exercise (EXam) and evening exercise (EXpm) between Visit 2 and Visit 3, compared with changes in the control group, (d), Loadings for changes after morning exercise (EXam) and evening exercise (EXpm) between Visit 2 and Visit 3, compared with changes in the control group. CH = cholesterol, FC = free cholesterol, PL = phospholipids, TG = triglycerides, AB = Apolipoprotein-B100, A1 = Apolipoprotein A-1, A2 = Apolipoprotein A-2.
Despite no further elevation of total serum LDL cholesterol, cholesterol concentration in the larger LDLs (LDL-1–4) increased, while it decreased in the smaller LDLs (LDL-5–6) after continued HFD. Most of the HDL-related variables increased, except for triglycerides which decreased in all subfractions (Supplementary Table 4). Corresponding development in postprandial samples are shown in Supplementary Figure 3 and Supplementary Table 5.
## Changes in lipoprotein profiles after exercise training
The development in lipoprotein profile between after 5 days of HFD to after 11 days deviated clearly from the changes in CONTROL in the exercise groups, with similar development over time in EXam and EXpm (Fig. 5). In agreement with the repeated measures ANOVA simultaneous component (RM-ASCA +) analysis, univariate analyses showed a significant (q < 0.05) decrease in 20 of the lipid variables after EXam and in 24 after EXpm (15 of these in common) (Supplementary Table 4) in fasting blood. For those variables that changed significantly only in one of the exercise trained groups, the changes in both exercise groups were in the same direction (albeit not statistically significant in the other exercise group). Supplementary Figure 3 shows the development in postprandial lipoprotein profiles.
As also evident from the RM-ASCA + analysis, total serum cholesterol and Apo-A2 concentrations decreased in both exercise trained groups, compared with CONTROL (Supplementary Table 4). Univariate analysis revealed that EXam decreased the fasting concentrations of free cholesterol, phospholipids, and triglycerides in VLDL-1, as well as total serum Apo-A1, and concentration of phospholipids in IDL (all q < 0.05). Even if there were no significant reduction in total LDL cholesterol concentration in either of the exercise groups after adjusting for multiple comparisons, EXpm decreased the total fasting concentration of LDL free cholesterol (Supplementary Table 4). EXpm additionally affected several LDL-related variables, with reductions in fasting cholesterol concentrations in LDL-1 ($q = 0.002$), LDL-3 ($q = 0.044$), and LDL-4 ($q = 0.013$), whereas EXam only reduced LDL-1 cholesterol concentrations ($q = 0.020$) (Supplementary Table 4).
Independent of time-of-day, exercise training induced several changes in fasting HDL subfraction concentrations, without a statistically significant change in total serum HDL cholesterol concentration (Supplementary Table 4). Exercise training affected the smaller HDL particles (HDL-3 and HDL-4), with lower cholesterol concentrations in these particles after both EXam ($q = 0.040$ for both HDL-3 and HDL-4) and EXpm ($q = 0.032$ for HDL-3 and $q = 0.034$ for HDL-4) at study completion, compared with CONTROL. As evident from both the RM-ASCA + analysis and univariate analyses, exercise had less effect on the postprandial samples (Supplementary Figure 3, Supplementary Table 5). There were no statistically significant univariate differences in any fasting or postprandial lipoprotein subfraction variables between the EXam and EXpm (Supplementary Table 6).
## Discussion
We determined the effect of consumption of an HFD and exercise training performed in the morning or in the evening on circulating lipoprotein subfractions in men with overweight/obesity. We report that 5 days of HFD induced substantial changes in lipoprotein subfraction profiles, with alterations in VLDL, IDL, LDL, and HDL subfractions. Based on previous evidence of associations between lipoprotein subfractions and risk of cardiometabolic diseases6,7,16–21, we interpret the effect of the HFD on lipoprotein subfractions as mostly beneficial for cardiometabolic health. Daily exercise training for 5 days also induced distinct and favourable changes in lipoprotein subfraction profiles, with no clear difference between morning and evening exercise.
Consumption of an HFD for 5 days reduced the total fasting concentrations of cholesterol, free cholesterol, and phospholipids in VLDL, as well as cholesterol and triglycerides in several VLDL subfractions. Higher levels of large VLDL particles are associated with insulin resistance16 and incident type 2 diabetes17, independent of established risk factors such as circulating glucose and insulin concentrations. Furthermore, cholesterol in VLDL explained $40\%$ of the excess risk of myocardial infarction associated with obesity in 29,010 individuals from the Copenhagen General Population Study18. In our study, consumption of the HFD primarily impacted the larger VLDL particles (VLDL-1–3), with no significant changes in VLDL-4 and VLDL-5. Fasting concentrations of VLDL-1 phospholipids were decreased after the HFD, with a tendency ($q = 0.060$) of decreased free cholesterol in VLDL-1. Streese and colleagues reported that both VLDL-1 phospholipids and free cholesterol concentrations were inversely associated with retinal arterio-to-venular diameter ratio, an independent measure of cardiovascular outcomes19. There were no effects of the HFD on main VLDL composition in postprandial samples after the HFD, but some of the subfraction components changed (Fig. 3). The timing of blood collection after a meal has implications on VLDL concentrations22, but in our study the timing of these collections was standardised on measurement days. We found that exercise training undertaken in the morning, but not in the evening, further decreased the concentrations of VLDL-1 free cholesterol, phospholipids, and triglycerides. There was also a tendency ($q = 0.055$) of reduced total triglycerides in VLDL after morning exercise training. The beneficial effect of exercise training on large VLDL particles is in agreement with a recent study reporting an inverse correlation between cardiorespiratory fitness (maximum oxygen uptake) and several VLDL subfractions (e.g., VLDL-1 free cholesterol, VLDL-1 phospholipids, and VLDL-1 triglycerides)23. It has been suggested that high intensity exercise is necessary to decrease the hepatic release of VLDL, and lower VLDL concentrations were only associated with a moderate-to-high intensity, and not low-intensity, aerobic physical activity in 509 participants with increased risk of impaired glucose regulation in the Walking Away from Diabetes study24. The exercise training protocol in the current study included three high-intensity interval training sessions, which may explain the beneficial effect of exercise on VLDL subfractions.
The increase in total serum cholesterol concentrations in LDL after 5 days of HFD was due to increased concentrations of cholesterol in the larger LDL subfractions, with no changes in small, dense LDL particles. We found no increase in the fasting concentrations of Apo-B, an indirect measure of LDL particle number, in total serum or in LDL. There was increased concentrations of Apo-B in larger LDL particles (LDL-2 and LDL-3), again revealing a shift towards larger, more buoyant LDL particles after the HFD. These findings are in line with a systematic review and meta-analysis showing an overall trend for an increase in the larger subclasses of LDL and a decrease in the smaller, more dense LDL subclasses, after carbohydrate-restricted dietary interventions11. The circulation time of smaller LDL particles is longer than that of large LDL particles, with small dense LDL particles being more susceptible to atherogenic modifications, including glycation and oxidation25. Indeed, several studies have shown a strong association between small dense LDL particles and incidence of cardiovascular disease6,20,21. For example, larger LDL particles showed no association with future coronary heart disease events in the Atherosclerosis Risk in Communities (ARIC) study, whereas the concentration of small-density LDL cholesterol predicted later incidence of such disease, independent of traditional cardiovascular risk factors in this large cohort study with 11,419 participants21.
We found that exercise training mostly affected large LDL particles, with reductions in fasting Apo-B, cholesterol, free cholesterol, and phospholipids concentrations in LDL-1 after both morning and evening exercise. There were additional reductions in some LDL-3 and LDL-4 subfractions after evening exercise training. These findings, in part, contrast with the results of a meta-analysis of 10 exercise interventions lasting 20–26 weeks which showed that large LDL particles increased and small dense LDL particles decreased after endurance exercise training26. The reason for these discrepant findings may be that the exercise intervention period in our study was short term (5 days).
HDL particles are heterogeneous in their size and composition and standard clinical measurement of HDL cholesterol concentrations is unable to capture this diversity. The findings of recent studies indicate that the inverse associations of cholesterol in HDL particles and incident type 2 diabetes and ASCVD are limited to large and medium subclasses7,27–30. In fact, some studies show that concentrations of smaller HDL particles are associated with higher risk of developing type 2 diabetes28,30. Diets high in fat (41–$62\%$ of total energy intake, TEI) generally increase total HDL cholesterol31, but this effect may be mediated by a concomitant reduction in body mass32. In our study, there was no change in total body mass and no change in total HDL cholesterol after 5 days of HFD. However, the HFD induced significant reductions in several HDL-related variables. There was reduced triglyceride enrichment in HDL-3 and HDL-4, indicating a beneficial effect of HFD since triglyceride concentrations in these small HDL particles are associated with risk of myocardial infarction7. Moreover, triglycerides concentration in HDL, most prominently HDL-3, is associated with reduced microvascular health (retinal arterio-to-venular diameter ratio)19 and low cardiorespiratory fitness23, both of which are important predictors of ASCVD events and mortality33–35.
Exercise training also primarily affected the smallest HDL particles, producing reduced fasting concentrations of Apo-A1, Apo-A2, and free cholesterol in both HDL-3 and HDL-4, and additionally lower HDL-4 phospholipids concentrations, after both morning and evening exercise. These findings are in agreement with a previous study that reported reduced concentrations of small HDL particles after 4 days of daily exercise (20 min of moderate intensity endurance exercise) in sedentary but otherwise healthy men, despite no change in total HDL concentrations36.
Strengths of the current study include its randomised design, rigorous dietary control with all meals provided to the participants, along with prescribed times for eating, and supervised exercise sessions in the laboratory (i.e., little uncontrolled environmental stimuli). The comprehensive analyses of lipoprotein particle profile quantified using NMR spectroscopy, with examination of different lipoprotein attributes not measured in a standard lipid profile, is a major strength of our study. However, there is no standardised method to analyse lipoprotein subfractions, with different methods utilising different techniques for separation of subfractions, making it challenging to directly compare our results with those of others. The timing of blood collection post-exercise can affect circulating concentrations of lipoproteins. For example, decreased total plasma triglyceride concentrations and increased concentrations of cholesterol in HDL were reported 24 h after exercise, and lasted through 48 h, after a single session of moderate-intensity treadmill walking in physically inactive men37. Due to the design of our study, which aimed to compare the effect of morning versus evening exercise, there was a difference in the timing of biological sampling since the last exercise session (12 vs 24 h for fasting samples and 24 vs 36 h for postprandial samples). This is a limitation in our study and such discrepancy is inherent in any investigation of the effects of the time of day of exercise. We only included men in our study and the results cannot be generalised to women. Furthermore, the small sample size per group may limit the interpretation of the findings due to large variability between individuals for some of the variables.
Our results are from a short-term experimental intervention albeit with strict control of the participants’ dietery intake, and accordingly, our findings should not be taken as clinical recommendations. We acknowledge that clinical guidelines for primary prevention of ASCVD from the major cardiology associations (ESC and AHA/ACC), recommend to reduce the intake of saturated fats38,39. However, several meta-analyses and recent studies indicate that there is no clear association between intake of total fat, or saturated fat, and risk of ASCVD8–10,40. Indeed, there is a current spirited debate in the scientific community about the scientific basis for the concept that fat in general, and saturated fat in particular, causes ASCVD41,42.
Short-term consumption of a HFD in men with overweight/obesity induced marked alterations in lipoprotein subfraction profiles, including reductions in several large VLDL subfractions and reduced triglycerides concentrations in small HDL particles. Daily exercise while consuming the HFD, undertaken either in the morning or in the evening, resulted in a distinctive lipoprotein subfraction signature, compared with no exercise. The effect of exercise was particularly evident for the largest LDL particles, with lower concentrations of Apo-B, cholesterol, free cholesterol, and phospholipids in LDL-1, as well as for several subfractions in the smallest HDL particles. Taken collectively, we interpret the overall effects of both the HFD and exercise intervention on lipoprotein subfractions as beneficial for cardiovascular disease prevention.
## Trial design and participants
This was a randomised trial with three parallel groups, undertaken at the St Patrick’s (Fitzroy, VIC) campus of the Australian Catholic University. To be eligible for inclusion, the participants had to fulfil the following criteria: male sex; aged 30–45 years; BMI 27.0–35.0 kg/m2; and sedentary lifestyle (< 150 min/week moderate-intensity exercise for > 3 months and sitting for > 5 h each day). The exclusion criteria were known cardiovascular disease or type 2 diabetes; major chronic illness that impairs mobility or eating/digestion; taking prescription medications (i.e., β-blockers, anti-arrhythmic drugs, statins, or insulin sensitising drugs); previous bariatric surgery; shift-work; smoking; strict dietary intake regimes (e.g., vegan, not regularly consuming three meals per day, actively trying to lose weight); or not being weight stable (± 5 kg) for the last 3 months. The experimental protocol and methodology for the trial are published previously15. In brief, all participants consumed an HFD for 11 days and after the first 5 days of HFD, participants were randomly allocated (1:1:1) to one of three groups: One group of participants did not exercise (CONTROL), while one group trained in the morning (EXam) and one group in the evening (EXpm) on days 6–10. We used a computer random number generator developed and administered by the Unit for Applied Clinical Research at the Norwegian University of Science and Technology to allocate participants into groups. The randomisation had varying block sizes, with the computer technician defining the first, the smallest, and the largest block. The researcher who enrolled the participants (T.M.) was unaware of the size of the blocks.
The trial was approved by the Human Research Ethics Committee of the Australian Catholic University (2016-254H) and was performed in accordance with the Decleration of Helsinki. The trial was registered with the Australian New Zealand Clinical Trials Registry (registration no. ACTRN12617000304336) on $\frac{27}{02}$/17, prior to inclusion of the first participant. Participants provided written informed consent before participation.
## Experimental protocol and interventions
Figure 6 shows an schematic of the experimental protocol. All participants consumed a HFD consisting of $65\%$ TEI from fat, $15\%$ TEI from carbohydrate and $20\%$ TEI from protein for 11 days. The breakdown of the fat component of the diet was $52\%$ saturated fat, $10\%$ polyunsaturated fat and $38\%$ monounsaturated fat (Supplementary Table 7). The HFD consisted of three pre-packed meals (breakfast, lunch, and dinner) each day, which were to be consumed at prescribed times (07:30, 13:00, and 19:00 h). Each of these meals contained $33.3\%$ of TEI, with individualised TEI based on resting metabolic rate measurements15. Supplementary Table 8 shows examples of high-fat meals the participants ate. Participants could drink water, coffee/tea (without sugar/milk) ad libitum. Figure 6Experimental design. All participants consumed a high-fat diet for 11 days. Two groups of participants exercised daily on day 6–10, either in the morning ($$n = 8$$) or in the evening ($$n = 8$$), and one group of participants ($$n = 8$$) remained inactive during the whole study period (control group). Blood samples were obtained in the morning (fasted) and in the evening (postprandial) at baseline (Visit 1), after 5 days of high-fat diet (Visit 2), and at study completion (Visit 3).
After the initial 5 days, two groups of participants exercised daily on days 6–10, either at 06:30 h (EXam) or at 18:30 h (EXpm), whereas one group of participants did not exercise (CONTROL). The exercise protocols were identical for the EXam and EXpm groups and consisted of a combination of high-intensity interval training and moderate-intensity continuous cycling. The high-intensity interval training sessions (completed on day 6, 8 and 10) consisted of a 10-min warm-up followed by ten 1-min work-bouts at 95–$120\%$ of individual peak power output, separated by 1-min low-intensity cycling. The moderate-intensity continuous sessions comprised cycling at 60–$65\%$ of individual peak power output for 40 min (on day 7) and 60 min (on day 9). In the meal following each exercise session, the participants consumed a 419 kJ snack, with the same macronutrient composition as in the HFD, to maintain energy balance. Participants allocated to CONTROL maintained their habitual activities of daily living.
## Blood sampling and clinical chemistry
We sampled venous blood before breakfast and after dinner, at baseline (Visit 1), after 5 days of HFD (Visit 2), and after a further 5 days on the HFD, either with daily exercise or no exercise (Visit 3) (Fig. 6). The participants fasted from 22:00 h the night before the blood sampling on all visits, with blood obtained between 07:15 and 07:45. The evening (postprandial) samples were obtained 34 (SD 7) minutes after eating dinner, between 19:20 and 20:00 h. The time since the last bout of exercise at Visit 3 was (by design) different between the EXam and EXpm group: For fasted samples this time was ⁓24 h for EXam and ⁓12 h for EXpm, and for postprandial samples the time was ⁓36 h for EXam and ⁓24 h for EXpm. Circulating concentrations of total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides were analysed in whole blood using Cobas b 101 (Roche Diagnostics Ltd, Switzerland), and have been reported previously15.
## Nuclear magnetic resonance spectroscopy and lipid subfraction quantification
Serum samples were kept at − 80 °C until shipment on dry ice to the NTNU MR Core Facility in Trondheim, Norway. The samples were thawed at room temperature prior to the NMR analysis. Serum (320 µL) was mixed with equal volume of buffer ($20\%$D2O in 0.075 M Na2HPO4, 6 mM NaN3, 4.6 mM 3-(trimethylsilyl)propionic-2,2,3,3-tetradeutero-acid (TSP-d4), pH 7.4) and analysed in 5 mm tubes at 310 K. For 16 of the samples, the volume of serum was < 300 µL and these samples were analysed in 3 mm tubes, using 120 µL serum and buffer. Samples were analysed using a Bruker Avance III 600 MHz spectrometer (Bruker BioSpin GmbH, Germany), equipped with a BBI probe. Data acquisition and sample handling were automated (SampleJet with Icon-NMR on TopSpin 3.6). Lipoprotein subclassification was performed using Bruker BioSpin (Bruker IVDr Lipoprotein Subclass Analysis B.I.LISA™), based on one-dimensional NOESY NMR spectra43. This model provides information on concentrations of cholesterol, free cholesterol, phospholipids, and apolipoprotein A1 (Apo-A1), apolipoprotein A2 (Apo-A2) and apolipoprotein B-100 (Apo-B) in serum, as well as in each of the lipoprotein classes (very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), LDL, and HDL). Each lipoprotein class was additionally subdivided into subfractions according to their density. VLDL was divided into VLDL 1–5, LDL into LDL 1–6, and HDL into HDL 1–4, with increasing density. In addition, their concentrations of cholesterol, free cholesterol, phospholipids, Apo-A1, Apo-A2, Apo-B, and triglycerides were estimated.
## Statistical analysis
We did not complete a formal sample size calculation for this study due to the exploratory nature of the research question. Fasted (morning) and postprandial (evening) blood samples were analysed separately. We calculated Pearson’s correlation coefficients between total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides measured by standard clinical chemistry and NMR spectroscopy in the fasted and postprandial samples. Data are expressed as means with standard deviation SD and estimates with $95\%$ confidence intervals.
As a first step in the exploratory analysis, we performed a PCA comparing samples from baseline to after 5 days on the HFD. We then employed multilevel PLS-DA for supervised analysis44. In multilevel PLS-DA, we utilised the multilevel structure of the data to remove the between-subject variation, thereby focussing on the within-subject variation. Multilevel PLS-DA was validated by leave-one-patient-out cross-validation, and the resulting model was orthogonalized for increased interpretation. The loading plots of the orthogonalized PLS-DA were coloured according to the lipoprotein variable importance score (VIP score), which indicates how important each variable was for creating the discrimination model. The PCA and PLS-DA analyses were undertaken in Matlab R2018b using the PLS_Toolbox 8.7.2 (Eigenvector Research, Wenatchee, WA, USA), and variables were autoscaled before analysis.
We used RM-ASCA+45 to determine multivariate changes in lipoprotein profiles between EXam, EXpm, and CONTROL. In RM-ASCA+ the effect matrices resulting from univariate linear mixed models are analysed by PCA to assess overall effects. Linear mixed models were performed using time (visit 2 and visit 3) and the time*group interaction as fixed effects, while participant was used as random effect. Fixed effect variables were reference coded with visit 2 and CONTROL as reference for time and group, respectively. We analysed the time effect and time*group interactions separately in the RM-ASCA+ analysis, and the results are visualized as scores and loadings. Due to the reference coding, the time effect will represent time changes in CONTROL. The time*group interactions plots show how EXam and EXpm deviate from CONTROL. Non-parametric bootstrapping was used to construct $95\%$ confidence intervals.
We also used univariate analyses to investigate each of the 100 lipoprotein subfraction variables. To determine the effect of 5 days of HFD on lipoprotein subclasses, we used paired samples t-tests, with adjustments for multiple comparisons using the Benjamini–Hochberg procedure46. For these comparisons, we consider adjusted p-values (q-values) < 0.05 to be statistically significant. To determine the between-group difference in lipoprotein subclasses after exercise in the morning, exercise in the evening, or no exercise, we used linear mixed models. These models were executed for each of the 100 lipid variables from the NMR spectroscopy as dependent variables, with adjustments for baseline values (the values at Visit 2, prior to the exercise intervention), with participant as random effect, and time and time*group interactions as fixed effects. We built separate models for the comparison of EXam and EXpm versus the control group, and for EXpm versus EXam. P-values were adjusted as described above.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4. The online version contains supplementary material available at 10.1038/s41598-023-31082-0.
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|
---
title: 'Altered sialin mRNA gene expression in type 2 diabetic male Wistar rats: implications
for nitric oxide deficiency'
authors:
- Nasibeh Yousefzadeh
- Sajad Jeddi
- Maryam Zarkesh
- Khosrow Kashfi
- Asghar Ghasemi
journal: Scientific Reports
year: 2023
pmcid: PMC10006425
doi: 10.1038/s41598-023-31240-4
license: CC BY 4.0
---
# Altered sialin mRNA gene expression in type 2 diabetic male Wistar rats: implications for nitric oxide deficiency
## Abstract
Nitrate therapy has been suggested to boost nitric oxide (NO) levels in type 2 diabetes (T2D); however, little is known about nitrate transport across the membranes. This study aimed to assess changes in the mRNA expression of sialin, as a nitrate transporter, in the main tissues of rats with T2D. Rats were divided into two groups ($$n = 6$$/group): Control and T2D. A high-fat diet combined with a low dose of streptozotocin (STZ, 30 mg/kg) was used to induce T2D. At month 6, samples from the main tissues of rats were used to measure the mRNA expression of sialin and levels of NO metabolites. Rats with T2D had lower nitrate levels in the soleus muscle ($66\%$), lung ($48\%$), kidney ($43\%$), aorta ($30\%$), adrenal gland ($58\%$), epididymal adipose tissue (eAT) ($61\%$), and heart ($37\%$) and had lower nitrite levels in the pancreas ($47\%$), kidney ($42\%$), aorta ($33\%$), liver ($28\%$), eAT ($34\%$), and heart ($32\%$). The order of sialin gene expression in control rats was: soleus muscle > kidney > pancreas > lung > liver > adrenal gland > brain > eAT > intestine > stomach > aorta > heart. Compared to controls, rats with T2D had higher sialin mRNA expressions in the stomach (2.1), eAT (2.0), adrenal gland (1.7), liver (8.9), and soleus muscle (3.4), and lower sialin expression in the intestine (0.56), pancreas (0.42), and kidney (0.44), all P values < 0.05. These findings indicate altered sialin mRNA expression in the main tissues of male T2D rats and may have implications for future NO-based treatment of T2D.
## Introduction
The prevalence of type 2 diabetes (T2D) in adults increased from 151 to 537 million during the last two decades, and it is estimated to reach 783 million by 20451. Decreased nitric oxide (NO) bioavailability, the amount of NO that becomes available to its targets, is involved in the pathophysiology of T2D2. NO is produced via NO synthase (NOS)-dependent and NOS-independent (nitrate-nitrite-NO) pathways3 that account for about $90\%$ and $10\%$ of the whole-body NO production, respectively4. In the NOS-dependent pathway, NO is synthesized from L-arginine. In the second pathway, NO is produced from the reduction of nitrate and nitrite5, where nitrate has endogenous (oxidation of NOS-derived NO) and exogenous (mainly through diet) sources4,6. The nitrate-nitrite-NO pathway is complementary to the NOS-dependent pathway7,8. In support, dietary nitrate/nitrite deprivation leads to decreased skeletal muscle nitrate and nitrite in rats7 and developing metabolic syndrome and endothelial dysfunction in mice9. In T2D, both NO production pathways are disrupted, and decreased endothelial NOS (eNOS) and increased inducible NOS (iNOS) expression and activity10,11 and impaired nitrate-nitrite-NO pathway12, have been reported.
Intervention by nitrate and nitrite, which have NO-like bioactivity5, is now one strategy for NO boosting in NOS-disrupted conditions, including T2D5,13. Indeed, the beneficial metabolic effects of nitrate/nitrite in rodent models of T2D have been shown14,15, and the underlying mechanisms include increased insulin secretion from pancreatic β-cells16,17 as well as improved peripheral glucose utilization15,18–21. Stimulatory effect of nitrate/nitrite on insulin secretion in rats with T2D are mediated by increasing pancreatic islets' blood flow16, increasing pancreatic islets' insulin synthesis and exocytosis17, and blunting diabetes-induced oxidative stress in pancreatic islets22. Despite extensive research on the beneficial effects of nitrate and nitrite in T2D, little is known about nitrate transport across the membranes. Nitrate cannot freely permeate through the phospholipid bilayer and needs transporters to move across the plasma membrane23.
Slc17a5 (solute carrier family 17, member 5) gene that encodes sialin protein, was first identified in 199924. Slc17a5 is located on chromosome 6q in humans and 9q in rodents25 and is a highly conserved gene in humans and rodents26. The Slc17 family includes type I phosphate, sialin, vesicular glutamate, and vesicular nucleotide transporters27,28. Sialin protein has 495 amino acids29 and, in both humans and rodents, is an integral membrane protein that has 12 transmembranes domains25,30. In 2012, it was reported that sialin could act as a 2NO3−/H+ cotransporter in the salivary glands, which causes nitrate influx to the cell31. Nitrate transport into cells through sialin is essential for the nitrate-nitrite-NO pathway32. Sialin is widely expressed in the eye, brain, liver, pancreas, kidney, muscles, and salivary glands31,33–35. This wide expression in almost all tissues in both humans and rodents28 reflects a housekeeping function for this carrier27. Inhibition of sialin or its knockout decreases nitrate uptake in human skeletal muscle cells36 and human submandibular gland cell line (HSG), whereas its overexpression increases nitrate uptake31. In addition, sialin expression increases in hypoxic cancer cells37 and hypercholesterolemic rats' heart and liver tissues38. To our knowledge, changes in sialin gene expression, if any, have not been reported in T2D. Therefore, this study aimed to assess the changes in sialin mRNA expression in the main tissues of type 2 diabetic rats.
## Ethical approval
All experiments of the current study were affirmed by the published guideline of the care and use of laboratory animals in Iran39 and reported following ARRIVE guidelines40. The ethics committee of the Research Institute for Endocrine Sciences affiliated with the Shahid Beheshti University of Medical Sciences confirmed and approved all experimental procedures of the current study (Ethic Code: IR-SBMU.ENDOCRINE.REC.1398.034; Approved Date: 2019-08-06).
## Induction of type 2 diabetes in rat
Male Wistar rats ($$n = 12$$), 2 months old, weighing 190–200 g, were housed in polypropylene cages under standard conditions with free access to regular rat diet (Khorak Dam Pars, Co., Tehran, Iran) and drinking water. Rats were randomly allocated to 2 groups ($$n = 6$$/group): Control and T2D. A high-fat diet (HFD) combined with streptozotocin at a low dose (STZ, 30 mg/kg, intraperitoneal (IP) injection) was used to induce T2D; one week later, anesthetized rats with overnight (12 h) fasting serum glucose concentration ≥ 150 mg/dL were included in the study as diabetic rats41. For the preparation of HFD, 586 g of powdered regular diet, 310 g of sheep butter as a source of fat, 73 g of casein (Iran Caseinate Company, Karaj, Iran) as a source of protein, 1.8 g, 4.1 g, and 25 g of DL-methionine, vitamin mix, and mineral mix (Behroshd Company, Saveh, Iran) were thoroughly mixed to produce 1000 g HFD. In the prepared HFD, the total caloric value was ~ 4900 kcal/1000 g, and calories received from fat, carbohydrate, and protein were $58.8\%$, $27.0\%$, and $14.2\%$, respectively. Details on the induction of T2D in rats using combination of HFD and low dose of STZ have been reviewed in our previous report41.
## Experimental design
The protocol for this experimental interventional study is shown in Fig. 1. At month 0 (start of the experiment) and month 6 (end of the experiment), body weight (using Tefal Scale; sensitivity 1 g) and serum glucose were measured in all rats. At month 6, samples from main tissues, including the left ventricle of the heart, aorta, stomach, intestine (i.e., duodenum), epididymal adipose tissue (eAT), brain, adrenal gland, liver, lung, pancreas, kidney, and soleus skeletal muscle were used to measure the mRNA expression of sialin (Slc17a5) using real-time PCR. In addition, NO metabolites (nitrate + nitrite = NOx) in all studied tissues were measured at month 6 by the Griess method, as previously reported42.Figure 1Experimental design of the study. HFD high fat diet, STZ streptozotocin, NO nitric oxide.
## Measurement of serum glucose concentration
Serum glucose concentration was measured at months 0 and 6 by the glucose oxidase method (Pars Azmoon Co., Iran). After overnight fasting (12 h), blood samples were obtained from the tail tips of anesthetized rats (IP injection of ketamine at dose 50 and xylazine at dose 10 mg/kg) and centrifuged (10 min at 5000g) and then the sera were used to measure serum glucose concentrations; the intra-assay coefficient of variation (CV) was $2.6\%$.
## Measurement of tissues’ nitric oxide metabolites
The NOx and nitrite concentration in all tissues were measured by the Griess method42, with slight modification on sample deproteinization. According to our previous report43, to deproteinize samples, we used zinc sulfate (15 mg/mL) instead of centrifugation by a 30-kDa molecular weight filter as reported by Miranda et al.42; in addition, NaOH (3.72 M44) was used for preventing turbidity in the Griess reaction. After adding zinc sulfate and NaOH to 300 μL of the homogenized tissues, samples were centrifuged (10 min at 10,000g), and supernatants were removed o measure of NOx and nitrite levels. In brief, all tissues were homogenized (100 mg of the heart, aorta, stomach, intestine, adrenal gland, liver, lung, pancreas, kidney, and soleus skeletal muscle in 500 μL of phosphate-buffered saline (PBS, pH 7.4) and 100 mg of the brain and eAT in 200 μL of PBS) and centrifuged for 10 min at 10,000 g. For measuring NOx, nitrate was reduced to nitrite by adding vanadium trichloride (VCL3, 8 mg/mL in 1 M HCl, the solutions passed through the membrane filter), followed by N-(1-naphthyl) ethylenediamine ($0.1\%$ in ddH2O) and sulfanilamide ($2\%$ in $5\%$ HCl). Samples were kept for 30 min at 37 °C, and optical density (OD) was read at 540 nm. NOx concentrations were measured using a standard calibration curve of 0–100 µM sodium nitrate.
Nitrite was measured using the same method, except that samples were only exposed to sulfanilamide, NEDD, and 1 M HCl instead of VCL3; nitrite standards (0–20 µM) were used. Tissue nitrate concentrations were calculated by subtracting nitrite values from NOx concentrations. Protein concentration was measured using the Bradford method; nitrite and nitrate concentration in tissues are presented as nmol/mg protein. The intra-assay CVs for NOx in the heart, aorta, stomach, intestine, adrenal gland, liver, lung, pancreas, kidney, brain, eAT, and soleus skeletal muscle were 2.2, 1.7, 2.7, 2.0, 1.4, 2.2, 2.1, 2.5, 2.9, 2.9, 1.8, and $3.5\%$, respectively. The intra-assay CVs for nitrite in the heart, aorta, stomach, intestine, adrenal gland, liver, lung, pancreas, kidney, brain, eAT, and soleus skeletal muscle were 3.0, 3.6, 2.0, 2.8, 2.8, 4.0, 2.2, 1.5, 2.9, 2.3, 2.5, and $3.2\%$, respectively.
## Assessment of sialin mRNA expression
Total RNA from all tissues were extracted using the TRIzol reagent (Invitrogen, USA). The purity and quantity of the extracted total RNA was determined using A NanoDrop-1000 spectrophotometer (Thermo Scientific, USA). The $\frac{260}{280}$ and $\frac{260}{230}$ absorbance ratios were used as indices of RNA purity; a pure RNA sample (i.e., free of protein and phenolic compounds and other solvent contamination) is characterized by $\frac{260}{280}$ and $\frac{260}{230}$ absorbance ratios ranging between 1.8 and 2.245. For cDNA synthesis, a cDNA synthesis kit (SMOBiO Technology, Taiwan), in accordance with manufacturer instructions, and a peqSTAR Universal PCR machine (Peqlab, Germany) was used. The reaction contained 1 μg of extracted RNA, 1 μL of ExcelRT Reverse transcriptase (RT) (200 U/μL), 4 μL of RT buffer, 1 μL of random hexamer (100 µM), 1 μL of dNTPS Mix (10 mM), 1 μL of RNAok RNase inhibitor (20 U/μL) and 4 μL of DEPC-treated H2O. The thermal cycling settings included 5 min at 70 °C for RNA denaturation followed by incubation at 25 °C for 20 min and 50 °C for 50 min, respectively.
Finally, amplification of synthesized cDNA was done using SYBR Green PCR Master Mix 2X (Ampliqon Company, Denmark) in a Rotor-Gene 6000 real-time PCR machine (Corbett, Life Science, Sydney, Australia). The reaction contained 2 µL cDNA, 2 μL of primers (forward and reverse), 12.5 μL Master Mix, and 8.5 μL DEPC-treated H2O, yielding a total volume of 25 µL. The thermal cycling settings included a 10 min initial denaturation (95 °C) followed by 40 cycles with 45, 45, and 60 s at 94, 58, and 72 °C, respectively. All tissues were run in duplicate; nuclease-free water was used instead of templates in the negative control reactions. Sequences of primer for sialin and GAPDH (housekeeping) genes are presented in Table 1. We used GAPDH as a housekeeping gene because it is useful when the study aims to compare gene expression between tissues because its expression is abundant and less variable among tissues46. GAPDH has relatively stable expression in the liver47, kidney48, pancreas49, heart50, brain51, adipose tissue47, and intestine52 in rodents. In addition, its expression remains relatively stable in the tissues of rodents with T2D53.Table 1Primers sequence used for Real time-PCR.GenesAccession noSequence (5' → 3')PCR product length (bp)SialinNM_001009713.2F: GTCAGCCAAGCAACGATAGR: AAGCATAGAGAACGAAGAAACC209GAPDHNM_017008.4F: AGTGCCAGCCTCGTCTCATAR: GATGGTGATGGGTTTCCCGT248F forward, R reverse.
## Statistical analyses
Data were analyzed using GraphPad Prism version 8.0.0 for Windows, GraphPad Software, San Diego, California USA, www.graphpad.com. All values are presented as mean ± SEM, except for mRNA expressions of sialin, which are represented as relative fold changes. A two-way mixed (between-within) analysis of variance followed by the Bonferroni post-hoc test was used to compare body weight and glucose at the start and end of the study among control and T2D rats. The Student's t-test was used to compare the NOx, nitrite, and nitrate levels between groups. To determine the precision of the assays, CV was calculated using the formula: CV = (standard deviation/mean) × 100)]54. Relative expressions of genes were calculated based on cycle thresholds of sialin versus GAPDH as a reference gene using the REST software55. This software uses a randomization test to compare the difference between control and diabetic samples, which avoids any assumptions about data distribution and is therefore preferred over parametric tests55. Quantities of interest in PCR data are derived from ratios and variances can be high, so standard parametric tests, which depend on normal distribution, are inappropriate for their statistical analysis. A randomization test repeatedly and randomly reallocates the observed values to control and treated groups and notes the expression ratio. P values are calculated from the proportion of random allocations of mean observed data to the control and treated groups. Since examining of all possible allocations is impractical, a random sample is drawn; taking 2000 samples or more provides a reliable estimate of P value at 0.05 level55. In addition, REST software provides efficiency-corrected relative gene expression, which is highly recommended55, prevents any miscalculating expression ratio differences56, and allows the comparison of two groups for one reference and one or more target genes55. Two-sided P values < 0.05 and between 0.05 and 0.10 were considered statistically and marginally significant, respectively.
## Serum glucose and body weight
As shown in Table 2, compared to month 0, both control and diabetic rats had higher body weight by $84\%$ and $113\%$ ($P \leq 0.001$) at month 6. In addition, compared to month 0, serum glucose levels at month 6 were higher by $57\%$ ($P \leq 0.001$) in the diabetic rats. Table 2Body weight and fasting serum glucose concentration at the start and end of the study in control and type 2 diabetic rats. ControlDiabetesMonth 0Month 6Month 0Month 6Body weight (g)192.8 ± 3.6354.4 ± 7.3*186.2 ± 6.2396.3 ± 7.8*Fasting glucose (mg/dL)117.2 ± 6.9127.5 ± 3.8110.2 ± 4.2172.7 ± 7.4**Significant difference compared to month 0. Values are mean ± SEM. ( $$n = 6$$ rats/group).
## Tissue nitrate and nitrite levels
As presented in Fig. 2, compared to controls, rats with T2D had lower values of nitrate in the soleus muscle ($66\%$, $$P \leq 0.004$$), lung ($48\%$, $$P \leq 0.008$$), kidney ($43\%$, $$P \leq 0.003$$), aorta ($30\%$, $$P \leq 0.070$$), adrenal gland ($58\%$, $$P \leq 0.006$$), eAT ($61\%$, $$P \leq 0.009$$), and heart ($37\%$, $$P \leq 0.043$$); however, nitrate levels were higher in the intestine of diabetic rats ($92\%$, $$P \leq 0.029$$) and no changes were observed in the stomach, pancreas, brain, and liver. Figure 2Changes in tissues’ nitrate and nitrite levels in control and type 2 diabetic rats.
Compared to controls, rats with T2D had lower values of nitrite levels in the pancreas ($47\%$, $$P \leq 0.009$$), kidney ($42\%$, $$P \leq 0.022$$), aorta ($33\%$, $$P \leq 0.002$$), liver ($28\%$, $$P \leq 0.071$$), eAT ($34\%$, $$P \leq 0.091$$), and the heart ($32\%$, $$P \leq 0.064$$). Nitrite values in the soleus muscle, stomach, intestine, lung, adrenal gland, and brain were similar among control and diabetic rats.
## Sialin mRNA expression in tissues
As shown in Fig. 3, compared to the control group, rats with T2D had higher mRNA expressions of sialin in the stomach, eAT, adrenal gland, liver, and soleus muscle by 2.1, 2.0, 1.7, 8.9, and 3.4 folds, respectively. mRNA expressions of sialin were significantly lower in the intestine, pancreas, and kidney of the diabetic rats compared to controls by 0.56, 0.42, and 0.44 folds, respectively. No change was observed in the mRNA expression of sialin in the heart, aorta, brain, and lung of diabetic rats (Supplementary Figure 1).Figure 3Changes in mRNA expression of sialin in control and type 2 diabetic rats. * Significant difference compared to the liver tissue of control rats, which its expression was considered as reference.
If mRNA expressions of sialin in the liver of control rats is considered as reference (sialin mRNA expression = 1), in control rats, mRNA expressions of sialin in the heart (0.006-fold), aorta (0.089-fold), stomach (0.11-fold), intestine (0.17-fold), eAT (0.2-fold), brain (0.27-fold), and adrenal gland (0.61-fold) were lower than the liver; all P values are < 0.001. Compared to the liver, sialin mRNA expression was higher in the pancreas (2.17-fold, $P \leq 0.001$), kidney (4.15-fold, $P \leq 0.001$), and soleus muscle (7.17-fold, $P \leq 0.001$). mRNA expressions of sialin in the lung of rats were comparable with the liver.
Compared to mRNA expressions of sialin in the liver of controls, rats with T2D had higher sialin mRNA expression in the soleus muscle (32.1-fold, $P \leq 0.001$) and kidney (1.97-fold, $$P \leq 0.005$$) and lower expression in the heart (0.008-fold, $P \leq 0.001$), aorta (0.063-fold, $P \leq 0.001$), stomach (0.26-fold, $P \leq 0.001$), intestine (0.096-fold, $P \leq 0.001$), eAT (0.51-fold, $$P \leq 0.010$$), and brain (0.29-fold, $P \leq 0.001$). No change was observed in the sialin mRNA expressions in the adrenal gland, lung, and pancreas of diabetic rats, as compared with the expressions in the liver of controls.
## Discussion
To our knowledge, this is the first report documenting changes in mRNA expression of sialin, as a nitrate transporter, in the main tissues of rats with T2D. mRNA expression of sialin in the control rats differed among tissues, with the lowest expression registered in the heart and the highest in the soleus muscle. T2D increased mRNA expression of sialin in most tissues (soleus muscle, stomach, adrenal gland, liver, and eAT), decreased it in some tissues (intestine, pancreas, and kidney), and did not affect other studied tissues (lung, aorta, brain, and heart).
Our results indicate that except for the brain, stomach, and intestine, in other studied tissues, lower values of nitrate (soleus muscle, lung, kidney, aorta, adrenal gland, eAT, and heart) or nitrite (pancreas, kidney, aorta, liver, eAT, and heart) are observed in diabetic rats. These results represent a generalized decreased NO bioavailability in T2D and are in line with other reports indicating lower NOx concentrations in the liver ($43\%$)57, soleus muscle ($64\%$)57, eAT ($42\%$)57, inguinal adipose tissue ($30\%$)15, kidney ($42\%$)58, heart ($60\%$)58, and aorta ($40\%$)59 of rats with T2D as well as lower nitrate ($33\%$) and nitrite ($51\%$) concentrations in the gastrocnemius muscle18 and heart ($43\%$)60 of mice with T2D. In addition, chronic metabolic conditions, including hypertension and T2D, are characterized by reduced NO production61. Decreased NO metabolites in tissues of diabetic rats are due to impaired NOS-dependent NO production, including decreased availability of L-arginine, decreased eNOS expression, increased arginase activity, uncoupling of NOS, and increased levels of asymmetric dimethyl L-arginine (ADMA, an endogenous NOS inhibitor)57,62. In T2D, eNOS is uncoupled and produces superoxide anion instead of NO62. Superoxide anion rapidly reacts with iNOS-derived NO to form peroxynitrite, a potent oxidant, which enhances eNOS uncoupling62,63 by increasing intracellular ADMA levels (an L-arginine analog) and oxidation of tetrahydrobiopterin (an eNOS cofactor)62,64,65. In addition, oxidation of eNOS by peroxynitrite66, may be an essential mechanism in the development of eNOS uncoupling and decreased NO metabolites in diabetic conditions. The impaired nitrate-nitrite-NO pathway is also involved in NO deficiency in T2D and is due to decreased reduction of nitrate to nitrite and then to NO because of oral microbiota dysbiosis, which decreases oral nitrate-reducing bacteria, abnormal metabolism of ascorbic acid, which decreases gastric conversation of nitrite to NO, and decreased nitrate-nitrite reductase enzymes67–69.
In our study, the order of sialin expression in tissues of control rats (soleus muscle > kidney > pancreas > lung > liver > adrenal gland > brain > eAT > intestine > stomach > aorta > heart) was different from those reported for humans (brain > kidney > liver > pancreas) and pigs (liver > brain > kidney > muscle > pancreas)31, indicating species difference in the tissue pattern of sialin expression. These findings are supported by data showing species differences in NO metabolism; it has been reported that NO production in the rat (0.55 ± 0.05 μmol/kg/h) is similar to humans (0.38 ± 0.06 μmol/kg/h) but it is about 20 times higher in mice (7.68 ± 1.47 μmol/kg/h)4,61. In addition, the accumulation of nitrate in the saliva (with a concentration 10–20 fold higher than blood) in humans is more effective than in rodents; however, nitrate reduction in basal conditions in rodents is more effective than humans4,61.
Compared to the controls, in rats with T2D, sialin mRNA expression was higher in the adrenal gland, eAT, stomach, soleus muscle, and liver. Conversely, it was lower in the intestine, pancreas, and kidney. In most studied tissues, increased sialin mRNA expression in diabetic rats was accompanied by decreased nitrate and nitrite concentrations, suggesting that increased sialin mRNA expression acts as a compensatory mechanism to counteract reduced NO bioavailability. In support of this notion, it has been reported that sialin expression in the skeletal muscle of control mice increased by $89\%$ upon deletion of myoglobin, which reduces nitrite to NO70, and this compensatory pathway partially sustains NO bioavailability in myoglobin-deficient mice70. Moreover, it has been reported that a decrease in dietary nitrate for 21 days that causes decreased nitrate levels in the soleus muscle is associated with increased sialin expression by $50\%$ in rats71.
In the current study, sialin mRNA expression was lowest in the heart and highest in the soleus muscle. It has been reported that NO production in the heart of normal Wistar rats mainly relies on the NOS-dependent rather than the nitrate-nitrite-NO pathway; indeed, NOS-dependent and -independent pathways contribute about $80\%$ and $20\%$ to the total heart NO formation, respectively. However, in the ischemic heart, NO formation from the NOS-independent pathway increases and can exceed NOS-dependent NO generation72. Our finding that sialin mRNA expression was highest in the skeletal muscle is in line with the hypothesis proposed by Piknova et al. that skeletal muscle tissue is the main nitrate reservoir organ and the liver and other organs are the final site of reduction of nitrate to nitrite and then NO73. According to this hypothesis, due to its large size and low nitrate reductase activity, skeletal muscle is an optimal place to store nitrate and may have a protective role against possible future periods of dietary nitrate deprivation, at least in rodents73.
In our study, decreased sialin mRNA expression was accompanied by reduced nitrate/nitrite levels in some tissues (i.e., kidney and pancreas). Therefore, whereas nitrate deficiency in T2D was associated with increased sialin mRNA expression in some tissues (i.e., adrenal gland, eAT, stomach, soleus muscle, and liver) due to a compensatory response, it was associated with decreased sialin mRNA expression in some other tissues. Although not completely understood, high apical expression of sialin in the kidney distal tubule cells of female mice74, male pigs, and male humans31, suggests that sialin may contribute to renal reabsorption of nitrate75. Therefore, it is expected that decreased sialin mRNA expression in the kidney would be accompanied by reduced nitrate and nitrite levels. However, our data cannot explain the causality between nitrate/nitrite deficiency and sialin expression, and this issue needs further investigation.
As a strength, we used an HFD/low-dose STZ model of T2D, which mimics the pathophysiology of T2D in humans by inducing long-lasting and stable hyperglycemia, insulin resistance, relative hyperinsulinemia, hypertriglyceridemia and also it is sensitive to glucose-lowering effects of metformin and troglitazone41,76. In this model, consumption of HFD induces insulin resistance, and subsequently, administration of STZ at a low dose causes partial destruction of pancreatic β-cells41 by apoptosis77. Increased apoptosis is responsible for impaired insulin secretion in pancreatic β-cell in both humans and rats77–79. We used low dose of STZ along with HFD, as it has been reported that the animals fed only with HFD for 276, 480, 1681, and 3682 weeks progressed insulin resistance but not hyperglycemia.
As a limitation, we used ketamine/xylazine to anesthetize rats that can increase blood glucose in normal (~ $40\%$)83 and diabetic (~ $60\%$)84 rats. However, the hyperglycemic effect of ketamine/xylazine is acute (observed only 6–22 min after IP injection83) and mainly observed in fed, not fasted rats84–86. We measured our serum glucose values after overnight fasting and therefore it is unlikely that our data was affected by use of ketamine/xylazine.
In conclusion, our results indicated altered sialin mRNA expression, as a nitrate transporter, in the main tissues of male T2D rats. It seems that increased sialin mRNA expression in some tissues (i.e., soleus muscle, adrenal gland, liver, and eAT) may act as a compensatory mechanism to counteract reduced NO bioavailability; however, this hypothesis does not explain decreased mRNA expression of sialin in the pancreas and kidney. Since sialin can play an important role in the physiological regulation of systemic nitrate–nitrite–NO balance31, these data may have implications for future NO-based treatment of T2D, which has been suggested to be a cost-effective approach87. Further studies are needed to elucidate the role of sialin in the pathophysiology of T2D.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31240-4.
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|
---
title: Novel long non-coding RNAs associated with inflammation and macrophage activation
in human
authors:
- Avisankar Chini
- Prarthana Guha
- Venkat S. Malladi
- Zibiao Guo
- Subhrangsu S. Mandal
journal: Scientific Reports
year: 2023
pmcid: PMC10006430
doi: 10.1038/s41598-023-30568-1
license: CC BY 4.0
---
# Novel long non-coding RNAs associated with inflammation and macrophage activation in human
## Abstract
Inflammation plays a central role in immune response and macrophage activation. Emerging studies demonstrate that along with proteins and genomic factors, noncoding RNA are potentially involved in regulation of immune response and inflammation. Our recent study demonstrated that lncRNA HOTAIR plays key roles in cytokine expression and inflammation in macrophages. The primary goal of this study is to discover novel lncRNAs that are crucial players in inflammation, macrophage activation, and immune response in humans. Towards this, we have stimulated THP1-derived macrophages (THP1-MΦ) with lipopolysaccharides (LPS) and performed the whole transcriptome RNA-seq analysis. Based on this analysis, we discovered that along with well-known marker for inflammation (such as cytokines), a series of long noncoding RNAs (lncRNAs) expression were highly induced upon LPS-stimulation of macrophages, suggesting their potential roles in inflammation and macrophage activation. We termed these family of lncRNAs as Long-noncoding Inflammation Associated RNA (LinfRNA). Dose and time dependent analysis demonstrated that many human LinfRNA (hLinfRNAs) expressions follow similar patterns as cytokine expressions. Inhibition of NF-κB suppressed the expression of most hLinfRNAs suggesting their potential regulation via NF-κB activation during inflammation and macrophage activation. Antisense-mediated knockdown of hLinfRNA1 suppressed the LPS-induced expression of cytokines and pro-inflammatory genes such as IL6, IL1β, and TNFα expression, suggesting potential functionality of the hLinfRNAs in cytokine regulation and inflammation. Overall, we discovered a series of novel hLinfRNAs that are potential regulators of inflammation and macrophage activation and may be linked to inflammatory and metabolic diseases.
## Introduction
Inflammation is a vital biological process associated with the immune response1–3. The immune system recognizes and removes injurious stimuli (e.g. bacterial and viral infections) and helps healing. Inflammation causes activation of immune cells such as monocytes, macrophages, T cells, and others and this induces production of inflammatory mediators such as cytokines and chemokines, to fight against infection. However, uncontrolled and chronic inflammation and dysregulation in macrophage activation contribute to many human severe diseases including sepsis, autoimmune disorders, atherosclerosis, metabolic diseases, neurological disorders and cancer4–9. Despite the extensive research and availability of a wide variety of drugs, many chronic and inflammatory diseases cannot be treated effectively. Therefore, finding novel therapeutic targets and discovering critical regulators of inflammation for developing effective therapeutic strategies becomes urgent.
Macrophage activation plays a central role in inflammation and immune response10,11. Macrophages are originally derived from bone marrow derived monocytes and then infiltrate through blood vessels to reach different tissues for protecting against different kinds of tissue damage and infections. Once reached to different tissue, macrophages differentiate into tissue specific resident macrophages such as Kupffer cells, microglia etc. Resting macrophages (M0 type macrophages) produce very low levels of cytokines and inflammatory mediators10–12. However, upon inflammation, tissue resident or recruited macrophages (from circulatory macrophages) gets activated by different types of inflammatory mediators and differentiate into M1 (pro-inflammatory or killer macrophage) and M2 (ant-inflammatory or healing macrophages) type of macrophages10–13. M1 macrophages secrete various cytokines, chemokine and pro-inflammatory signals and induces inflammatory response and that helps removing pathogenic infection and phagocytosis. M2-macrophages secrete high levels of anti-inflammatory cytokine (such as IL4, IL10, IL13, etc.) and helps wound healing and tissue regeneration. Under chronic inflammation macrophages cause tissue damage and metabolic reprogramming contributing toward chronic metabolic diseases such as obesity, diabetes, cardiovascular diseases, neurological disorder, and multiorgan failure.
Inflammation and immune signaling follow complex pathways involving various genomic and protein-based factors (e.g. cytokines, interferons, etc.)14–18. However, emerging evidence suggests that along with proteins, noncoding RNAs (ncRNAs) are key players in the immune response and inflammation19–25. For example, miR-155 is induced upon inflammation via targeting IκB in NF-κB activation26,27. Similar to microRNAs, Lethe, a long noncoding RNA (lncRNA), is induced upon TNFα stimulation and it inhibits NF-κB by interacting with RelA (P65) subunit of active NF-κB28. LincRNA-Cox2 inhibits interferon-stimulated genes and chemokines in resting macrophages (M0)29. LncRNA P50-associated COX-2 extragenic RNA (PACER) has been reported as a regulator of NF-κB signaling and PTGS2 (COX-2) expression30. LncRNA THRIL has been reported a negative feedback regulator of TNFα. The lncRNA NEAT1 (Nuclear enriched abundant transcript 1), is involved in controlling the heterochromatin structure formation and virus medicated pathogenesis and immunity31.
Recently, we discovered that lncRNA HOTAIR plays a critical role in inflammation32,33. Notably, HOTAIR, is one of the most well-studied LncRNAs, which regulates gene silencing via coordination with PRC2 and LSD1 complexes34–37. Our studies showed that, beyond its classical roles in gene silencing, HOTAIR regulates the expression of proinflammatory cytokines, glucose transporter, glucose uptake, and glucose metabolism in macrophages during inflammation and this is mediated via regulation of NF-κB activation. Taken together, our findings demonstrated the critical roles of HOTAIR in inflammation, macrophage activation, and immune response32,33. Thus, LncRNAs appear to be integral component of inflammation and immune response. Here we aimed to discover novel LncRNAs that are critical regulators of inflammation and immune signaling in humans in an unbiased manner and explored their potential functions and regulations. For discovering human specific LncRNAs associated with inflammation, we used macrophages derived from THP1 cells (monocytes, human acute leukemia cells). Notably, THP1-derived macrophages are widely used as a model cell line38–40 for human macrophages for studying inflammation and immune response.
## Cell culture, macrophage differentiation, and treatment with Lipopolysaccharide (LPS)
Human acute leukemia cells (THP1, monocyte, ATCC) were cultured and maintained in T75 cell cultured flasks using RPMI-1640 media, supplemented with $10\%$ heat-inactivated FBS (fetal bovine serum), 2 mM L-glutamine, 100 units/mL penicillin and 0.1 mg/mL streptomycin) in a humidified incubator with $5\%$ CO2 and $95\%$ air at 37 °C41–43.
## Differentiation of THP1 cells (monocytes) into macrophages
THP1 cells were grown in 60 mm cell culture plate using complete RPMI-1640, treated with 25 nM of Phorbol 12-myristate 13-acetate (PMA, stock solution in DMSO) for 48 h42,43. Media was replaced with complete RPMI-1640 medium without PMA and incubated for additional 24 h for the recovery and this resulted in differentiated THP1-derived macrophage (THP1-MΦ). Notably, THP1 monocytes grow in suspension, however, after differentiation into macrophages, they become adherent. THP1-MΦ were further characterized by immunostaining for expression of surface antigens (such as CD68).
## LPS-treatment
3 × 106 cells THP1 cells were seeded in 60 mm cell culture dishes, differentiated into THP1-MΦ using PMA (as above) and then treated with LPS (1.0 μg/mL, Invivogen) for 4 h (or varying dose or time periods) and subjected to RNA and protein extraction, and immunostaining, as needed32,33.
## RNA extraction
Total RNA was extracted from cells cultured in 60 mm dish using RNeasy RNA extraction kit (Qiagen) following the manufacturer’s protocol. The final RNA was eluted and quantified using nanodrop spectrophotometer. Prior to making the libraries, RNA concentration was again measured on a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, USA) using Qubit RNA BR Assay Kit (Cat# Q10210, Thermo Fisher Scientific, USA). RNA quality was assessed on an Agilent Biotechnologies 4200 TapeStation (Agilent Technologies, USA) using RNA ScreenTape (Cat# 5067–5576), and average RIN was 8.7.
## Library preparation and transcriptome sequencing44–46
The input for library construction was 500 ng of total RNA which was delivered in a 10 µL volume. Then total RNA samples (500 ng) were hybridized with Ribo-Zero Gold to substantially deplete cytoplasmic and mitochondrial rRNA from the samples. Stranded RNA sequencing libraries were prepared as described using the Illumina TruSeq Stranded Total RNA Library Prep Gold kit (Cat# 20020598, Illumina, USA) with IDT-for Illumina TruSeq RNA UD Indexes (Cat# 20023785, Illumina, USA). The average insert size of libraries constructed with the TruSeq Stranded Total RNA Library Prep Kit was 200 bp. Purified libraries were qualified on an Agilent Technologies 4200 TapeStation using a D1000 ScreenTape assay (cat# 5067–5582). The molarity of adapter-modified molecules was defined by quantitative PCR using the Kapa Biosystems Kapa Library Quant Kit (Cat#KK4824, KAPA). Individual libraries were normalized to 1.30 nM in preparation for Illumina sequence analysis. Sequencing libraries (1.3 nM) were chemically denatured and applied to an Illumina NovaSeq flow cell using the NovaSeq XP chemistry workflow (Cat# 20021664, Illumina, USA). Following transfer of the flow cell to an Illumina NovaSeq instrument, a 2 × 151 cycle paired end sequence run was performed using a NovaSeq S4 reagent Kit (Cat# 200122866, Illumina, USA).
## Analysis of RNA-seq data47,48
Reads with phred quality scores less than 20 and less than 35 bp after trimming were removed from further analysis using trimgalore (v0.4.1). Quality-filtered reads were then aligned to the reference genome using the HISAT (v 2.0.1) PMID: 27560171) aligner using default settings and marked duplicates using Sambamba (v0.6.6) (PMID: 25697820). Aligned reads were quantified using ‘featurecount’ (v1.4.6) (PMID: 30783653) per gene ID against GENCODE (PMID: 30357393). *Differential* gene expression analysis was done using the R package edgeR (v3.10.5) (PMID:19910308)49,50. Cutoff values of absolute fold change greater than 2.0 and FDR ≤ 0.05 were then used to select for differentially expressed genes between sample group comparisons.
## RNA extraction, cDNA synthesis, and real-time PCR32,33
Total RNA was extracted from the control and treated THP1-MΦ cells using TRIzol™ Reagent (Invitrogen) according to the manufacturer’s instructions. Briefly, supernatant from the cell culture plates were discarded after treatment, then 500 μL of Trizol reagent were directly added into the plate containing cells, incubated for 10 min. Cell lysate was harvested into 1.5 mL Eppendorf tube and further incubated on ice for 30 min with occasional mixing. Chloroform (100 μL, one fifth volume of Trizol reagent) was added to the cell lysate, mixed, incubated (15 min, on ice) and then centrifuged at 12,000 rpm for 15 min (at 4 °C). The top aqueous layer was collected carefully, mixed with equal volume of isopropanol (10 min, rt) and centrifuged at 12,000 rpm (10 min, at 4 °C). The precipitated RNA pellet was washed with $70\%$ ethanol (ice cold), air dried, and finally the RNA was dissolved in 50 μL of RNase-free water (DEPC treated, Sigma) and quantified using a Nanodrop spectrophotometer.
## cDNA synthesis
cDNA synthesis (reverse transcription) was performed in two steps, RT1 and RT2. In RT1, 1 μg of total RNA was mixed with 0.6 μL of oligo (dT)15 primer (Promega, 500 μg/mL stock) and RNase free water to a final volume of 12 μL, and incubated for 15 min at 70 °C. In RT2, 5 μL of 5 X M-MLV RT Buffer (Promega), 2 μL of DTT (10 mM DTT stock in nuclease free water), 0.25 μL of dNTP Mix (40 mM stock, Promega), 0.25 μL of RNase Inhibitor (40 U/μL, Promega), 0.5 μL of M-MLV Reverse Transcriptase (200 U/μL, Promega) was mixed with nuclease free water to a final volume of 13 μL and then combined with RT1 (after the initial incubation). For the cDNA synthesis, the RT1 and RT2 mix (total volume 25 μL) was incubated for 90 min at 37 °C, followed by 5 min incubation at 95 °C, and then at 4 °C (final hold), in a thermocycler. Finally, the cDNA was diluted in nuclease free water to a final volume of 100 μL.
## qPCR analysis
The qPCR was performed in CFX96 real-time detection system (Bio-Rad), using iTaq Universal SYBR Green Supermix (Bio-Rad) with gene specific PCR primers as listed in Table 1. In brief, 2 µL of cDNA (template) was mixed with 1 µL of gene specific primer pair (0.5 µM final concentration for both forward and reverse), 3 µL of iTaq Universal SYBR Green Supermix, and nuclease free water to a final volume of 10 µL. The polymerase chain reaction was programmed for an initial denaturation at 95 °C for 2 min and in-loop denaturation at 95 °C for 5 s, both annealing and polymerization combined for 30 s at 58 °C for 39 cycles. The threshold fluorescence (RFU) was determined by the CFX96 real-time detection system (Bio-Rad) software. The threshold cycles (Ct) of each expression data were normalized to the corresponding β-Actin expression and expressed as 2(−ΔCt). Each qPCR analysis was performed in three parallel replicates and the experiment was repeated thrice. Table 1qPCR Primer sequences Forward (5′-3′) Reverse (5′-3′).GeneForward (5’ → 3’)Reverse (5’ → 3’)Gene specific qPCR primersβ-ActinCTCTTCCAGCCTTCCTTCCTAGCACTGTGTTGGCGTACAGIL6GAAAGCAGCAAAGAGGCACTTTTCACCAGGCAAGTCTCCTIL1βAAGGCGGCCAGGATATAACTCCCTAGGGATTGAGTCCACAACOD1GGTTTTCTCCAGTGCCCATACAACTTGCCAAGCTTCAACAIDO1TCAGTGCCTCCAGTTCCTTTCCTGAGGAGCTACCATCTGCTNFAIP6CAACTCTGCCCTTAGCCATCAAGCTCACCTACGCAGAAGCCXCL11TGGGATTTAGGCATCGTTGTCCTGGGGTAAAAGCAGTGAADLL4ACAGTAGGTGCCCGTGAATCGCGAGAAGAAAGTGGACAGGADORA2A-AS1TCATGGTGAAGGGTGATGAAGCTCAGAAAGCTTGGACACCAC007362.3GGCTTTGGATGGTTGAAGAGTCCCCTAAGCTCCTTCCTGTAP001610.5GCACGTTCTCTCCCCAAATACTTCAGGTGGAACACGAGGTRP11-519G16.3GGGAAATTCCATGGTTTCCTGGGTCCTCAAATCAGCTGTCRP1-68D18.4GCGCTGTGGTCCAATAGACTCCAGAGGACAAAAGGCAAAAAntisense oligonucleotides (ASO) and Scramble oligonucleotide sequences (5′-3′)hLinfRNA1– ASO1GGCAGATCTCTTCACTCCAGAhLinfRNA1 –ASO2TTCGTAGACAAGCATGTGGTGhLinfRNA1 –ASO3TCTTCCTCGGTAGTCCTGTGAScramble oligoTCCATGGCCAACACTTGTCA
## Protein extraction and western blot analyses32,33
The cells were washed twice in ice-cold PBS, mixed with RIPA cell lysis buffer (Thermo Fisher), complete protease inhibitor cocktail (1 X), and phosphatase inhibitor (1X) cocktail (Roche) and incubated 30 min on ice. The resulting cell lysates were centrifuged at 13,000 rpm at 4 °C for 10 min. The supernatant was collected, and protein was quantified using BCA protein assay kit (Pierce). For the Western blot, 30 μg protein was loaded onto $10\%$ SDS-PAGE gels, transferred to nitrocellulose membrane, and blocked with $5\%$ nonfat dry milk (in 1 X TBST). Membranes were washed (thrice, 1 X TBST, 5 min each), incubated with primary antibodies against Phospho-IκBα (1:1000 dilution, 2859S, Cell Signaling), Phospho-p65 (NF-κB subunit, 1:1000 dilution, 3033S, Cell Signaling), IκBα (1:1000 dilution, 4814 T, Cell Signaling), p65 (1:1000 dilution, 10745-1-AP, Proteintech), IL6 (1:1000 dilution, GTX110527, GeneTex), ACOD1 (1: 1000 dilution, 775010S, Cell Signaling), and IDO1 (1:1000 dilution, 13268-1-AP, Proteintech), IL1 β (16806-1-AP, Proteintech) and β-actin (1:1000 dilution, A2066, Sigma) overnight at 4 °C. Membranes were washed (thrice) and incubated with AP-conjugated Goat anti-mouse (# ab97020, Abcam) or goat anti-rabbit secondary (# ab6722, Abcam) antibodies for 2 h. Membranes were washed (thrice) and developed with BCIP-NBT (Alkaline phosphatase substrate) solution. For ECL Western blot, we used Horseradish peroxidase conjugated goat Anti-Mouse (# ab6789, Abcam) or goat Anti-Rabbit (# ab6721, Abcam) secondary antibodies and developed with developed using Pierce ECL western blotting substrate in LI-COR C-Digit blot scanner. Bands were quantified with ImageJ software and plotted.
## NF-κB inhibition assay32,33
THP1 cells (3 × 106) were seeded in 60 mm cell culture dishes and differentiated into macrophages (THP1-MΦ, using PMA as above). After differentiation, cells were initially treated with IKKβ inhibitor (SC-514, 25 μM, Sigma) for 1 h (to inhibit NF-kB signaling) followed by treatment with LPS (1 μg/mL) for 4 h. RNA and proteins were isolated and analyzed by RT-qPCR and Western blotting. For the phospho-protein analysis, cells were harvested at 1 h post LPS-treatment and proteins were isolated and probed for Western blotting.
## Immunofluorescence microscopy analysis
THP1 cells were seeded on cover slips and differentiated into macrophage (THP1-MΦ), fixed in $4\%$ paraformaldehyde (PFA, 10 min at rt), washed (1 X PBST, thrice 5 min each), permeabilized with $0.1\%$ triton X100 (15 min, 1 X PBST, rt), and blocked with $3\%$ BSA (in 1XPBST for 1 h). The cells were then incubated with mouse anti-human CD68 primary antibody (# 14-0688-82, Invitrogen, 4 h, rt). Cells were washed 3 times with PBST followed by incubation with FITC-conjugated goat Anti-Mouse (# 6785, Abcam) secondary antibodies for 1 h at RT. DAPI was added to the cells with a final concentration of 1 μg/mL and incubated for 15 min. Finally, the cells were washed 3 times with PBST and fixed with mounting media on a slide and analyzed under a fluorescence microscope (Nikon ECLIPSE TE2000-U).
## Antisense-mediated knockdown of hLinfRNA1
THP1 cells (1 million/well) was seeded in a 6-well plate and differentiated into THP1-MΦ cells using PMA (as described above). The media was exchanged with fresh complete RPMI prior and then subjected to transfection with LinfRNA1-specific and scramble antisense oligonucleotide (ASO) using similar protocols described earlier51–53. Briefly, transfection cocktail was prepared by mixing 1.5 μg of ASO (diluted with 100 μL of 1X transfection buffer) with 3 μL of transfection reagent (GeneMute, SignaGen) at room temperature for 15 min. The cocktail was added to the cells dropwise and gently mixed throughout the well by swirling. After 5 h of incubation, the cultured media was replaced by 2 mL of complete media and cells were further incubated for 43 h, then treated with LPS (1 μg/mL) and incubated for additional 4 h. Control and transfected cells were harvested and subjected to RNA purification using Trizol reagent. RNA was reverse transcribed and subjected qPCR analysis using primers specific to hLinfRNA1 and others. Human beta-actin was used as loading control.
## Statistical analysis
Each experiment was performed three times with at least three replicates ($$n = 3$$) independently. Data are presented as means ± SEM. All Statistical significance was calculated by unpaired Student’s t test (GraphPad Prism 6), and P ≤ 0.05 was considered statistically significant.
## Differentiation of THP1 cells into corresponding macrophages and analyzing their inflammatory response
To discover LncRNAs that are associated with inflammation and immune response in an unbiased manner, we performed RNA-seq analyses in human THP1 derived macrophage (THP1-MΦ) cells that were stimulated with LPS. Prior to LPS stimulation, THP1 cells were differentiated in macrophages as described previously42,43. Briefly, the THP1 cells were treated with 25 nM PMA for 48 h followed 24 h recovery. The differentiation of THP1 cells into macrophages (THP1-MΦ) were confirmed by analyzing the expression of macrophage specific surface marker antigen (CD68) using immunofluorescence microscopy (Fig. 1A). Briefly, THP1-MΦ were immune-stained with anit-CD68 antibody followed by immunostaining with FITC- tagged secondary antibody. The immuno-fluorescence microscopic analysis showed that CD68 is expressed at the membrane surfaces suggesting successful differentiation of the THP1 monocytic cells into macrophages. We also examined the inflammatory response of control (undifferentiated THP1) with differentiated THP1-MΦ. Briefly, undifferentiated THP1 and THP1-MΦ were independently treated with endotoxin lipopolysaccharide (LPS, 1 µg/mL, 4 h) and then expression of well-known pro-inflammatory markers such as interleukin 6 (IL6) and IL1β were analyzed using RT-qPCR. Our analysis showed that, LPS treatment induced the expression of both IL6 (250-fold) and IL1β (sixfold) mRNA in THP1-MΦ macrophages (Fig. 1B). However, LPS has no significant impacts on IL6 and IL1β expression in undifferentiated THP1 (monocytes) cells. These observations further demonstrated that PMA treatment indeed resulted in differentiation of THP1 cells (monocytes) into corresponding macrophages (THP1-MΦ) and macrophages are responsive to inflammatory stimuli such as LPS-treatment. Notably, the THP1 cells (monocytes) that grew in suspension culture became adherent upon differentiation to macrophages with PMA. Any nonadherent cells were removed during media replacement and the differentiated THP1-MΦ were used for the further experiments. Figure 1PMA induced differentiation of THP1 monocytes into macrophages (THP1-Mɸ) and their response to LPS-stimulation. ( A) THP1 cells (monocytes) were differentiated using PMA (25 nM, 72 h) on a coverslip (35 mm cell culture plate). Cells were immuno-stained with CD68 antibody (mouse) followed by FITC-conjugated secondary antibodies, counterstained with DNA binding dye DAPI, mounted, and analyzed under a fluorescence microscope. Images taken at 40X resolution (bar = 50 μm). ( B) LPS-stimulation of THP1 (monocytes) and THP1-Mɸ. THP1 and THP1-Mɸ cells were treated with LPS (1 μg/mL, 4 h) independently, total RNA was isolated, reverse transcribed to cDNA, and analyzed by RT-qPCR for expression of IL-6 and IL-1β. Each experiment was repeated at least thrice with three parallel replicates. β-Actin was used as loading control. Data represent mean ± SEM ($$n = 3$$); *$p \leq 0.05$, **$p \leq 0.001$, ***$p \leq 0.0001.$
## RNA-seq analysis to identify LncRNAs associated with inflammation
To identify LncRNAs associated with inflammation, we performed RNAseq analysis in THP1-MΦ that were stimulated with LPS. Briefly, THP1-MΦ cells were treated with LPS (1 µg/mL, 4 h). RNA was isolated from control (untreated) and LPS-treated THP1-MΦ, quantified and subjected to RNAseq analysis. Briefly, total RNA from the control and LPS-treated THP1-MΦ isolated and subjected to ribodepletion using RiboZero Gold kit to substantially deplete cytoplasmic and mitochondrial rRNA from the samples. Stranded RNA sequencing libraries were prepared using the Illumina TruSeq Stranded Total RNA Library Prep Gold kit and the average insert size of libraries constructed was 200 bp. Purified libraries were qualified, normalized (to 1.30 nM), and then applied to RNAseq analysis using Illumina NovaSeq instrument. Data analyses were performed at the UT Southwestern’s bioinformatics core facility and differentially expressed genes were compared and plotted. These analysis demonstrated that LPS-stimulation of THP1-MΦ resulted in upregulation of many well-known markers of inflammation, these include proinflammatory cytokines IL6 (interleukin 6), TNFα (tumor necrosis factor alpha), CXCL (chemokines), CL(chemokines), and others54,55 (Fig. 2). Additionally, we also found up- and down-regulation of many protein coding genes such as ACOD1 (aconitate decarboxylase 1)56,57, IDO1 (Indoleamine 2,3-Dioxygenase 1)58–60, and others (Fig. 2) under LPS treatment conditions suggesting their potential association with inflammation and immune response. Notably, ACOD1, also known as IRG1 (immune responsive gene 1) is previously implicated in inflammation and immune response)56,57. Similarly, IDO1, a heme-based enzyme, is a well-known player in tryptophan catabolism and inflammation61–64. IDO1 is upregulated in inflammatory diseases and is a major drug target for immunotherapy. Thus, LPS-induced upregulation of cytokines and well-known proinflammatory genes suggested the potential functionality of other novel genes that are identified based on our RNAseq analysis. Figure 2RNAseq analysis of LPS-treated macrophages. THP1 cells were differentiated into PMA into macrophages (THP1-MΦ), treated with LPS (1.0 μg/mL) for 4 h. Total RNA was extracted from the control cells (C1–C3) and LPS-treated THP1-MΦ cells (L1–L3), quantified and subjected to ribo-depletion followed by library construction using the Illumina TruSeq Stranded Total RNA Library Prep Gold kit. Libraries were sequenced in an Illumina NovaSeq instrument. *Differential* gene expression analysis was done using the R package edgeR (v3.10.5) (PMID:19910308). Differentially expressed genes (log2 -old) were plotted as a heatmap. Cutoff values of absolute fold change greater than 2.0 and FDR ≤ 0.05 were used to select for differentially expressed genes between sample group comparisons.
In addition to gene expression analysis, we also analyzed the different pathways that are potentially affected upon LPS stimulation of THP1-MΦ using Panther-based database analysis. These analyses demonstrated that highly affected pathways include Toll like receptor (TLR) signaling, gonadotropin-releasing hormone receptor pathways, inflammation mediated chemokine and cytokines signaling pathways, interleukin signaling pathways and others (Fig. 3, Table 2 lists some of the genes affected in respective pathways. Notably, TLRs are crucial players in inflammation and immune response65–68. TLRs are class of pattern recognition receptors (PRRs) which recognize a variety of pathogen-associated molecular patterns (PAMPs) and danger associated molecular patters (DAMPs) derived from various pathogens and trigger downstream signaling, induction of cytokines and pro-inflammatory genes resulting in inflammation, macrophage activation and immune response. Similar to TLR signaling, gonadotropin-releasing hormone receptor, chemokine and cytokines signaling, and interleukin signaling pathways are also closely integrated with inflammation response and immune signaling69,70. These analyses further demonstrated that LPS-treatment induced the inflammatory response in THP1-derived macrophages. Figure 3Pathways affected by LPS-stimulation of THP1-MΦ. RNAseq data was analysis using Panther-based data analysis to identify different signaling pathways that are affected by LPS-stimulation of macrophages. Table 2LPS-induced inflammation associated signaling pathways and genes expression. GeneLog2 fold changeApoptosis signalingBIRC39.23TNF8.11NFKB25.35NFKBIA5.26BCL2A14.94NFKB14.49RELB4.00MAP2K33.88CFLAR3.58TNFSF103.57Toll receptor signalingNR4A17.99SRC6.57BMP6544MAP3K85.27SMAD14.92PTGIR3.98ATF33.89MAP2L33.88JUNB3.42PTGER43.07Gonadotropin-releasing hormoneTNFAIP36.04PTGS25.95NFKB25.35MAP3K85.27NFKBIA5.26IFNB14.75NFKB14.49IRF73.90MAP2K33.88MYD882.65Gonadotropin-releasing hormoneNR4A17.99SRC6.57BMP65.44MAP3K85.27SMAD14.92PTGIR3.98ATF33.89MAP2K33.88JUNB3.42PTGER43.07CCKR signaling mapBIRC39.29NR4A17.99CXCL86.68SRC6.57CXCL16.44PTGS25.95IER35.28NFKBIA5.26CETP4.59SNAI13.10Inflammation mediatedby chemikinesCCL3L311.13CCL89.82CCL39.20IL63.71IL1B7.52BCL3,AC0920666.96CXCL106.80CCL206.71CXCL86.68GNG46.27Interleukin signalingIL68.71CXCL86.68IL23A6.51STAT46.26ILIA4.94IL15RA4.39IL153.71IL10RA3.52STAT5A3.50STAT23.02Oxidative stress responseDUSP85.21DUSP25.00MAP2K33.88DUSP13.52DUSP163.21AC067945,STAT12.58MYC2.05DUSP41.99TXNL11.40JUN1.25JAK STAT signalingSTAT46.26SOCS14.88STAT5A3.50STAT23.02AC067945,STAT12.53JAK11.10STAT31.10 Interestingly, our analysis demonstrated that along with well-known protein coding genes, cytokines and chemokine, LPS-stimulation of THP1-MΦ, also significantly affected (induction and down regulation) the expression of many noncoding RNAs. The highly upregulated long-noncoding RNA transcripts include ADORA2A-AS1 (ADORA2A antisense transcript 1), AC007362.3, AP001610.5, RP11-519G16.3, RP1-68D18.4, and many others (Fig. 2). We termed this novel of class of noncoding transcripts as Long-noncoding inflammation associated RNAs, LinfRNAs. We labelled these human LinfRNAs (hLinfRNA) as hLinfRNA1 (ADORA2A-AS1), hLinfRNA2 (AC007362.3), hLinfRNA3 (AP001610.5), hLinfRNA4 (RP11-519G16.3), hLinfRNA5 (RP1-68D18.4) and others as their order of upregulation (Log2 fold change) in the RNAseq analysis. Significant induction of hLinfRNAs along with protein coding genes and cytokines suggest their potential association with inflammation, macrophage activation, and immune response. However, it’s important to note that though these hLinfRNAs are identified as a transcript induced by LPS and they are potentially involved with inflammation signaling, their detailed structures and functions mostly remain elusive.
## hLinfRNAs expressions are induced by LPS
To further confirm the LPS-induced expression of coding and noncoding transcripts, we performed RT-qPCR analysis for the above highly upregulated coding and noncoding transcripts in the control and LPS-treated THP1-MΦ cells. As seen in Fig. 4, LPS-treatment induced the expression (mRNA levels) of well-known cytokines such as IL6 (425 fold) and IL1β (20 fold), coding genes ACOD1 (700 fold) and IDO1 (240 fold), TNFαIP6 (TNFα inducible protein 6; 300 fold), CXCLL11 (chemokine; 270 fold), DLL4 (300 fold) (Figs. 4A-B). LPS-treatment also induced the noncoding transcripts such as LinfRNA1 (ADORA2A-AS1; 1.7 fold), hLinfRNA2 (AC007362.3; threefold), hLinfRNA3 (AP001610.5; 30 fold), hLinfRNA4 (RP11-519G16.3; 2.3 fold), and hLinfRNA5 (RP1-68D18.4; 40 fold) (Fig. 4C). In addition to the RNA level, we also analyzed the protein level expression of well-known pro-inflammatory genes IL6 and IL1β, and top protein coding genes such as ACOD1 and IDO1 using western blot (Fig. 4D, quantification in Fig. 4E, and supplementary Figure S1). Beta-actin was used as a loading control. Interestingly, the LPS treatment indeed induced the expression of IL6, IL1β, ACOD1, and IDO1 in protein levels. The LPS induced expression of pro-inflammatory cytokines (IL6, and IL1β), protein coding genes (e.g. ACOD1, IDO1 and others), and noncoding transcripts such as hLinfRNAs (#1–5), and others, suggest that hLinfRNAs are potentially associated with inflammation and immune response in macrophages. Thus, along with many protein coding genes, our RNAseq analyses led to the discovery of a series of human specific LinfRNAs (hLinfRNAs) that are potential regulators of inflammation and the immune response in humans. Figure 4LPS induces inflammation in THP1-macrophages (THP1-Mɸ). THP1-Mɸ cells were treated with LPS (1 μg/mL, 4 h), total RNA and proteins were isolated. RNA was reverse transcribed to cDNA and analyzed by RT-qPCR for expression of proinflammatory cytokines like IL-6 and IL-1β (A), as well as top upregulated protein coding genes (found in RNA-seq analysis) including ACOD1 and IDO1 at transcript level (B); and hLinfRNAs (1–5) (C). ( D) Western blot analysis of protein coding genes. Proteins from the control and LPS-treated THP1-MΦ were analyzed by Western blot using antibodies against IL6, IL-1β, ACOD1, IDO1, and β-Actin (control). Bands were quantified and plotted in Fig. 4E. The specific region selected for each western blot is shown by red–rectangle in original respective western blot in the supplementary figure S1. Each experiment was repeated at least thrice with three parallel replicates. β-Actin was used as loading control. Data represent mean ± SEM ($$n = 3$$); *$p \leq 0.05$, **$p \leq 0.001$, ***$p \leq 0.0001.$
## LPS-induced expression of hLinfRNAs at varying doses and varying time of LPS treatments
To further understand the potential association of LinfRNAs with inflammation, we examined their LPS-induced expression at varying concentration of LPS and varying time of LPS-treatments and compared that with well-known inflammatory markers such as IL6 and IL1β. Briefly, THP1-MΦ cells were treated with varying concentrations of LPS (0.1–1000 ng/mL, for 4 h) or for a varying time period (up to 8 h) with 1000 ng/mL of LPS treatment. RNA was analyzed by RT-qPCR for the expression of LinfRNAs along with IL6 and IL1β. Dose-dependent analysis showed that the expression of IL6 and IL1β were increased with the increase in concentration of LPS and reaching a plateau at around 10–100 ng/mL and similar effects were observed for hLinfRNAs with some variations for different hLinfRNAs (Fig. 5). In particular, hLinfRNA1 (2.5 fold), hLinfRNA2 (fourfold), hLinfRNA4 (fourfold) and hLinfRNA5 (sevenfold) were induced by 2.5. 4, 4 and sevenfold respectively in response to LPS-treatment and reached a plateau at 1 ng/mL LPS (Fig. 5). Although, the hLinfRNA3 shows more dose dependent response and reaches the plateau around at 10 ng/mL like IL6. To be consistent for different genes, we used 1000 ng/mL LPS treatment for further studies. Figure 5hLinfRNAs are expressed in a dose-dependent manner in THP1-macrophages (THP1-Mɸ) under LPS induced inflammation. THP1-MΦ cells were treated with varying concentration of LPS (0.1- 1000 ng/mL, 4 h), total RNA was isolated and analyzed by RT-qPCR for expression of proinflammatory cytokines (IL6, IL-1β) and top 5 hLinfRNAs. β-Actin was used as loading control. Data represents mean ± SEM ($$n = 3$$).
Temporal studies demonstrated that IL6 and IL1β were increased with time reaching a maximum around 6 h and then decreased (Fig. 6). Interestingly, similar to IL6 and IL1β, LinfRNA3 induction was increased with time reaching a maximum around 6 h and then decreased suggesting similar type of expression behavior of LinfRNA in comparison to the IL6 and IL-1β (Fig. 6). hLinfRNA4 expression followed is increased with time and potentially plateaued at round 6–8 h of LPS treatments. hLinfRNA5 is relatively early responsive, while hLinfRNA1 may be late responsive compared to IL6 (Fig. 6). Thus, even though hLinfRNAs expression are induced by LPS-stimulation of THP1-MΦ, each RNA may have distinct modes of regulation during inflammation and macrophage activation and this behavior is similar to many inflammation associated protein coding genes, cytokines and chemokines. Figure 6Temporal expression of hLinfRNAs under LPS-stimulation of THP1-Mɸ. THP1-Mɸ cells were treated with LPS (1 μg/mL) for varying time periods. RNA was analyzed by RT-qPCR for expression of proinflammatory cytokines (IL6, IL-1β) and top 5 hLinfRNAs. Each experiment was repeated at least thrice with three parallel replicates. β-Actin was used as loading control. Data represents mean ± SEM ($$n = 3$$).
## LinfRNAs expressions are regulated via NF-κB
Inflammation signaling is complex and may follow diverse pathways. Upon infections, pathogen associated PAMPs/DAMPs activate variety of PRRs (e.g. TLRs)65–68 that triggers signaling cascades and activates transcription factors such as NF-κB, AP1, IRFs, STATs, and induce transcription of pro-inflammatory genes (cytokines, chemokines, IFNs etc.) leading to inflammation response. LPS is well known to trigger TLR (TLR4 in particular) signaling and follow NF-κB activation. Here, initially, aimed to investigate if LinfRNA expressions are regulated by NF-κB activation. Briefly, THP1-MΦ cells were treated with IKKβ (IκB-kinase) inhibitors SC514 (25 and 50 µM, 1 h) followed by stimulation with LPS (1 μg/mL, 4 h), as described by us previously32,33. Notably, IKKβ is a kinase that phosphorylates IκBα (which inhibits NF-κB)71–74. Thus, the inhibition of IKKβ results in deactivation of NF-κB73,74. Thus, treatment with SC514 will result in inhibition of NF-κB activation and suppress inflammatory response. RNA and proteins from the control and SC514 treated (+ /− LPS-stimulation) THP1-MΦ cells were analyzed by RT-qPCR and Western blot for the expression of inflammatory genes and hLinfRNAs. Western blot analysis showed that LPS-treatment induced phospho-p65 (NF-κB subunit) as well as the phospho- IκBα level in comparison to the untreated control (Fig. 7A, quantification in 7B, supplementary figure S2), this LPS-induced increased phospho-p65 (NF-κB subunit) and the phospho- IκBα level was down-regulated upon treatment with SC514, suggesting LPS-induced activation of NF-κB and its deactivation by SC514. Notably, the total p65 level was mostly unchanged upon LPS or SC514 treatment, however, the IκBα level was reduced by LPS suggesting its degradation and this was inhibited by SC514 treatment (Fig. 7A, quantification in 7B, supplementary figure S2). The RNA from the control, SC-514 and LPS-treated cells were also analyzed by RT-qPCR for the expression of pro-inflammatory cytokine IL6. This analysis demonstrated that IL6 expression is induced by LPS-treatment, and this was significantly downregulated upon treatment with SC514, further demonstrating the SC514-mediated inhibition inflammatory response via inhibition of NF-κB activation (Fig. 7C). Interestingly, RT-qPCR analysis also demonstrated that LPS-induced expression of most hLinfRNAs (such as hLinfRNA$\frac{1}{2}$/$\frac{4}{5}$) were effectively suppressed by SC514, while expression of hLinfRNA3 was not affected (Fig. 7D). For example, similar to IL6, the expression of hLinfRNA1 (ADORA2A-AS1) was elevated (threefold) by LPS and treatment with SC514 down-regulated the LPS-induced elevation of hLinfRNA1 to almost basal level (Fig. 7D). Similar impacts of SC514 were observed for hLinfRNA2 and hLinfRNA4. ( Fig. 7D), where LPS-induced expression was down-regulated to the basal level in presence of SC514-treatments. The basal level of expression (in the absence of LPS-treatment) of most hLinfRNAs were also suppressed by SC514-treatment. The LPS-induced expression of hLinfRNA5 was also repressed by SC514 ($25\%$), though to smaller extent in comparison to hLinfRNA1, 2 and 4 (Fig. 7D). In contrast to IL6 and hLinfRNAs$\frac{1}{2}$/$\frac{4}{5}$, the LPS-induced elevation of hLinfRNA3 is most remain unaffected upon treatment with SC514 (Fig. 7D), These observations suggest that LSP-induced hLncRNAs$\frac{1}{2}$/$\frac{4}{5}$ expression are potentially regulated by NF-κB activation in macrophages, while hLinfRNA3 is likely regulated by other mechanism. Figure 7hLinfRNAs are regulated by NF-κB signaling pathway in THP1-macrophages. THP1-MΦ cells were treated with IKKβί (SC-514, 25 μM, 1 h) followed by LPS (1 μg/mL). RNA and proteins were isolated from the control and LPS (with and without SC514) -treated cells and analyzed by RT-qPCR and Western blotting respectively. ( A-B) Western blot analysis for the IκBα, phospho-IκBα, p65 and phospho-p65 (β-actin was used as a loading control). Quantifications are shown in panel 7B. The specific region selected for each western blot are shown by red–rectangle in the original respective western blots, supplementary figure S2. C-D) RT-qPCR analysis for the expression of pro-inflammatory cytokine (IL6, panel C) and hLinfRNAs (1–5, panel D). Data represents mean ± SEM ($$n = 3$$). * $p \leq 0.05$, **$p \leq 0.001$, ***$p \leq 0.0001.$
## hLinfRNA1 knockdown downregulates pro-inflammatory cytokines expression under inflammation
To understand the potential function of LinfRNAs in inflammation, we knocked down one LinfRNA, hLinfRNA1 in macrophages and then analyzed its impacts on LPS-induced cytokine expression. To knockdown hLinfRNA1, we initially designed three antisense oligonucleotides (ASOs) for against hLinfRNA1 (Table 1) and tested for their knockdown efficacies, human beta-actin was used a loading control. hLinfRNA1-ASO1 and ASO3 showed effective knockdown efficacies of $50\%$) (Fig. 8A) and were used for additional experiments. Notably, hLinfRNA1 (NR_028484.3) is a 2831 nt long LncRNA with splice variants (NR_028483.2; 2052 nt), located in chromosome 22. Briefly, THP1-MΦ cells were transfected (48 h) with LinfRNA1-ASOs and scramble-ASO (control), independently, and then treated with LPS (additional 4 h). RNA was isolated and analyzed by RT-qPCR. Our analysis showed that hLinfRNA1 expression was induced by LPS and this was effectively knocked down (> $50\%$) upon transfection with hLinfRNA1-ASO1/ASO3 (Fig. 8A). Scramble-antisense has no significant impact on LPS-induced hLinfRNA1 expression. Interestingly, hLinfRNA1 knockdown down-regulated the LPS-induced expression of pro-inflammatory genes IL6, TNFα and IL1β significantly, suggesting critical roles of hLinfRNA1 in regulation of cytokines expression and inflammation in macrophage (Fig. 8A & 8B). Our observations demonstrated that hLinfRNA1 is not only regulated via NF-κB activation, but also is functional in regulation of NF-κB regulated cytokines expression during inflammation. Detailed roles of hLinfRNAs still remains elusive and our future goals. Figure 8Knockdown of hLinfRNA1 down-regulates the LPS-induced inflammatory response in macrophage. THP1-MΦ cells were transfected with hLinfRNA specific antisense oligonucleotide (ASO1 and ASO3) and scramble antisense for 48 h, stimulated with LPS (1 μg/mL) and incubated for additional 4 h. RNA was analyzed by RT-qPCR for expression of hLinfRNA1 and proinflammatory cytokines IL6, TNFα and IL1β (Fig. 8A) and PCR amplified product was analyzed in $2\%$ agarose gel electrophoresis (Fig. 8B). The specific region selected for each agarose gel is shown by red–rectangle in the supplementary figure S3. Each experiment was repeated at least thrice with three parallel replicates. β-Actin was used as loading control. Data represents mean ± SEM ($$n = 3$$); *$p \leq 0.05$, **$p \leq 0.001$, ***$p \leq 0.0001.$
## Discussion
According to the World Health Organization (WHO), infectious and inflammatory diseases remain a leading cause of death worldwide, with over 17 million new cases per year. Nearly 50,000 people die every day from infectious diseases75 because no treatments are available for many of these conditions. Sepsis, bowel disease, colitis, stroke, respiratory disease, obesity, diabetes, and cancer all have roots in chronic inflammation76,77. The prevalence of chronic inflammatory diseases is persistently increasing in the USA and around the world. Thus, understanding the mechanisms underlying inflammation and discovering novel regulators of inflammation are important for developing novel diagnostics and therapies.
NcRNAs are class of transcripts, which are encoded by the genome and transcribed, however, remains untranslated78–83. They are abundant in the cells and tissues and many of them are being detected in diseased cells, tissue, and circulating body fluids. NcRNAs are classified based on the sizes: small (< 50 nt), medium (50–200 nt), and long noncoding RNAs (lncRNAs, > 200 nt). Though, a large number of ncRNAs are being discovered, their structures and biochemical functions remains mostly unknown. NcRNAs, being nucleic acids, have the ability to interact with different proteins and other nucleic acids and thus, may contribute to alter protein and enzyme functions and may modulate cell signaling pathways, and ultimately may influence gene transcription, translation and gene expression processes.
Emerging evidence suggests that, along with protein-based factors, ncRNAs are also closely associated with inflammation and immune response. Our recent studies demonstrate that LncRNA HOTAIR plays key roles in cytokine regulation and inflammation in macrophages32,33. HOTAIR expression is upregulated in macrophages in response to LPS-induced inflammation. HOTAIR regulates NF-κB activation via regulation of IκBα degradation and hence regulates expression of cytokines and pro-inflammatory genes such as IL6, iNOS expression. Additionally, studies from our laboratory also demonstrated that HOTAIR regulates LPS-induced glucose transporter (Glut1) expression in macrophages and that in turn regulates the glucose uptake and metabolism. Notably, macrophages utilize glucose metabolism as a primary source of energy during inflammation. Thus, HOTAIR plays a critical role in regulation of inflammatory response and glucose metabolism in macrophages. Here, we aimed to discover novel lncRNAs that are critically linked to inflammation and immune signaling, in an unbiased manner. We performed an RNA-sequencing analysis in THP1-derived macrophages (THP1-MΦ) that were stimulated with LPS. Based on these analyses, we have discovered a series of novel human LncRNAs (termed as hLinfRNAs) that are potential regulators of immune response and inflammation. Similar to well-known protein coding genes and markers of inflammation, many hLinfRNAs are significantly up- and down-regulated upon LPS-induced inflammation in THP1-MΦ, suggesting their potential involved in macrophage activation, inflammation and immune signaling in human. Time course and concentration dependent LPS-stimulation induces these hLinfRNAs in macrophages. Thus, based on RNAseq analysis, we discovered a series of hLinfRNAs that are novel regulators of inflammation and immune signaling in humans. The functions of most of these LncRNAs remain elusive.
Inflammation and immune signaling are very complex processes and are associated with activation of macrophages and other immune cells65. The inflammation signaling may involve variety of pathways, receptors and factors. Notably, LPS in known to activate well known family of TLR receptors to induce inflammatory response. Among others, the transcription factor, NF-κB activation plays a central role in immune response and inflammation. NF-κB activation induces expression of NF-κB regulated cytokines and pro-inflammatory genes65,84. In the absence of any inflammation stimuli, NF-κB is complexes with I-κBα and remains inactive. However, upon inflammation signal, the I-κBα get phosphorylated followed by polyubiquitination and degradation. The degradation of I-κBα releases NF-κB (activated), which translocate to the nucleus, binds to the target gene promoters resulting in their induction in gene expression. As LPS-is well known to activate TLR signaling and NF-κB activation during inflammation, we investigated if newly discovered hLinfRNAs are potentially regulated via NF-κB signaling pathways. Towards, we applied well-known IKKβ inhibitor, SC514, which inhibits NF-κB. Importantly, application SC514 suppressed the LPS-induced expression of well-known cytokines and most hLinfRNAs (such as hLinfRNA1, 2, 4 & 5), suggesting their potential regulation via NF-κB signaling. Notably, there were few hLinfRNAs (such as hLinfRNA3) expressions were not affected upon NF-κB inhibition suggesting alternate mode of regulations.
The functions of most of these hLinfRNAs is unknown. To understand the potential roles of hLinfRNAs in inflammation, we knocked down the one of the hLinfRNAs, hLinfRNA1 (ADORA-2A-AS1), using anti-sense oligonucleotide (ASO) on in THP1-MΦ and analyzed LPS-dependent expression of cytokine expression. Interestingly, our results demonstrated that application of hLinfRNA1-ASO not only knocked the level of hLinfRNA1 expression, but also down-regulated the expression of well-known cytokines such as IL6, IL1β and TNFα. These observations demonstrate that hLinfRNAs are functional and are potential regulators of cytokines expression, inflammation, and immune response.
Independent studies from our laboratory and others demonstrate that lncRNAs plays critical roles in inflammation and immune signaling32,85–91.LncRNA being long in sizes, have the ability interact with variety of proteins and transcription factors and modulate their activities and thus lncRNAs may influence enzymatic functions and cell signaling events. Our discovery of the series of hLinfRNAs suggests their potential functions in immune response and inflammation. Notably, the expression levels of different LinfRNAs vary and often it is much lower in comparison to well-known cytokines and pro-inflammatory genes expression. However, their time course and response behavior follow similar patterns to many cytokine expressions. Like cytokines and pro-inflammatory genes, most LPS-induced hLinfRNAs expression are regulated via NF-κB signaling pathways. Additionally, independent knockdown of hLinfRNAs altered the expression of well-known cytokines and suggested their functionality in inflammation, macrophage activation and immune signaling. Irrespective of their level of induction, each hLinfRNAs are unique and expected to have their own mode of regulation and functions. Overall, here we discovered a series of novel hLinfRNAs that are potentially regulators of inflammation and immune signaling. A model showing the induction of hLinfRNAs during macrophage activation and their potential roles in inflammation and macrophage activation is shown in Fig. 9. Like many other lncRNAs, the functions of hLinfRNAs remain unknown and will require a significant amount of time for their structural and functional characterization. The modes of action of different hLinfRNAs may vary. Similar to other nucleic acids, hLinfRNAs may interact with proteins and enzymes regulating their structures and functions and eventually contributing towards regulation of gene expression and regulation, cell signaling and metabolism, differentiation, growth and development. Many lncRNAs may act as a precursor to microRNAs or may also act as microRNA sponge and thus, may regulate mRNA stability, functions, and cell signaling events. Misregulation of hLinfRNA may contribute towards human diseases. Nevertheless, the discovery of the novel hLinfRNAs opens new avenues for screening their expressions in different types of inflammatory and immune diseases towards discovery LinfRNA-based biomarkers and therapeutic targets. Figure 9Model showing the induction of hLinfRNAs and their roles in inflammation. LPS induces TLRs activation and that triggers a cascade of downstream signaling including NF-κB activation and that induces expression of cytokines and pro-inflammatory genes. Our studies demonstrated that along with cytokines, LPS-induces the expression of a series of noncoding RNAs including LinfRNAs. LinfRNA expression are regulated via NF-κB signaling and potentially via other pathways. Knockdown of hLinfRNAs affect the expression of cytokines suggesting their functionality and potential roles in inflammation. Detailed structure functions of most hLinfRNAs remain elusive.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30568-1.
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|
---
title: 'Prevalence of silver resistance determinants and extended-spectrum β-lactamases
in bacterial species causing wound infection: First report from Bangladesh'
authors:
- Kazi Sarjana Safain
- Mohammad Sazzadul Islam
- Jumanah Amatullah
- Mohammad Al Mahmud-Un-Nabi
- Golam Sarower Bhuyan
- Jakia Rahman
- Suprovath Kumar Sarker
- Md Tarikul Islam
- Rosy Sultana
- Firdausi Qadri
- Kaiissar Mannoor
journal: New Microbes and New Infections
year: 2023
pmcid: PMC10006487
doi: 10.1016/j.nmni.2023.101104
license: CC BY 4.0
---
# Prevalence of silver resistance determinants and extended-spectrum β-lactamases in bacterial species causing wound infection: First report from Bangladesh
## Abstract
### Background
The use of silver is rapidly rising in wound care and silver-containing dressings are widely used along with other antibiotics, particularly β-lactams. Consequently, concerns are being raised regarding the emergence of silver-resistance and cross-resistance to β-lactams. Therefore, this study aimed to determine the phenotypic and genotypic profiles of silver-resistance and extended-spectrum β-lactamases in isolates from chronic wounds.
### Methods
317 wound swab specimens were collected from tertiary hospitals of Dhaka city and analysed for the microbial identification. The antibiotic resistance/susceptibility profiles were determined and phenotypes of silver resistant isolates were examined. The presence of silver-resistance (sil) genes (silE, silP, and silS) and extended-spectrum β-lactamases (ESBL) (CTX-M-1, NDM-1, KPC, OXA-48, and VIM-1) were explored in isolated microorganisms.
### Results
A total of 501 strains were isolated with *Staphylococcus aureus* ($24\%$) as the predominant organism. In $29\%$ of the samples, polymicrobial infections were observed. A large proportion of Enterobacterales ($59\%$) was resistant to carbapenems and a significantly high multiple antibiotic-resistance indexes (>0.2) were seen for $53\%$ of organisms ($P \leq 0.001$). According to molecular analysis, the most prevalent types of ESBL and sil gene were CTX-M-1 ($47\%$) and silE ($42\%$), respectively. Furthermore, phenotypic silver-nitrate susceptibility testing showed significant minimum-inhibitory-concentration patterns between sil-negative and sil-positive isolates. We further observed co-occurrence of silver-resistance determinants and ESBLs ($65\%$).
### Conclusions
Notably, this is the first-time detection of silver-resistance along with its co-detection with ESBLs in Bangladesh. This research highlights the need for selecting appropriate treatment strategies and developing new alternative therapies to minimize microbial infection in wounds.
## Funding information
This study was partially funded by The World Academy of Sciences (TWAS), Research Grant number 17-555.
## 1Introduction
Antimicrobial resistance (AMR) has become a global health issue in recent decades [1,2]. Burn victims are particularly vulnerable to this predicament since wounds frequently provide a favourable-habitat for the colonization of microorganisms. CDC maintains that burn-centres have the highest incidence of primary bloodstream infections among all ICUs [3]. Wounds are associated with high-rates of morbidity and mortality and are known to be a cause of significant economic burden [4,5]. For instance, USA alone spends $25 billion every year to manage chronic wounds and the interest in wound consideration is expanding radically [6].
For the treatment of infections caused by microorganisms of mixed-species, depending on the wound type, topical antimicrobials are used which has been reflected in the increased usage of silver in wounds [7]. For the treatment of burns and chronic wounds, silver compounds have been used for hundreds of years [8]. Silver-coated dressings are used for traumatic wounds and silver-impregnated polymers are ordinarily utilized in clinical gadgets. Therefore, there is a chance to develop a risk of nosocomial infections in hospitals by developing resistance against silver-ions [9].
Concerns have been raised about the misuse of silver and the potential emergence of bacterial resistance to silver, particularly in clinical-settings. An increasing number of outbreaks caused by silver-resistant strains of Enterobacterales had been reported worldwide [9,10]. Many clinicians and scientists had addressed whether the inescapable usage of silver could prompt cross-protection from antimicrobials, as biocides are frequently recommended with other antimicrobials [11,12]. It has been reported that silver could affect AMR directly by targeting porin deficiency, thereby mediating cross-resistance to β-lactams in particular [13]. Thus, investigation of the frequency of silver-resistance is important because plasmid transfer to develop cross-resistance to β-lactam antimicrobials is a high possibility. Previous studies reported that silver-resistance genes could be present on a plasmid carrying AMR genes; particularly extended-spectrum β-lactamases (ESBLs) [14,15]. Also, high-level dissemination of ESBL-producing Enterobacterales in wound-infections had been reported in most regions of the world [16,17] which affects low-income countries the most [18].
Due to the misuse and overuse of antimicrobials and poor healthcare standards in Bangladesh, AMR has been increasing gradually. In the last decade, the research works conducted in Bangladesh on wound-infections were only limited to the phenotypes of AMR [19,20] and thus, there is a paucity of data regarding the resistance spectrum to silver-nitrate of wound-derived bacterial isolates and the associated genetic-profiles. The co-occurrence of ESBL and silver-resistance (sil) genes among bacteria is a matter of concern because this phenomenon may help foster AMR. Hence, we investigated the phenotypic and genotypic profiles of bacterial-resistance to silver. In addition, the co-occurrence of sil genes and ESBLs in isolates from chronic-wound specimens has been studied. To the best of our knowledge, this is the first evidence from Bangladesh.
## Sample collection and microbiological analysis
This retrospective-analysis was conducted by reviewing records of chronic wound-swab samples that arrived at the Microbiology laboratory of Bangladesh Institute of Health Sciences Hospital and Shaheed Suhrawardy Medical College & Hospital at Dhaka, Bangladesh from January 2017 to March 2019. A total of 317 wound specimens were collected and analysed.
After superficial pre-cleansing of wounds with physiological saline, each specimen was collected by rotating a sterile, pre-moistened swab stick across the wound surface. Next, the swab was placed in the tube containing transport-medium and sent to the laboratory. The samples were then processed according to standard techniques. Bacterial identification and confirmation were performed by routine conventional microbial cultures and biochemical tests using standard techniques [21].
## Antimicrobial susceptibility testing
In this study, the Clinical and Laboratory Standard Institute (CLSI) guideline was followed to profile the antimicrobial susceptibility/resistance pattern of the isolates [22]. This profiling was performed by the modified Kirby-Bauer disc-diffusion method on Mueller–Hinton agar (MHA) plates. The only exception was Streptococcus agalactiae, which was cultured in MHA with $5\%$ sheep blood.
Antibiotic-susceptibility test discs in cartridges for tetracycline, trimethoprim-sulfamethoxazole, rifampicin, cefoxitin, nalidixic acid, cotrimoxazole, ciprofloxacin, azithromycin, ampicillin, cefixime, gentamicin, chloramphenicol, ceftriaxone, imipenem, piperacillin-tazobactam, penicillin, vancomycin, linezolid, colistin, erythromycin, televancin and clindamycin were obtained from Oxoid (Hampshire, UK). The cartridges were stored between 4 °C and −20 °C and allowed to come to room temperature before use. After inoculation with the isolates and placement of the disks, plates were incubated at 37 °C for 24 h and the zones of inhibition were measured.
## Determination of MAR index
The multiple antibiotic-resistance (MAR) index was calculated as the ratio of the number of antibiotics to which an organism was resistant (a) to the total number of antibiotics used in the susceptibility testing for the specific organism (b) [23].
## Phenotypic silver nitrate susceptibility testing
Entire isolates were subjected to MIC measurements by broth-microdilution method, in which ≥512 μg/mL was considered a clinical-breakpoint for silver-resistance [24]. Two-fold serial dilutions of silver-nitrate solution (Sigma Aldrich, USA) were prepared using deionized water, to obtain a concentration from 4 to 512 μg/mL.
## Detection of ESBL and silver resistance genes
The isolated bacterial colonies were subjected to genomic DNA extraction using QIAamp DNA Mini Kit (Qiagen, Germany) and the manufacturer's instructions were followed. 16S rRNA positive DNA extracts were examined for the presence of resistance-genes for silver and β-lactams by PCR using primer-sets for each resistance-gene which has been described in Supplementary Table 1. T100™ thermal cycler (Bio-Rad, USA) was used for gene-specific PCR amplification. Each 10 μL PCR reaction volume contained 1 μL 10x PCR buffer, 0.3 μL 50 mM MgCl2, 0.2 μL of 10 mM dNTPs mixture, 0.5 μL forward and reverse primers, 0.05 μL of Taq polymerase, 5.45 μL nuclease-free water and 2 μL of template DNA. Each reaction underwent initial denaturation at 95 °C for 5 min, 35cycles of denaturation at 95 °C for 30 s, annealing at 55 °C, cyclic extension at 72 °C for 45 s, and final extension at 72 °C for 6 min. The amplicons were visualized under UV light after electrophoresis through $1\%$ agarose gel stained with SYBR Safe (Invitrogen, USA) staining. Next, the PCR products were purified using a Quick PCR product purification kit (Invitrogen, USA).
## Sequencing of ESBL and sil genes
Sanger DNA sequencing was performed using ABI PRISM software version 3.1.0. Sequencing data were analysed by Chromas Lite 2.4 software to identify the target sequence by alignment with the reference sequence. The obtained sequence was further analysed using Basic Local Alignment Search Tool.
## Data analysis
Graphs were generated using GraphPad Prism v7 software. Cochran chi-square test was performed using http://www.openepi.com/website where the threshold for statistical significance was $P \leq 0.05.$
## Rate of isolation and polymicrobial infections
A total of 501 strains were isolated from 317 wound-specimens. As per microbial-culture and biochemical tests, 8 different microbial species were identified which constituted of $33\%$ Gram-positive and $67\%$ Gram-negative organisms. The most common bacterial-species detected included *Staphylococcus aureus* ($25\%$), followed by *Escherichia coli* ($19\%$), *Klebsiella pneumoniae* ($16\%$), *Pseudomonas aeruginosa* ($11\%$), *Proteus mirabilis* ($8\%$), *Streptococcus agalactiae* ($8\%$), *Enterobacter cloacae* ($7\%$), and *Acinetobacter baumannii* ($6\%$) (Fig. 1a).Fig. 1Percentages of different species of microorganisms and polymicrobial infections. ( a) Percentages of different species of microorganisms isolated from 317 wound swab samples and (b) Percentages of the most common bacterial co-infections in the wound swab specimens. Fig. 1 In addition, polymicrobial-infections were found in 91 ($29\%$) of the infected-wounds. Two species made up the majority of polymicrobial infections; whereas three species were the highest to be isolated from a sample, and it made up $3\%$ of the total polymicrobial infections. The most common combinations were *Staphylococcus aureus* and *Klebsiella pneumoniae* ($13\%$), *Staphylococcus aureus* and *Pseudomonas aeruginosa* ($10\%$), *Escherichia coli* and *Klebsiella pneumoniae* ($9\%$) and; *Staphylococcus aureus* and *Escherichia coli* ($6\%$) (Fig. 1b).
## Antimicrobial resistance phenotype
Next, we wanted to see the antimicrobial-resistance patterns of the isolated Gram-negative and Gram-positive organisms (Table 1). As per antibiogram data, $67\%$ of E. coli and $61\%$ of K. pneumoniae were resistant to 3rd generation cephalosporins. Among S. aureus isolates, $73\%$ were methicillin-resistant and $43\%$ were vancomycin-resistant. In addition, $59\%$ of Enterobacterales were resistant to carbapenem with E. coli ($76\%$) and K. pneumoniae ($69\%$) as the predominant microorganisms. Contrarily, P. mirabilis exhibited $100\%$ sensitivity to 3rd generation cephalosporin, whereas only $9\%$ A. baumannii and $10\%$ E. cloacae showed resistance to 3rd generation cephalosporins. $7\%$ S. agalactiae demonstrated to be penicillin-resistant. Table 1Antimicrobial resistance patterns of the organisms isolated. Table 1IsolatesNumber of tested organismsNumber of resistant organisms% resistance among the tested organismsNumber of resistant organisms% resistanceCarbapenem-resistant3rd generation cephalosporin-resistantE. coli8565765767K. pneumoniae6545694061P. mirabilis40112700E. cloacae30930310P. aeruginosa501122714A. baumannii2541629Vancomycin resistantMethicillin-resistant *Staphylococcus aureus* (MRSA)S. aureus12152438973Penicillin-resistantS. agalactiae4037 Table 2 shows the antibiotic-resistance profile and MAR-index of the indicated organisms. The proportion of isolates with MAR-index values less than 0.2 and greater than 0.2 were $47\%$ and $53\%$, respectively and the difference between these MAR-values has been found to be statistically significant ($P \leq 0.001$), demonstrating wound as high-risk contaminating source favouring growth of resistant-bacteria. A large proportion of E. coli ($73\%$), K. pneumoniae ($69\%$), and S. aureus ($67\%$) isolates exhibited MAR values greater than 0.2.Table 2MAR index values among the identified organisms. P values are calculated with Cochran chi-square test. Table 2MARIndex (Range)E. coli n(%)K. pneumoniae n(%)P. mirabilis n(%)P. aeruginosa n(%)A. baumannii n(%)S. aureus n(%)E. cloacae n(%)S. agalactiae n(%)P value<0.223 [27]20 [31]25 [62]23 [46]17 [68]40 [33]22 [73]31 [77]<0.001>0.262 [73]45 [69]15 [38]27 [54]8 [32]81 [67]8 [27]9 [23]
## Association of silver resistance gene variants with phenotypic silver nitrate susceptibility
Next, we determined the phenotypic and genotypic profiles of sil genes for the collected organisms. First, we checked MIC for silver nitrate. Consistent MIC patterns were observed in sil gene-carrying isolates as higher MIC endpoints were observed in most cases. $65\%$ ($\frac{101}{155}$) of the sil gene-bearing isolates showed to be phenotypically-resistant (MIC ≥512 μg/mL) as shown in Table 3. With acquisition of sil gene, $55\%$ K. pneumoniae, $55\%$ P. mirabilis, $75\%$ E. coli, $63\%$ P. aeruginosa, $80\%$ A. baumannii, $62\%$ S. aureus and $70\%$ E. cloacae revealed higher MIC endpoints at ≥512 μg/mL, generating significant P values. Since no isolates showed MIC for silver-nitrate at 4 μg/mL, data has not been shown for MIC at this point. Albeit, most sil-negative isolates exhibited lower MICs, $17\%$ ($\frac{59}{346}$) of the sil-negative isolates demonstrated phenotypic silver-nitrate resistance. On the contrary, S. agalactiae isolates showed no resistance to silver. This observation indicates that bacterial isolates which were phenotypically resistant to silver-nitrate harboured sil genes and the results were statistically significant compared to sil-negative bacterial isolates. Table 3Association of the presence of silver resistance genes with the minimum inhibitory concentration of silver nitrate. P values are calculated with Cochran chi-square test. Table 3OrganismsPresence of sil genesNumber of isolatesSilver nitrate MIC (μg/ml), n(%)P value163264128256≥512E. coli ($$n = 85$$)Positive44––2 [5]–9 [20]33 [75]<0.001Negative415 [12]9 [22]–20 [49]–7 [17]K. pneumoniae ($$n = 65$$)Positive22–––4 [18]6 [27]12 [55]0.008Negative435 [12]7 [16]–2 [4]10 [23]19 [44]P. mirabilis ($$n = 40$$)Positive11––3 [27]2 [18]–6 [55]0.001Negative293 [10]12 [42]10 [34]2 [7]–2 [7]P. aeruginosa ($$n = 50$$)Positive16–––4 [25]2 [12]10 [63]0.001Negative345 [15]12 [35]5 [15]9 [27]–3 [8]A. baumannii ($$n = 25$$)Positive5–––1 [20]4 [80]0.087Negative201 [5]4 [20]4 [20]5 [25]3 [15]3 [15]S. aureus ($$n = 121$$)Positive47–––8 [17]10 [21]29 [62]0.001Negative746 [8]6 [8]5 [7]18 [24]20 [27]19 [26]E. cloacae ($$n = 30$$)Positive10––––3 [30]7 [70]0.001Negative202 [10]7 [35]3 [15]6 [30]2 [10]–S. agalactiae ($$n = 40$$)Positive0––––––N/ANegative406 [15]–15 [38]4 [10]11 [28]4 [9]
## Occurrence of ESBL and sil genes in bacterial isolates
The 501 isolates were further analysed for the detection of ESBL and sil genes by sequencing the PCR products for the listed resistance-genes (Table 4). Among the ESBL genes investigated; CTX-M-1 ($47\%$) and VIM-1 ($24\%$) were the predominant resistance-genes, followed by KPC ($12\%$), OXA-48 ($9\%$), and NDM-1 ($8\%$). We found E. coli as the predominant reservoir of ESBLs as $49\%$, $38\%$, and $27\%$ of E. coli were seen to harbour CTX-M-1, NDM-1 and VIM-1 genes, respectively. A substantially higher-proportion of KPC ($42\%$) and OXA-48 ($31\%$) genes were observed in K. pneumoniae isolates. Importantly, genes encoding silver-resistance were detected exclusively in Enterobacterales. The most frequently identified sil gene was silE with the highest frequencies in E. coli ($40\%$).Table 4Occurrence of silver resistance determinants and ESBL genes in clinical isolates. Table 4OrganismsPercentage of antimicrobial resistance genes detected (%)CTX-M-1NDM-1VIM-1KPCOxa-48silEsilSsilPE. coli4938271228401931P. aeruginosa4829–31–201015K. pneumoniae621928423123–23A. baumannii23––––9–10S. aureus–––––311929E. cloacae3918–––19914P. mirabilis37––––411239
## Coexistence of ESBL and sil genes
We further demonstrated cross-resistance to silver and β-lactams and found that out of 501 clinical strains analysed, a total of 94 isolates demonstrated to harbour multiple resistance-genes (Supplementary Table 2). The most-common combinations were found to be CTX-M-1/silE ($33\%$), silE/silP/silS ($15\%$), CTX-M-1/silE/silP ($13\%$), and CTX-M-1/KPC/silE ($11\%$) among the 9 different types of coexistence-patterns (Fig. 2). $37\%$ E. coli, $31\%$ K. pneumoniae, $15\%$ P. mirabilis, $13\%$ E. cloacae, $20\%$ P. aeruginosa, $12\%$ A. baumannii and $17\%$ S. aureus were found to harbour multiple resistance genes. E. coli was the prevalent type bacterium which harboured 5 different sets of resistance-genes. Overall, the data revealed that a large proportion of Enterobacterales ($65\%$) harboured co-presence of single or multiple sil genes with or without different ESBLs. Fig. 2Rate of bacterial isolates harbouring multiple resistance genes. Fig. 2
## Discussion
In recent decades, wound healing has become a major therapeutic challenge in the health-sector, since several factors play a significant role in the wound-healing process and an infected wound can result in serious complications. Furthermore, wound provides an ideal environment for the transfer of plasmids, which may contain silver and other resistance-genes, as wound biofilm has been recognized as a significant niche for plasmid transfer [25]. Although silver-resistance is gradually becoming a major concern, especially for a developing-country like Bangladesh, there is no report about the co-occurrence of silver and β-lactam resistant-bacteria in clinical-specimens from Bangladesh. This is the first study demonstrating the presence of silver and β-lactam resistant-bacteria in wound-infected patients from Bangladesh and the information may help to adopt preventive measures to inhibit the rapid spread of such resistant-bacteria within the clinical-environment.
The study detected 8 different species in the wound-specimens. In addition, polymicrobial infections were found in $29\%$ of lesions. Such inter-species interactions are known to be dominated by bacterial-synergy, which increases their survival and complicates the eradication of infections [26]. Furthermore, more than half ($59\%$) of the total isolates were carbapenem-resistant Enterobacterales (CRE) with E. coli ($76\%$) as the predominant organism. This is in line with previous studies where CRE has been increasingly isolated [27,28]. In our investigation, $73\%$ S. aureus was found to be methicillin-resistant (MRSA). A study identified $72\%$ S. aureus strains as MRSA in an investigation in Bangladesh [29]. Therapeutic strategies for severe MRSA infections are indeed limited to fewer antibiotics and thus, it is a major health concern.
The emergence of plasmid-mediated silver-resistance raises concerns that silver-resistance will limit the efficacy of silver-based disinfectants in the future. Although some studies have reported silver-resistance [30,31], there are no such data from Bangladesh. As described by Gupta et al. [ 32], pMG101 is a 180-kb plasmid that accounts for resistance to multiple antibiotics and metals, including silver. *The* gene cassette harbouring silver resistance genes includes silP, silA, silB, silC, silR, silS, silE, ORF105, and silABC [33]. Among these, silE, silP, and silS are the most prevalent and have been investigated in prior studies [14]. Notably, our study could identify the three most common sil genes in Bangladesh and as expected, the occurrences of a single sil gene or a combination of multiple-genes were associated with increased phenotypic-resistance to silver-nitrate (higher MIC values). However, since there is no universal standard MIC breakpoint for silver-resistance, we used the cut-off value (≥512 μg/mL) according to recently published articles [10,34]. We, therefore, take this chance to emphasize the pressing necessity to establish the MIC breakpoint for silver resistance.
CTX-M-1 and silE were the most frequently detected ESBL and sil genes, respectively. Furthermore, these genes were most frequent among E. coli, and $80\%$ of E. coli harboured at least one of the investigated genes. Sil and ESBLs have most often been reported in members of Enterobacterales, a group of bacteria with the potential for causing infections [9]. We detected that $35\%$ S. aureus that belong to ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, *Pseudomonas aeruginosa* and Enterobacter spp.) pathogens also harboured silver-resistance genes and $17\%$ of these strains carried multiple sil genes. A recent study demonstrated that all plasmids from silver-resistant strains, even those from non- Enterobacterales group carried one of the tested replicon types of the Enterobacterales [35]. This along with some other studies indicate that occurrences of these resistance-genes in non- Enterobacterales members may have originated from organisms belonging to Enterobacterales group [36,37].
In addition, the frequent detection of CTX-M-1 in the present study also justifies the fact that CTX-M is the most commonly distributed ESBL among Enterobacterales and is considered a pandemic due to its global prevalence [37]. Furthermore, $85\%$ of the CRE were detected to be ESBL-producers, which is consistent with several other studies that have revealed the co-occurrences of multiple therapeutically relevant antibiotic-resistant genes in the same bacteria, resulting in enhanced resistance to various antimicrobials [38,39].
*Many* genes combined may make it easier for bacteria to emerge as high-risk enteric bacterial clones. According to multiple investigations, sil-positive isolates are substantially more common in CTX-M-positive isolates [15]. The study identified CTX-M-1 and silE ($33\%$) as the most prevalent combination. The observed frequent co-detection of CTX-M-1 and silE appeared concordant with a recent report from India [14]. ESBL production is linked to genetic-elements such as transposons, and heavy-metal determinants, which further complicates the scenario. Furthermore, it is possible that silver may exert selective pressure on CTX-M-producing Enterobacterales [15], and hence combined actions of these genes exert broad-spectrum resistance among microorganisms.
Genetic and phenotypic silver-resistance and ESBL were observed and the existence of such genes might provide some explanation for the high wound-infection rates. This is highly worrisome in terms of spread of resistance, especially within poly-microbial-infected wounds as there are only limited therapeutic options available. Surveillance of AMR plays a major role in patient-management, not only in the developing-countries but also in the developed-countries to establish prescription-guidelines and to determine investment in new therapies. Furthermore, strong evidence shows that AMR could disseminate globally across borders [40]. So, it is high time that nations should work together in reducing the emergence of global AMR. Also, appropriate measures should be taken to prevent the spread of these resistant-bacterial isolates by increasing awareness about health and hygiene and also by restricting the random use of antibiotics and antiseptics.
This study has several important drawbacks. Sample collection sites were limited to a few tertiary hospitals in the capital of Bangladesh. Nonetheless, our findings are similar to previous studies from other nations and thus the observed results are expected to be replicable in remaining parts of the country. Along with this, a comparison of wounds that were not infected, and which became infected could also be evaluated to shed light on this issue. Undoubtedly, more studies are required to find some mechanistic explanations for silver-nitrate phenotypic discrepancy to sil genes and further analyse the resistant and co-resistant bacterial strains through multi-locus sequence typing and whole-genome-sequencing. Even yet, for a country like Bangladesh with limited resources, this is a challenge. We invite attention to greater investigations and discussion about this issue in different hospital-infections in this dangerous circumstance where resistant-bacterial isolates are increasing at an alarming rate.
## CRediT authorship contribution statement
Kazi Sarjana Safain: Data curation, Formal analysis, Methodology, Validation, Visualization, Software, Writing - original draft, Writing - review & editing. Mohammad Sazzadul Islam: Formal analysis, Visualization, Conceptualization, Investigation, Supervision. Jumanah Amatullah: Formal analysis, Methodology, Validation, Visualization. Mohammad Al Mahmud-Un-Nabi: Methodology, Validation, Formal analysis, Software. Golam Sarower Bhuyan: Formal analysis, Supervision. Jakia Rahman: Formal analysis, Methodology. Suprovath Kumar Sarker: Methodology, Software. Md Tarikul Islam: Formal analysis. Rosy Sultana: Resources. Firdausi Qadri: Conceptualization, Project administration, Resources, Funding acquisition. Kaiissar Mannoor: Conceptualization, Investigation, Project administration, Funding acquisition, Supervision, Writing - review & editing.
## Declaration of competing interest
The author declares that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
## Supplementary data
The following are the *Supplementary data* to this article. Multimedia component 1Multimedia component 1Multimedia component 2Multimedia component 2
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|
---
title: Multivalent in vivo delivery of DNA-encoded bispecific T cell engagers effectively
controls heterogeneous GBM tumors and mitigates immune escape
authors:
- Daniel H. Park
- Kevin Liaw
- Pratik Bhojnagarwala
- Xizhou Zhu
- Jihae Choi
- Ali R. Ali
- Devivasha Bordoloi
- Ebony N. Gary
- Ryan P. O’Connell
- Abhijeet Kulkarni
- Diana Guimet
- Trevor Smith
- Alfredo Perales-Puchalt
- Ami Patel
- David B. Weiner
journal: Molecular Therapy Oncolytics
year: 2023
pmcid: PMC10006507
doi: 10.1016/j.omto.2023.02.004
license: CC BY 4.0
---
# Multivalent in vivo delivery of DNA-encoded bispecific T cell engagers effectively controls heterogeneous GBM tumors and mitigates immune escape
## Abstract
Glioblastoma multiforme (GBM) is among the most difficult cancers to treat with a 5-year survival rate less than $5\%$. An immunotherapeutic vaccine approach targeting GBM-specific antigen, EGFRvIII, previously demonstrated important clinical impact. However, immune escape variants were reported in the trial, suggesting that multivalent approaches targeting GBM-associated antigens may be of importance. Here we focused on multivalent in vivo delivery of synthetic DNA-encoded bispecific T cell engagers (DBTEs) targeting two GBM-associated antigens, EGFRvIII and HER2. We designed and optimized an EGFRvIII-DBTE that induced T cell-mediated cytotoxicity against EGFRvIII-expressing tumor cells. In vivo delivery in a single administration of EGFRvIII-DBTE resulted in durable expression over several months in NSG mice and potent tumor control and clearance in both peripheral and orthotopic animal models of GBM. Next, we combined delivery of EGFRvIII-DBTEs with an HER2-targeting DBTE to treat heterogeneous GBM tumors. In vivo delivery of dual DBTEs targeting these two GBM-associated antigens exhibited enhanced tumor control and clearance in a heterogeneous orthotopic GBM challenge, while treatment with single-target DBTE ultimately allowed for tumor escape. These studies support that combined delivery of DBTEs, targeting both EGFRvIII and HER2, can potentially improve outcomes of GBM immunotherapy, and such multivalent approaches deserve additional study.
Immune escape in cancer is a significant challenge for monovalent immunotherapeutic approaches. Here we show a dual tumor targeting approach for GBM, simultaneously targeting EGFRvIII and HER2 by DNA-encoded bispecific T cell engagers, to demonstrate suppression of immune escape in a heterogeneous GBM model.
## Graphical abstract
## Introduction
Glioblastoma multiforme (GBM) is the most lethal and aggressive glioma in adults with a 5-year survival rate of less than $5\%$.1 *With a* standard of care that comprises surgical resection, radiation, and chemotherapy, the median survival remains 15 months for GBM patients. Approximately $40\%$ of the patients have unresectable GBM and show poorer prognosis due to high recurrence rate.2 Currently, there is no Food and Drug Administration (FDA)-approved immunotherapy for GBM patients. The poor prognosis and the lack of alternative therapy illustrate the highly unmet clinical need of new therapies for GBM patients.
Recently, immunotherapies targeting epidermal growth factor receptor (EGFR) variant III (EGFRvIII) are receiving attention as potential treatment options for GBM. EGFRvIII is the most frequent mutant form of EGFR, which results from in-frame deletion of the EGF ligand-binding domain.3 EGFRvIII is an oncogenic, tumor-specific surface antigen that is present on up to $30\%$ of newly diagnosed GBM cases and is undetectable in normal tissues, making it an ideal target for immunotherapy.3,4 EGFRvIII-targeted approaches previously tested in clinical trials include chimeric antigen receptor T cells (CAR-T) as well as studies with a peptide vaccine strategy.5,6 However, they so far have demonstrated limited clinical benefits beyond the standard of care, with one obstacle reported of targeted antigen loss, resulting in tumor escape in treated patients.
Immune escape poses a significant challenge for antigen-targeted immunotherapies for GBM, which manifests a heterogeneous antigen landscape. GBM exhibits various degrees of antigenic heterogeneity. Clinical studies reveal that the expressions of antigens such as EGFRvIII and HER2 are highly heterogeneous in GBM patient samples.7,8 The antigen heterogeneity could be driven in part from tumor cells that evade immune surveillance by downregulation, mutation, deletion of antigen, and selective survival of antigen-negative tumor subpopulations.9,10,11 Such mechanisms of antigen escape create challenges for single antigen-targeted approaches in effectively eliminating the entire tumor burden and preventing recurrence. Thus, strategies that can target multiple tumor antigens simultaneously may be of importance for GBM patients.
Bispecific T cell engagers (BTEs), which target antigen-specific T cell-mediated anti-tumor cytotoxicity, are being studied for targeting solid tumors in preclinical and clinical studies.12,13 An EGFRvIII-targeting BTE was studied in an animal model of GBM, which demonstrated moderate tumor control as well as survival through delivery of 16 consecutive daily doses.14 Improving potency and in vivo pharmacokinetics are important for further development. Direct in vivo delivery of BTEs with more durable expression remains an important goal for study in therapeutic models of GBM. Such an approach could simplify clinical translation, providing patient benefit by improved pharmacokinetics likely with lower costs.
In a preliminary study, we described a DNA-encoded BTE (DBTE) targeting ovarian cancer in a peripheral challenge model.15 Here we build on this work focusing on engineering a new in vivo-produced EGFRvIII-targeting DBTE (EGFRvIII-DBTE) first as a monotherapy for direct in vivo treatment for GBM in both peripheral and orthotopic challenge animal models. We show the in vivo expression of the EGFRvIII-DBTE, specificity, T cell-mediated cytotoxicity, and efficacy in challenge models of GBM. We report that a single injection of EGFRvIII-DBTE exhibited durable in vivo expression and potent tumor regression and clearance in mice.
We advance the study to describe a delivery of multiple DBTEs as a potential combination therapy for heterogeneous GBM. The GBM-associated antigens, EGFRvIII and HER2, are expressed in up to $30\%$ and $80\%$ of GBM cases, respectively.3,16 We hypothesize that a combination approach of the EGFRvIII-DBTE in conjunction with HER2-targeting DBTE (HER2-DBTE) would limit GBM immune escape in vivo. We developed an EGFRvIII+/HER2+ heterogeneous GBM model to demonstrate that a co-administration of EGFRvIII-DBTE and HER2-DBTE dramatically enhanced tumor suppression in the heterogeneous GBM challenge as compared with single DBTEs. These findings suggest targeting multiple antigens, as was studied here, likely provide more potent tumor targeting and limit immune escape in GBM as well as other diverse cancers supporting improved patient benefit.
## Design and in vitro expression of EGFRvIII-targeted DNA-encoded bispecific T cell engager
To develop an EGFRvIII-targeted DNA-encoded bispecific T cell engager (EGFRvIII-DBTE), we identified variable fragment sequences for an EGFRvIII-binding antibody17 and a humanized CD3-binding antibody (clone UCHT-1). The sequences were modified to generate scFv sequences through codon optimization specific for in vivo expression and fusion with a GS linker (Figure 1A). For improved expression in mammalian cells, we added a human immunoglobulin (Ig)E leader sequence to the N terminus, as we have previously described,15 and encoded this construct in a modified pVAX1 expression vector. This EGFRvIII-DBTE was expressed in vitro using an Expi293 expression system. The supernatant from transfection studies was examined by western blotting to initially confirm expression, using pVAX1 empty vector as a negative control (Figure 1B).Figure 1Design, in vitro expression, and binding assessment of EGFRvIII-DBTE(A) Design and structure of EGFRvIII-DBTE. ( B) Western blot using supernatant of Expi293F cells transfected with EGFRvIII-DBTE or pVAX1 (vehicle control). ( C) Flow cytometry data showing on-cell binding activities of EGFRvIII-DBTE to U87vIII cells and human T cells. HER2-DBTE was used as an isotype control. ( D) Flow cytometry data showing T cell-bound U87vIII cells in the presence of EGFRvIII-DBTE. GFP is an EGFRvIII reporter. CD19-DBTE was used as an isotype control. ( E) Microscopic images showing T cell clustering around the U87vIII cells in a tumor-killing assay (5-h incubation).
## Generation of EGFRvIII-expressing tumor cells
To develop a therapeutic model of GBM, we generated a GBM cell line stably expressing EGFRvIII. We transfected Phoenix-AMPHO cells with a DNA plasmid encoding extracellular sequence of EGFRvIII, which produced gamma-retrovirus containing the EGFRvIII construct and then used the virus-containing media to transduce U87-MG, an aggressive malignant glioma cell line to generate a GBM cell line stably expressing EGFRvIII. The construct included a GFP reporter, allowing for identification of tumor cells in vitro as well as in vivo. The EGFRvIII-expressing U87-MG cells (U87vIII) were sorted via GFP and subcloned allowing for generation of a homogeneous stable positive population that then was validated by flow cytometry (Figure S1A).
## EGFRvIII-DBTE binds EGFRvIII and CD3
To examine the binding properties of EGFRvIII-DBTE, we incubated U87vIII cells with EGFRvIII-DBTE and stained them with an anti-human IgG F(ab’)2 fragment secondary antibody. For CD3-binding, primary human T cells were used. Supernatants collected from empty vector pVAX1 and HER2-targeted DBTE (HER2-DBTE) were used as controls. By flow cytometry analysis, we observed that EGFRvIII-DBTE engaged with both U87vIII and human T cells (Figure 1C). Binding specificity was also confirmed by ELISA in which EGFRvIII-DBTE did not bind to wild-type EGFR (Figure S2). We further examined EGFRvIII-DBTE’s ability to form an immunological synapse between the target and effector cells by co-incubating U87vIII cells and T cells in the presence of EGFRvIII-DBTE and examined cultures for T cells engaging with U87vIII cells. We gated on the tumor population and observed a double-positive population of GFP (U87vIII) and CD3 (T cells) in the presence of EGFRvIII-DBTE, indicating that T cells were engaging tumor cells (Figure 1D). This engagement was not observed in the presence of an irrelevant control DBTE. In another assay, we plated U87vIII cells in a tissue culture plate with T cells and observed by fluorescent microscopy that EGFRvIII-DBTE induced T cells to cluster around the target cells (Figure 1E). These data support that in vitro-expressed EGFRvIII-DBTE binds to both EGFRvIII and CD3, facilitating T cells to bind to target cells with high specificity.
## EGFRvIII-DBTE cytotoxicity against EGFRvIII-expressing tumor cells
We examined if EGFRvIII-DBTE can induce T cell-mediated cytotoxicity against U87vIII cells. We used the xCelligence RTCA system, which uses gold biosensors at the bottom of the special plate to continuously and non-invasively measure the relative cell counts by impedance differential created by cell attachment to the plate. U87vIII cells were plated in a 96-well E-plate and primary human T cells were added at an E:T ratio of 10:1 and EGFRvIII-DBTE at 10 ng/mL. The viability of U87vIII cells was measured in real time for 48 h by xCelligence RTCA analyzer. As a result, EGFRvIII-DBTE induced potent T cell-mediated cytotoxicity against U87vIII cells (Figure 2A). We observed that EGFRvIII-DBTE did not induce cytotoxicity without T cells (Figure 2B) or against EGFRvIII-negative U87 cells (Figure 2C). To assess a half maximal effective concentration (EC50) value of EGFRvIII-DBTE, we examined % cytolysis using EGFRvIII-DBTE at a series of concentrations in 48-h tumor-killing assay. Using primary T cells from four different donors, we determined that EC50 of EGFRvIII-DBTE against U87vIII cells was potent at 2.19 ng/mL, which is equivalent to 41.5 pM (Figure 2D). In addition, we examined the potency of EGFRvIII-DBTE in lower E:T ratios and observed that EGFRvIII-DBTE induced significant cytotoxicity at the low E:T ratio of 1:1 (Figure 2E).Figure 2T cell-mediated cytotoxicity of EGFRvIII-DBTEReal-time target cell viability in tumor-killing assay upon addition of EGFRvIII-DBTE (A) with human T cells, (B) without human T cells, and (C) with human T cells in the absence of EGFRvIII on target cells (U87-MG). ( D) *Percent cytolysis* data in 48-h tumor-killing assay using a series of concentrations of EGFRvIII-DBTE. T cells from four different donors were used to determine EC50 value. E:T ratio was 10:1 for (A)–(D). ( E) *Percent cytolysis* data in 48-h tumor-killing assay using a series of E:T ratios.
## Targeting tumors by EGFRvIII-DBTE drives T cell activation
To explore EGFRvIII-DBTE’s ability to enhance T cell functions, we examined activation markers and cytokine release in primary T cells after stimulation by EGFRvIII-DBTE. In the tumor-killing assay described above, we added fluorochrome-conjugated CD69 antibody and caspase-3 dye and monitored T cell activity. Upon addition of NOD scid gamma (NSG) mouse sera, which was treated with an intramuscular (IM) injection of 100 μg of EGFRvIII-DBTE followed by EP, we observed CD69 activation in T cells that was focused on target-bound T cells, as well as activation of the caspase-3 pathway in target cells (Figure 3A). The cell counts of GFP+ target cells decreased significantly by 12 h when EGFRvIII-DBTE was added (Figure 3B). CD69 activation (Figure 3C) and caspase-3 induction (Figure 3D) was rapid with initiation within 6 h and persistent throughout 48-h incubation, showing increasing signals for CD69. Video S1 is a movie showing side-by-side comparison between pVAX1 and EGFRvIII-DBTE in the tumor-killing assay. Here, we observed that T cells migrated toward the target tumor cells during CD69 activation (Video S1).Figure 3EGFRvIII-DBTE induces T cell activation(A) Fluorescent images of U87vIII cells in a tumor-killing assay upon addition of mouse sera treated with EGFRvIII-DBTE or pVAX1. Day 14 sera were used. Target cells are shown in green. CD69 activation is shown in red. Caspase-3 induction is shown in blue. ( B–D) Quantified GFP+ cell counts, CD69 activation, and caspase-3 induction in the tumor-killing assay. Flow cytometry data showing (E) IFN-γ, TNF-α, IL-2, and (F) CD107a responses in CD4+ T cells and CD8+ T cells in a 24-h tumor-killing assay. ( G) Tumor-killing assay with CD4+ T cells and/or CD8+ T cells in the presence of EGFRvIII-DBTE. ( H) Fluorescent images of the target cells at 0-, 6-, and 24-h time points.
Video S1. Time-lapse video of T cell-mediated cytotoxicity of EGFRvIII-DBTETime-lapse video of T cell-mediated cytotoxicity against U87vIII cells upon addition of day-14 serum of an NSG mouse treated with pVAX1 or EGFRvIII-DBTE. U87vIII is shown in green (GFP). Caspase-3 induction is shown in blue. CD69 activation is shown in red. Hour marks at the top left corner indicate incubation time after addition of T cells and mouse sera.
Next, cytokine responses were examined in T cells using the tumor-killing assay. T cells were collected after a 24-h incubation with U87vIII cells in the presence of EGFRvIII-DBTE and then analyzed by flow cytometry. CD19-DBTE was used as an isotype control. We observed that CD4+ and CD8+ T cell populations both exhibited increased secretion of interferon (IFN)-γ, tumor necrosis factor (TNF)-α, and interleukin (IL)-2, which are associated with anti-tumor activities (Figure 3E). CD8+ T cells displayed upregulation of IFN-γ and TNF-α secretion while CD4+ T cells exhibited an upregulation of IL-2 and TNF-α secretion. Both CD4+ and CD8+ T cells showed activation of CD107a, a marker for degranulation, with a greater response observed in CD8+ T cells (Figure 3F).
CD4+ and CD8+ T cells were sorted and used as effector cells in a tumor-killing assay to examine their independent cytotoxicity. We observed robust cytotoxicity from CD8+ T cells (Figure 3G). Importantly, CD4+ T cells also showed significant but lower cytotoxicity at a high E:T ratio of 20:1 (Figure 3G). By fluorescent microscopy, we observed that the onset of tumor cytolysis was more rapid in CD8+ T cells than in CD4+ T cells (Figure 3H). These data support that EGFRvIII-DBTE drives anti-tumor activation of both CD8+ and CD4+ T cells that can contribute to bispecific killing potential against tumor targets with slower kinetics.
## In vivo expression of EGFRvIII-DBTE
To determine functionality of EGFRvIII-DBTE expressed in vivo, we injected a single dose of EGFRvIII-DBTE or pVAX1 in the tibialis anterior (TA) muscle of NSG mice using electroporation (EP), as previously described.18 Sera were collected over a period of 105 days and EGFRvIII-DBTE activity was studied in 48-h T cell-mediated cytotoxicity assays against U87vIII cells to monitor tumor killing over time. We observed that a single injection of 100 μg of EGFRvIII-DBTE produced a durable expression in NSG mice of more than 100 days (Figure 4A). For comparison, sera of NSG mice given an intraperitoneal (i.p.) injection of 100 μg of protein EGFRvIIIxCD3 BTE was included as a control. Cytotoxicity of the i.p.-delivered protein EGFRvIIIxCD3 BTE peaked in the first day but quickly declined and diminished after 4 days (Figure 4A). In addition, we treated NSG mice with lower doses of EGFRvIII-DBTE and observed that the day 14 sera of the mice treated with as low as a 10-μg dose induced significant cytotoxicity against U87vIII cells (Figure 4B). These results illustrate that a single injection of EGFRvIII-DBTE can produce durable and potent in vivo expression, which is not observed following a single dose of protein BTE infusion and is dose-sparing. Figure 4In vivo expression of EGFRvIII-DBTE(A) T cell-mediated cytotoxicity assay against U87vIII cells using NSG mouse sera treated with a single injection (100 μg) of pVAX1, EGFRvIII-DBTE, or recombinant EGFRvIIIxCD3 antibody. ( B) T cell-mediated cytotoxicity assay against U87vIII cells using NSG mouse sera treated with various doses of EGFRvIII-DBTE. % *Cytolysis data* after 48-h incubation were plotted for both (A) and (B).
## Heterotopic model of GBM
To evaluate in vivo cytotoxicity of EGFRvIII-DBTE, we conducted a GBM challenge in NSG mice by injecting U87vIII cells subcutaneously in the right flank. At day 8 when tumor size had grown to 50 mm3, the mice were given an IM injection of 100 μg EGFRvIII-DBTE or pVAX1 in the TA muscle followed by EP and an i.p. injection of primary human T cells. Seven days later, we treated mice with another injection of DNA/EP and continued to monitor the tumor sizes over time (Figure 5A). We observed that all five of five mice treated with EGFRvIII-DBTE demonstrated tumor regression, with four animals clearing the challenge, whereas zero of five mice treated with pVAX1 controlled their tumor growth (Figures 5B and 5C).Figure 5Heterotopic GBM challenge(A) A scheme of heterotopic GBM challenge in NSG mice. ( B) Tumor volume in the challenge plotted over time. ( C) IVIS bioluminescent images of tumor burden in the challenged mice.
## EGFRvIII-DBTE clears tumor burden in an orthotopic animal model of GBM
To evaluate the efficacy of EGFRvIII-DBTE in an orthotopic model, we conducted an intracranial GBM challenge in NSG mice by injecting 1 × 105 U87vIII-luc cells in the right hemisphere of NSG mice. At day 6, we treated the mice with EGFRvIII-DBTE, HER2-DBTE, or pVAX1 in the TA muscle followed by EP. At day 7, mice received an i.p. injection of primary human T cells (Figure 6A). Tumor burden was monitored by IVIS Spectrum using in vivo-grade luciferin. We observed that 10 of 10 mice treated with EGFRvIII-DBTE exhibited tumor clearance. None of 10 mice treated with pVAX1 or 10 mice treated with HER2-DBTE targeting irrelevant antigen in this model showed tumor regression (Figures 6B–6D). All 20 of the control animals succumbed to the challenge, demonstrating the specificity of the EGFRvIII-DBTE. This study in NSG mice did not continue beyond approximately 28 to 34 days due to onset of chronic graft versus host disease in the surviving animals, as is described for the used model.19 Cryosections of the mouse brains were collected at the endpoints and examined by confocal microscopy for EGFRvIII expression in the tumor region of the brain. We observed that the brains of EGFRvIII-DBTE-treated mice showed clearance of tumor burden as well as EGFRvIII expression, neither of which was observed in the brains of pVAX1-treated mice (Figure 6E).Figure 6Intracerebral GBM challenge(A) A scheme of intracranial GBM challenge in NSG mice. ( B) Tumor burden of the challenged mice measured by IVIS. ( C) IVIS images of the challenged mice. ( D) Survival of the challenged mice. ( E) Representative confocal images of brain sections of the challenged NSG mice at the endpoint of the study. EGFRvIII expression is shown in magenta. Nuclei are shown in yellow.
## EGFRvIII+/HER2+ heterogeneous model of GBM
A significant challenge for GBM immunotherapy remains the heterogeneity of antigen expression, which permits tumor escape in single-agent immunotherapeutic approaches.5,6,11 A strategy for overcoming this issue could be co-delivery of multiple DBTEs targeting additional antigens. Here we targeted both EGFRvIII and HER2, which are expressed in up to $30\%$ and $80\%$ of GBM cases, respectively.3,16 *In this* set of studies, we used EGFRvIII-DBTE in conjunction with previously described HER2-DBTE, which showed efficacy in a HER2-expressing tumor model.15 To develop an EGFRvIII+/HER2+ heterogeneous model of GBM, we chose U87vIII (EGFRvIII+/HER2−) and U251 cells (EGFRvIII−/HER2+) (Figure S1B).
U87vIII and U251 cells were plated together in the same well in a 1:1 ratio mixture. Sera from mice treated with EGFRvIII-DBTE, HER2-DBTE, or combination of the two DBTEs was then added to the tumor cells along with primary T cells. After a 48-h incubation, the mice co-treated with both EGFRvIII-DBTE and HER2-DBTE exhibited enhanced cytotoxicity against heterogeneous tumor cells compared with sera of the mice treated with single DBTE (Figure 7A). We also observed that co-administration of the DBTEs did not impair killing of U87vIII cells when compared with EGFRvIII-DBTE or U251 cells when compared with HER2-DBTE, indicating co-delivery of the two DBTEs does not interfere with the expression and the tumor-killing capabilities of one another. In the heterogeneous tumor-killing assay, we dyed U251 cells with a cell-trace blue dye and observed by fluorescent microscopy that the mouse sera co-treated with two DBTEs induced apoptosis in both U87vIII (GFP) and U251 (blue) cell populations (Figure 7B).Figure 7EGFRvIII+/HER2+ heterogeneous model of GBM(A) T cell-mediated cytotoxicity assay against U87vIII cells (EGFRvIII+) and/or U251 cells (HER2+) using NSG mice treated with EGFRvIII-DBTE and/or HER2-DBTE. ( B) Fluorescent images of the heterogeneous tumor mixture (U87vIII/U251) in a 48-h tumor-killing assay.
## Co-delivery of EGFRvIII-DBTE and HER2-DBTE enhanced tumor regression and improved survival in an orthotopic animal model of heterogeneous GBM
To further investigate the efficacy of a combined treatment of EGFRvIII-DBTE and HER2-DBTE, we developed and conducted an intracranial challenge of heterogeneous GBM in NSG mice. In this model, we implanted 5 × 104 U87vIII-luc cells and 5 × 104 U251-luc cells in a single injection in the right hemisphere of NSG mice, with five female and five male mice per group. On day 6 of challenge, we treated animals with a single 200-μg dose of pVAX1, EGFRvIII-DBTE, HER2-DBTE, or both DBTEs delivered in separate sites. All mice were given an i.p. injection of 1 × 107 primary human T cells the following day (Figure 8A). Tumor burden was monitored by IVIS Spectrum using in vivo-grade luciferin. We observed enhanced tumor regression and survival in the group that received combined treatment of the two DBTEs over the groups that received single DBTE treatments (Figures 8B–8F). In the pVAX1-treated group, uncontrolled, aggressive tumor growths were observed in 10 of 10 mice (Figure 8B). In the EGFRvIII-DBTE-treated group, seven of 10 mice showed moderate tumor control initially and two mice lost tumor control soon after treatment, while one mouse succumbed to challenge after initial tumor escape (Figure 8C). In the HER2-DBTE-treated group, one mouse showed tumor regression while nine mice lost tumor control (Figure 8D). In the combined treatment group, eight of 10 mice showed complete tumor regression while two mice exhibited tumor escape (Figure 8E). At study completion on day 34, $80\%$ of the mice that received both DBTEs survived the challenge, whereas $20\%$ in the EGFRvIII-DBTE group, $10\%$ in HER2-DBTE group, and $0\%$ in pVAX1 group survived the heterogeneous GBM challenge (Figure 8F). This study was limited to 34 days due to onset of chronic graft versus host disease in the surviving animals.19Figure 8Co-delivery of EGFRvIII-DBTE and HER2-DBTE in heterogeneous GBM challenge(A) A scheme of heterogeneous orthotopic GBM challenge in NSG mice wherein a mixture of U87vIII cells and U251 cells were inoculated in the brain. ( B–E) Tumor burden of the challenged mice that received a treatment of (B) pVAX1, (C) EGFRvIII-DBTE, (D) HER2-DBTE, or (E) both EGFRvIII-DBTE and HER2-DBTE. ( F) Survival of the challenged NSG mice. ( G) IVIS images of the challenged mice. ( H) Representative confocal images of the brain sections of the challenged mice at the endpoints of the study. EGFRvIII expression is shown in magenta. HER2 expression is shown in cyan. Nuclei are shown in yellow.
At the endpoints of the study, cryosections of the brains were collected and examined for EGFRvIII and HER2 expression in the tumor region. By fluorescent confocal microscopy, we observed that the mice that received combination treatment cleared the tumor burden and demonstrated loss of both EGFRvIII and HER2 expressing cells, while EGFRvIII-DBTE-treated mice showed HER2 expression and conversely HER2-DBTE-treated mice showed continued EGFRvIII expression in the tumor sections (Figure 8G). These data strongly support that the co-treatment of EGFRvIII-DBTE and HER2-DBTE result in improved efficacy in controlling heterogeneous GBM tumors through reduced heterogeneous tumor escape and illustrate the non-competitive nature of this combination approach.
## Discussion
In this article, we developed a new EGFRvIII-DBTE for study in therapeutic models of GBM, exploring its anti-tumor cytotoxicity, specificity, T cell activation, in vivo pharmacokinetics, and impact in GBM challenge models. This treatment exhibited durable in vivo expression of EGFRvIII-DBTE over 15 weeks, with killing activity and continued ability to lower and clear tumor burden in all treated animals in an intracranial challenge of GBM after a single dose. We next studied a dual tumor targeting approach combining EGFRvIII-DBTE and HER2-DBTE treatments in a heterogeneous challenge model. In this combination approach, treatment with two DBTEs exhibited impressive control of heterogeneous GBM tumors and mitigated immune escape in $80\%$ of the challenged mice.
We also studied the detailed characterization of EGFRvIII in impact on tumor. The EGFRvIII-DBTE exhibited specific binding activities and cytotoxicity against EGFRvIII-expressing GBM cells (Figures 1C–1E and 2A), while not binding to wild-type EGFR (Figure S2) or inducing anti-tumor cytotoxicity in the absence of either targets, EGFRvIII or CD3 (Figures 2B and 2C). Safety and toxicity are of concern in cancer immunotherapeutic agents in development. These data support the specificity of EGFRvIII-DBTE, reducing the potential of off-target toxicity. Also, the DNA vector itself has been reported to show low intrinsic immunogenicity and high safety profile in clinical trials.20,21 We characterized the T cell responses driven by the EGFRvIII-DBTE in an activation assay against U87vIII cells and observed that EGFRvIII-DBTE induced CD69 activation in T cells (Figure 3A) and anti-tumor cytokine release in both CD8+ and CD4+ T cell populations (Figures 3E and 3F). One particularly important aspect was that CD4+ T cells also exhibited clear, but lower CD107a activation, supporting that cytolytic activities of CD4+ T cells are also induced by EGFRvIII-DBTE (Figure 3F).22 We analyzed CD4+ T cells in a tumor-killing assay and observed that EGFRvIII-DBTE induced anti-tumor cytotoxicity by isolated CD4+ T cells at an E:T ratio of 20:1 (Figures 3G and 3H). In the tumor microenvironment for immune-cold tumors like GBM, CD8+ T cells often enter an inactivated state and instead CD4+ T cell tumor infiltration is observed. A high level of CD4+ tumor-infiltrating lymphocytes (TILs) with a low level of CD8+ TILs is associated with poor prognoses, which has been reported in glioma patients.23 The role of CD4+ T cells in cancer was thought to be primarily in priming immune response for CTLs. However, recent emerging evidence suggests that under some conditions, CD4+ T cells can participate in cytolytic activities against tumors.24 Our results expand on this work demonstrating that the stimulation using EGFRvIII-DBTE can induce CD4+ T cells to participate directly to kill target GBM and likely can be an important tool to recruit effector CD4+ T cells to target these tumors.
It has been suggested that the GBM microenvironment may impose challenges in in vivo delivery due to its immunosuppressive properties and the presence of the blood-brain barrier (BBB). However, recent reports suggest that BBB is impaired in GBM, which likely allows for increased passive diffusion, and the bioavailability of the peripherally delivered drugs in the brain remains of concern.25,26 Thus, agents that offer efficacy at low concentrations are likely important for treatment of GBM. In a killing assay, we observed that EGFRvIII-DBTE induced potent cytotoxicity against U87vIII cells with a dose with EC50 of 41.5 pM (Figure 2D) and function at a low E:T ratio of 1:1 (Figure 2E). This engineered EGFRvIII-DBTE is a potent T cell engager that maintains function in the modeled GBM microenvironment studied here. We demonstrated potency in the intracranial challenge, observing that the EGFRvIII-DBTE coordinates T cells to the target and reduces tumor load in the challenge model, resulting in complete clearance of tumor in all treated animals by a single injection (Figures 6B–6D).
In in vivo pharmacokinetics study, a single injection of EGFRvIII-DBTE exerted tumor-killing activity lasting more than 15 weeks (Figure 4A). In contrast, a single injection of a recombinant EGFRvIIIxCD3 delivered peripherally exhibited cytotoxicity that lasted for just 4 days. Due to their short serum half-life, a protein-based EGFRvIII-targeted BTE required multiple doses over 16 consecutive days to exert significant tumor regression and show $72\%$ survival in an animal challenge in a recent study.14 However, in the intracranial challenge study described here, we observed that a single injection of the EGFRvIII-DBTE exhibited complete tumor clearance with $100\%$ (10 of 10 animals) survival (Figures 6B–6D), providing a simpler and potentially important additional treatment tool. It is possible that such genetic-based biologics could be dose- and cost-sparing.27 DBTEs may allow for tumor targeting and control for longer periods of time, potentially lowering treatment cost for patients.
The animal model used in which NSG mice are repopulated with primary human T cells is well established and a widely used model for development of bivalents and CAR-Ts, due to anti-drug antibody (ADA) responses in immunocompetent animal models.28,29,30 ADA response is a significant challenge for antibody therapies in immune-competent models and does appear in the clinic at a detectable level. Humanized antibodies such as Blinatumomab, an FDA-approved BTE, have lower immunogenicity than mouse antibodies in human subjects, reporting <$1\%$ ADA responses in treated patients.31,32 EGFRvIII-DBTE and HER2-DBTE described in this article are humanized antibodies, lowering the risk of ADA responses in humans. In vivo delivery by electroporation alone does not induce an ADA response, as we previously reported that a DNA/EP delivery in mice of a species-matched DNA-encoded monoclonal antibody resulted in a durable antibody expression over 10 weeks without detectable ADA responses.33 A challenge for immunotherapy for GBM cancer remains antigen heterogeneity. GBM displays various degrees of antigens such as EGFRvIII and HER2 in a heterogeneous manner.7,8 Immunotherapies such as CAR-T and peptide vaccine, which targeted EGFRvIII alone, have not yet demonstrated significant clinical benefits in GBM patients thus far.5,6 In single antigen-targeting therapies, tumor cells that are not recognized by the therapy likely use immune escape mechanisms by mutation of tumor antigen and selective survival of antigen-negative tumor subpopulations. Strategies that target multiple tumor antigens will likely help mitigate immune escape in this context. Tri-specific antibodies for dual tumor antigen targeting have been previously reported.34,35 However, these approaches used tumor models in which two antigens are homogeneously expressed and failed to address the impact of biologics in suppression of tumor escape. Here, we described a co-delivery of multiple bispecific biologics in a heterogeneous in vivo tumor model wherein NSG mice are orthotopically challenged with a mixture of EGFRvIII-expressing tumor cells (U87vIII) and HER2-expressing tumor cells (U251). We observed that sera from animals that received co-treatment of EGFRvIII-DBTE and a HER2-DBTE induced cytotoxicity against both EGFRvIII+ and HER2+ tumor populations (Figures 7A and 7B). In an intracranial heterogeneous GBM challenge, a single injection of the combined treatment of these DBTEs exerted enhanced tumor regression and improved survival as compared with single DBTE treatments or controls (Figures 8B–8F). Eighty percent of tumor-bearing mice showed persistent tumor control and clearance if administered both DBTEs at the same time. This was not observed in mice treated with single DBTEs (Figure 8F), supporting that the combined treatment with two DBTEs targeting two different antigens enhanced tumor suppression and improved morbidity in challenge. Examination of brain sections collected at the endpoints for each mouse revealed antigen escape in mice that received a single-agent therapy, but not in mice that received the combined treatment of both DBTEs (Figure 8G). The two DBTEs effectively controlled tumor growth and mitigated antigen escape in the heterogeneous GBM challenge.
In conclusion, the simplicity in production and delivery of DBTEs suggests the importance of studying combination approaches targeting multiple tumor antigens. GBM expresses many other antigens that are receiving attention, such as IL13Ra2 and EphA2. A combination approach as we illustrate here could be expanded to target additional antigens, potentially further improving tumor control and advancing patient outcomes. Broadening treatment options for GBM and other cancer patients with combination therapies, potentially providing a personalized combination of DBTEs based on antigen expression profile of each patient, deserve additional study.
## Animals and cell lines
Male and female NSG mice were obtained from the Wistar Institute Animal Facility and used in in vivo expression studies and GBM challenge models. All animals were housed at the Wistar Institute animal facilities and given free access to food and water in groups of five animals per cage. Animal experiments were approved by the Institutional Animal Care and Use Committee at The Wistar Institute (protocol 201,401).
EGFRvIII-expressing U87-MG (U87vIII) cell line was generated by sequentially transducing U87-MG tumor cells (HTB-14, ATCC) with firefly luciferase lentivirus (PLV-10003, Cellomics Technology) and virus-containing media of Phoenix-AMPHO cells (CRL-3213, ATCC) transfected with pBMN-I-GFP embedding human EGFRvIII, which was generated by Genscript. Transduced cells were sorted by GFP expression and EGFRvIII expression was validated by anti-human EGFRvIII flow antibody (NBP2-50599, Novus Biologicals). U251-luc cell line was generated by transduction of U251-MG tumor cells (09,063,001, Millipore Sigma) with firefly luciferase lentiviral vector (PLV-10003, Cellomics Technology). DK-MG cells were obtained from Amsbio (CL 01008-CLTH). Tumor cells were kept in low passage number, cultured in MEM (or RPMI1640 for DK-MG) containing $10\%$ heat-inactivated FBS and 100 U/mL penicillin/streptomycin, at 37°C in a $5\%$ CO2 incubator.
Expi293F cell line (Thermo Fisher Scientific, A14527) was used for in vitro expression studies. Expi293F cells were cultured in Expi293 expression medium (A1435101, Thermo Fisher Scientific) and kept in suspension by an orbital shaker, at 37°C in an $8\%$ CO2 incubator.
Primary human T cells were obtained from healthy donors at Human Immunology Core at University of Pennsylvania by negative selection using RosetteSep Human T cell isolation kit (Stemcell, #15061). T cells were cultured in RPMI1640 containing $10\%$ heat-inactivated FBS and 100 U/mL penicillin/streptomycin, at 37°C in a $5\%$ CO2 incubator. In GBM challenge studies, T cells were activated and expanded with T cell activation/expansion kit (130-091-441, Miltenyi Biotec) and recombinant IL-2 (130-097-745, Miltenyi), following the manufacturer’s protocol.
## Design of EGFRvIII-DBTE and HER2-DBTE
We designed EGFRvIII-DBTE by encoding a codon-optimized sequence of EGFRvIII-binding scFv17 fused with humanized CD3-binding scFv (clone UCHT-1) by a GS linker. Human IgE leader sequence was added to the N terminus of the construct. Altogether, the construct was subcloned into a modified pVAX1 expression vector. Previously described HER2-DBTE15 is composed of HER-binding scFv fused with CD3-binding scFv.
## In vitro expression of DBTEs
For in vitro expression of DBTEs, Expi293F cells were transfected with DBTE constructs by using ExpiFectamine 293 transfection kit (A14524, Thermo Fisher Scientific), following the manufacturer’s protocol. Supernatants were collected at day 5 of transfection.
## Western blot
The total protein concentration of the supernatant samples from DBTE-transfected Expi293F cells were quantified using a bicinchoninic acid assay (Pierce, Thermo Fisher Scientific). Ten micrograms of supernatant samples were loaded on a $4\%$–$12\%$ Bis-Tris SDS-PAGE gel (NuPAGE, Thermo Fisher Scientific). The gel was transferred to a PVDF membrane using the iBlot 2 system (Thermo Fisher Scientific). The membrane was blocked in Intercept (PBS) blocking buffer (Licor) and then probed with a donkey anti-human IgG H + L secondary antibody (Licor) diluted 1:15,000 in Intercept T20 (PBS) antibody diluent (Licor). The membrane was scanned with Odyssey CLx imaging system (Licor). Western blotting was performed three times.
## Quantitative ELISA
Ninety-six-well plates (Fisher) were coated with anti-human F(ab’)2 antibody (Novus Bio) and incubated overnight at 4°C. The plates were blocked in PBS, $10\%$ FBS, for 1 h and the diluted samples and standards were added for 1 h. Then they were probed with 1:5,000 anti-human F(ab’)2 antibody, horseradish peroxidase-conjugated (Jacksonimmuno Research) for 1 h. The plates were developed using TMB solution (ThermoFisher) for 10 min and stopped using 2N H2SO4 solution. The optical densities were measured at 450 nm using plate scanner (BioTek Synergy 2). The concentration of samples was determined based on the standard curve (4-parameter sigmoidal) using purified bispecific antibodies as standards. The purified bispecific antibodies were generated by Genscript by CHO transfection followed by purification using 6X HisTag at N terminus, which then was removed by protease. Quantitative ELISA experiments were performed with three replicates of each sample and standard.
## T cell-mediated cytotoxicity assay
U87vIII cells or U251-luc cells were plated on 96-well E-plate (ACEA biosciences) at 1 × 104 cells/well in 100 μL RPMI 1640 medium containing $10\%$ FBS (R10) and incubated at 37°C overnight. Pre-treatment cell viability of the target cells was monitored by xCelligence RTCA eSight machine for 18 to 20 h. Primary human T cells were rested at 37°C overnight in R10 and added to the target cells at various effector to target ratios together with DBTE-containing supernatant (10 ng/mL) or mouse serum (diluted 1:10) in a total volume of 100 μL. The cell viability was monitored with xCelligence RTCA eSight for 48 h. The cell viability of each assay well was normalized to the last cell index of pre-treatment incubation. Percent cytolysis was plotted as the percent difference of cell indices from the baseline (target cells with T cells only) at each time point. For fluorescent imaging, human CD69 antibody conjugated with Alexa Fluor 647 (FAB23591R, R&D Systems) and caspase-3 blue dye (SCT102, Millipore Sigma) were added at 10 μg/mL to the wells upon addition of effector cells. Bright field images and fluorescent (green, blue, red) images were taken with xCelligence RTCA eSight. T cell-mediated cytotoxicity assays were performed with three replicates for in vitro samples and five replicates for in vivo samples.
## Flow cytometry
U87vIII cells were plated on a 96-well plate (ThermoFisher) at 1 × 104 cells/well in 100 μL RPMI 1640 medium containing $10\%$ FBS (R10) and incubated at 37°C overnight.
Primary human T cells were rested overnight in a 37°C incubator and $5\%$ CO2 and added to the target cells together with DBTE-containing supernatant, normalized at 10 ng/mL, in a total volume of 100 μL. A 1X Protein transport inhibitor cocktail (eBioSciences, 00-4980-03) and CD107a antibody conjugated to PE-Cy7 (clone H4A3, Biolegend) diluted 1:100 were added to the wells. After 24-h incubation in a 37°C incubator and $5\%$ CO2, T cells were collected and washed with PBS. T cells were first incubated with Live/Dead viability stain (Zombie Yellow, Biolegend) diluted 1:1,000 in PBS for 10 min, and then CD4 conjugated to BV510 (clone OKT4, Biolegend) and CD8 conjugated to APC-Cy7 (clone SK1, Biolegend) diluted 1:100 in PBS with $1\%$ FBS for 30 min. Cells were then fixed and permeabilized using Cytofix/Cytoperm reagents (554,714, BD Biosciences). Further intracellular staining was performed using IFN-γ conjugated to AF700 (clone B27, Biolegend), TNF-α conjugated to AF488 (clone MAb11, Biolegend), and IL-2 conjugated to PerCP/Cy5.5 (clone MQ1-17H12, Biolegend) diluted 1:100 in Perm/Wash buffer (554,723, BD Biosciences) for 1 h. Single stain and fluorescence minus one (FMO) controls were included for gating. Samples were analyzed using a BD LSR II flow cytometer and data were analyzed using FlowJo 10 software. Boolean gating was performed on T cell populations specifically secreting IFN-γ, TNF-α, and/or IL-2. Flow cytometry experiments were performed with three replicates of each sample.
## DBTE treatment in mice
For in vivo expression studies and tumor challenge studies, mice received intramuscular injections (100 μg/site DNA plasmid) in TA muscles of EGFRvIII-DBTE, HER2-DBTE, or pVAX1 DNA plasmid co-formulated with hyaluronidase (200 U/L, Sigma Aldrich, Saint Louis, MO), followed by electroporation (IM-EP) using the CELLECTRA 3P adaptive constant current device (Inovio Pharmaceuticals, Plymouth Meeting, PA). Serum was collected longitudinally to monitor in vivo expression.
## Mouse xenograft studies
In the heterotopic GBM challenge study, five female NSG mice in each group were inoculated with GBM tumors via subcutaneous injection of U87vIII (5 × 105 cells in 100 μL of PBS) in the right flank. Tumor size was measured longitudinally with a digital caliper and the volume was calculated using the formula, V = (W2 × L)/2. When the tumor size reached 50 mm3, the mice received an intramuscular (IM) injection of DNA treatment in the TA and an i.p. injection of 1 × 107 primary human T cells in 100 μL PBS. A second dose of DNA treatment was administered 7 days later. Tumors were scanned with IVIS Spectrum following i.p. injection of in vivo-grade luciferin (Promega). The mice were euthanized when tumor size reached 2,000 mm3.
In the intracranial GBM challenge studies, five male and five female NSG mice in each group received intracranial injection of 1 × 105 tumor cells into the striatum. Mice were anesthetized with a cocktail of ketamine (Vedco, St. Joseph, MO, USA) and xylazine (Akorn Animal Health, Lake Forest, IL, USA). Skull was trepanned with a drill 1 mm posterior to the bregma and 2 mm lateral to the midline. At 2.5 mm in depth, a 2-μL injection of 1 × 105 tumor cells was inoculated over 2 min using a stereotactic frame and automatic syringe pump (Stoelting Co., Wood Dale, IL, USA). The syringe was withdrawn slowly (0.5 mm/min) and then the incision was sutured (Ethicon Inc., Somerville, NJ, USA). Mice received antibiotic ointment over the incision and a subcutaneous injection of buprenorphine analgesic. Then mice were monitored for adverse responses and weight loss. Mice that lost $20\%$ of initial weight or had symptoms of graft versus host disease were euthanized in CO2 chamber. Randomization was performed prior to DNA treatments.
## Fluorescent immunohistochemical images of murine brain sections
At endpoint of the studies, mouse brains were harvested and fixed by sequentially incubating in $10\%$ formalin (Millipore Sigma, USA), $15\%$ and $30\%$ sucrose solutions. The specimens were embedded in O.C.T. compound and frozen rapidly in dry ice. Ten-micron coronal sections of the brain specimens were performed by the Histotechnology Core at the Wistar Institute. The frozen section slides were blocked with $5\%$ normal goat serum in PBS and stained with 10 μg/mL anti-EGFRvIII murine antibody conjugated to AF647 (clone DH8.3; Novus Biologicals, USA) and 10 μg/mL anti-HER2 murine antibodies conjugated to AF555 (clone EP1045Y; Abcam, USA) and DAPI. The fluorescent confocal images of the sections were captured by the Imaging Facility at the Wistar Institute using Leica TCS SP8 confocal microscope. Section samples were blinded to the Imaging Facility.
## Statistics
The data were graphed and statistical analyses performed using GraphPad Prism 9.0 software (La Jolla, CA). Statistical comparisons included a two-way ANOVA, with correction for multiple comparisons, which compares groups within each time point (simple effects within rows). Survival data were represented by a Kaplan-Meier survival curve and significance was calculated using a log rank test between each group. In all experiments, samples with a p value < 0.05 were considered statistically significant. The line graphs represent individual animals, where indicated. Scatterplots display individual animals, the mean value, and error bars represent the standard deviation.
## Supplemental information
Document S1. Figures S1 and S2 Document S2. Article plus supplemental information
## Data availability
All data are available in the main text or supplemental information.
## Author contributions
D.H.P., A.P.P., A.P., and D.B.W. conceptualized and designed experiments. D.H.P. and A.P.P. engineered plasmid DBTE constructs. D.H.P., K.L., P.B., X.Z., J.C., D.B., E.N.G., R.P.O., A.R.A., and A.K. performed experiments and analyzed data. D.H.P. contributed substantially to writing. All authors contributed to revision of the manuscript.
## Declaration of interests
D.B.W. has received grant funding, participates in industry collaborations, has received speaking honoraria, and has received fees for consulting, including serving on scientific review committees. Remunerations received by D.B.W. include direct payments and equity/options. D.B.W. also discloses the following associations with commercial partners: Geneos (consultant/advisory board), AstraZeneca (advisory board, speaker), Inovio (board of directors, consultant), Sanofi (advisory board), BBI (advisory board), Pfizer (advisory board), Flagship (consultant), and Advaccine (consultant).
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|
---
title: Associations of atrial natriuretic peptide with measures of insulin and adipose
depots
authors:
- Catharine A. Couch
- Lauren A. Fowler
- Vibhu Parcha
- Pankaj Arora
- Barbara A. Gower
journal: Physiological Reports
year: 2023
pmcid: PMC10006508
doi: 10.14814/phy2.15625
license: CC BY 4.0
---
# Associations of atrial natriuretic peptide with measures of insulin and adipose depots
## Abstract
Low concentrations of natriuretic peptides (NPs) have been associated with greater risk for Type 2 diabetes (T2D). African American individuals (AA) have lower NP levels and are disproportionately burdened by T2D. The purpose of this study was to test the hypothesis that higher post‐challenge insulin in AA adults is associated with lower plasma N‐terminal pro‐atrial natriuretic peptide (NT‐proANP). A secondary purpose was to explore associations between NT‐proANP and adipose depots. Participants were 112 AA and European American (EA) adult men and women. Measures of insulin were obtained from an oral glucose tolerance test and hyperinsulinemic‐euglycemic glucose clamp. Total and regional adipose depots were measured from DXA and MRI. Multiple linear regression analysis was used to assess associations of NT‐proANP with measures of insulin and adipose depots. Lower NT‐proANP concentrations in AA participants was not independent of 30‐min insulin area under the curve (AUC). NT‐proANP was inversely associated with 30‐min insulin AUC in AA participants, and with fasting insulin and HOMA‐IR in EA participants. Thigh subcutaneous adipose tissue and perimuscular adipose tissue were positively associated with NT‐proANP in EA participants. Higher post‐challenge insulin may contribute to lower ANP concentrations in AA adults.
NT‐proANP is inversely associated with 30‐min insulin AUC in African American (AA) participants, and with fasting insulin and HOMA‐IR in European American (EA) participants. Higher post‐challenge insulin may contribute to lower ANP concentrations in AA adults.
## INTRODUCTION
Atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) belong to the natriuretic peptide (NP) family of hormones with cardiovascular and renal functions, playing a key role in cardiovascular homeostasis and sodium and water balance (Kone, 2001). In addition to these essential functions, NPs possess metabolic functions including stimulation of lipolysis, adipocyte browning, and modulation of adipokine secretion (Rukavina Mikusic et al., 2022). Low circulating concentrations of NPs have been observed in individuals with obesity, insulin resistance, and Type 2 diabetes (T2D) (Khan et al., 2011; Kovacova et al., 2016; Wang et al., 2004). As a group, African American (AA) individuals have previously been shown to have lower NP levels (Bajaj et al., 2018; Gupta et al., 2015a, 2015b, 2017; Patel et al., 2019) and are disproportionately burdened by T2D and other cardiometabolic diseases (Stierman et al., 2021). Insulin, which is higher in AA individuals (Gower et al., 2002; Haffner et al., 1996), has been shown to influence NP concentrations (Bachmann et al., 2018; Pivovarova et al., 2012). It remains unclear if physiological differences in insulin action and secretion among AA and EA individuals contribute to lower NP concentrations observed in AA populations.
Lower levels of NPs have consistently been demonstrated in individuals with insulin resistance (Khan et al., 2011; Olsen et al., 2005; Wang et al., 2007) and are also associated with incident T2D (Lazo et al., 2013; Magnusson et al., 2012; Sujana et al., 2021). Additional studies that investigated the acute effects of insulin on NP concentrations observed a decrease in plasma ANP concentrations in response to an infusion of insulin (Bachmann et al., 2018; Pivovarova et al., 2012). While the mechanisms responsible for the association between insulin and NPs remain unclear, it is known that insulin upregulates expression of the NP clearance receptor (NPR‐C), thereby leading to greater clearance of NPs from circulation (Kovacova et al., 2016; Parcha et al., 2021; Pivovarova et al., 2012). AA individuals tend to have lower insulin sensitivity (Gower et al., 2002; Osei & Schuster, 1994), a higher acute insulin response to glucose (Ellis et al., 2012; Haffner et al., 1996; Osei & Schuster, 1994), and lower hepatic insulin clearance (Gower et al., 2002; Osei & Schuster, 1994), all of which act to elevate circulating insulin. Therefore, it is possible that these physiological differences in insulin secretion and action, by increasing circulating insulin, contribute to the lower levels of NPs previously observed in AA individuals (Bajaj et al., 2018; Gupta et al., 2015a, 2015b, 2017).
NPs have also emerged as regulators of lipid metabolism, exerting lipolytic functions in adipose tissue (Moro et al., 2004; Sengenès et al., 2000) and promoting browning of adipocytes (Bordicchia et al., 2012). Prior research has postulated that through these metabolic functions, NPs are capable of influencing fat distribution (Neeland et al., 2013). BNP, N‐terminal pro‐BNP (NT‐proBNP), and N‐terminal pro‐ANP (NT‐proANP) have exhibited inverse associations with BMI and visceral adiposity (Cheng et al., 2011; Sugisawa et al., 2010; Wang et al., 2004). In the Dallas Heart Study, BNP and NT‐proBNP were inversely associated with visceral fat and liver fat, and positively associated with lower body fat, independent of BMI, age, sex, and race (Neeland et al., 2013). These findings suggest that NPs may be one of the many factors involved in adipose tissue distribution.
The primary aim of this study was to test the hypothesis that higher post‐challenge insulin in AA adults is associated with lower levels of NT‐proANP. A secondary aim was to explore associations between NT‐proANP and adipose depots.
## Subjects and study design
This was a secondary analysis of a cross‐sectional, observational study conducted at the University of Alabama at Birmingham (UAB), between 2013 and 2018. Participants were healthy AA and EA men and women aged 19–45 years who were recruited by public advertisement (flyers and newspaper ads). Race/ancestry was determined by self‐report, and by genetic admixture analysis as described below. Recruited individuals were screened for glucose tolerance status with a 2‐h 75 g oral glucose tolerance test (OGTT), and those with 2‐h glucose ≥200 mg/dL were excluded from participation. Other exclusion criteria were absence of regular menstrual cycle; pregnant, lactating, or postmenopausal; smoking; not weight stable (change in weight > 2.5 kg in the previous 6 months); taking oral contraceptives; use of any medication known to affect carbohydrate or lipid metabolism, or energy expenditure; and use of anti‐hypertensive agents that affect glucose tolerance (e.g., thiazide diuretics at doses >25 mg/day, angiotensin‐converting‐enzyme inhibitors). Participants were instructed to maintain their usual activity level, avoid strenuous physical activity the day prior to testing, and avoid all physical activity on the morning of testing. Women were tested 3–7 days after cessation of menstruation, while in the follicular phase of the menstrual cycle. All study assessments were conducted at the core facilities of the Center for Clinical and Translational Science (CCTS), Nutrition Obesity Research Center (NORC), and Diabetes Research Center (DRC). The UAB Institutional Review Board approved the study.
## Anthropometrics and blood pressure
Each participant underwent standard anthropometric measurements (weight and height) while wearing light clothing and no shoes. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m2). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by trained nurses in the UAB Clinical Research Unit after 10 min of seated rest.
## Oral glucose tolerance test (OGTT), insulin concentration, and insulin sensitivity
A 2‐h 75 g OGTT was completed. Participants arrived in the fasted state and venous access was obtained. Blood samples were collected at −10 min and −5 min relative to glucose load ingestion. Fasting values were calculated as the average of these two measures. A 75‐g oral glucose load was administered at time 0 and participants had 5 min to consume it. Blood samples were obtained at 10, 20, 30, 60, 90, and 120 min after glucose ingestion. Samples were processed for serum and stored at −85°C until assayed for glucose, insulin, and C‐peptide. Homeostasis model of insulin resistance (HOMA‐IR) was calculated as a measure of insulin resistance: HOMA‐IR = fasting insulinμU/mL× fasting glucosemmol/L/22.5. Matsuda index was calculated as a measure of whole‐body insulin sensitivity: Matsuda =10,000/√Gfasting×Ifasting×GOGTTmean×IOGTTmean, where fasting glucose and insulin are taken from time 0 of the OGTT and mean data represent the average glucose and insulin obtained during the entire OGTT (Matsuda & DeFronzo, 1999; Muniyappa et al., 2008). Total area under the curve (AUC) for insulin at 30 min (time 0 to 30 min) and 2‐h (time 0 to 120 min) were calculated using the trapezoidal rule (Tai, 1994).
## Hyperinsulinemic‐euglycemic glucose clamp
Skeletal muscle insulin sensitivity (indicated as SIClamp for the present study) was measured using the hyperinsulinemic‐euglycemic glucose clamp. Procedures were conducted at the UAB Clinical Research Unit following a ≥10 h fast. With the participant in a recumbent position, an intravenous catheter was placed in an antecubital vein for insulin and glucose infusion. A second catheter was placed in the contralateral arm for sequential blood sampling every 10 min to determine glucose and insulin concentrations. The insulin solution (regular Humulin, Eli Lilly & Co.) was prepared with normal saline and infused at 120 mU/m2/min (individualized to the participant's body surface area) for 3 h using an Alaris PC unit with Guardrails software (Carefusion Corp.). Blood glucose concentrations were measured at 5 min intervals using a glucose analyzer (YSI 2300 STAT Plus, YSI, Inc.), while an infusion of $20\%$ dextrose was adjusted to maintain blood glucose concentration at the participant's fasting level. Steady state for each individual was defined as a ≥30 min period that occurred ≥1 h after initiation of the insulin infusion, during which time the coefficients of variation for blood glucose, serum insulin, and glucose infusion rate were less than $5\%$. SIClamp (10−4.dL.kg−1.min−1/(μU/mL)) was defined as M/(G × ΔI), where M is steady state glucose infusion rate (mg/kg body mass/min), G is steady state serum glucose concentration (mg/dL), and ΔI is the difference between basal and steady state serum insulin concentrations (μU/mL). SIClamp was adjusted for total lean body mass, which was measured by dual‐energy X‐ray absorptiometry (DXA; iDXA instrument, GE Healthcare, Lunar) (Tay et al., 2020).
## Assays
Serum concentrations of glucose, total cholesterol, high density lipoprotein cholesterol (HDL‐C), and triglycerides were measured using a Stanbio SIRRUS analyzer (Boerne, TX). Low density lipoprotein cholesterol (LDL‐C) was calculated using the *Friedewald formula* (Friedewald et al., 1972; Sathiyakumar et al., 2020). Insulin and C‐peptide were assayed in duplicate with a TOSOH A1A‐900 immunoassay analyzer (TOSOH Corp.). For insulin, assay sensitivity was 0.5 uU/mL, inter‐assay CV was $3.95\%$, and intra‐assay CV was $1.49\%$. Minimum assay sensitivity for C‐peptide was 0.2 ng/mL, while inter‐assay CV was $6.81\%$ and intra‐assay was $1.67\%$. NT‐ProANP was measured using Human NT‐ProANP Quantikine ELISA kits (R&D Systems), with minimum assay sensitivity 0.63, intra‐assay CV $4.84\%$, and inter‐assay CV $4.10\%$.
## Body composition
Total fat mass was measured using dual‐energy X‐ray absorptiometry (DXA; iDXA instrument, GE Healthcare, Lunar). Participants were scanned in light clothing while laying supine with arms at their sides. Abdominal subcutaneous adipose tissue (SAT), thigh SAT, intra‐abdominal adipose tissue (IAAT), thigh intermuscular adipose tissue (IMAT), thigh perimuscular adipose tissue (PMAT), liver fat, and renal sinus fat (RSF) were measured with magnetic resonance imaging (MRI) (Ingenia 1.5 T wide bore MRI system; Phillips). Liver fat was assessed using the fast spin echo 2‐point Dixon technique (Ma et al., 2005). Volumes of abdominal SAT, IAAT, and RSF were assessed via transverse abdominal images obtained via 3D volumetric T1‐weighted magnetization‐prepared rapid acquisition gradient echo (MPRAGE). The echo time, repetition time, and pulse flip angles were selected to optimize the signal‐intensity contrast between the adipose and non‐adipose tissue compartments. A series of 10 mm slices spaced at 5 mm intervals from the L1‐L4/L5 vertebrae was performed for each participant. Scans were later analyzed for volume (cm3) of adipose tissue using Slice‐O‐Matic image analysis software (version 4.3: Tomovision). RSF was measured from a single slice of the right and left kidneys where the fat was most visible. As the right kidney is often positioned slightly lower in the abdomen than the left kidney, a different slice was analyzed for each kidney and volume from each slice was added together for the RSF value. Thigh skeletal muscle and adipose tissue volume were analyzed using three images from the mid‐thigh (mid‐point between the anterior iliac crest and the patella). IMAT was partitioned from PMAT by manually drawing a line around the muscle itself to capture adipose tissue located directly between and within muscle groups. Scans were not available for all participants.
## Determination of genetic admixture
Admixture analysis was performed on study participants with available DNA samples ($$n = 107$$). Filtering, quality control, and merging of genetic data were performed using PLINK (version 1.9) and the gaston package in R (Chang et al., 2015; Perdry et al., 2020; R Core Team, 2021). Study participants were genotyped with the Infinium Global Screening Array v3.0 (Illumina Inc.) at the Genomics Center at the University of Minnesota. CEU and YRI reference samples from 1000 Genomes Project Phase 3 were used to estimate European and African ancestry (respectively), while Colombian, Pima, Maya, Surui, and Karitiana reference samples from the Human Genome Diversity Project were used to estimate Native American ancestry (Auton et al., 2015; Cavalli‐Sforza, 2005). Prior to analysis, quality control was performed both separately for the reference and study datasets and on the merged datasets. Any individuals and variants meeting the following criteria were removed: [1] non‐autosomal SNPs; [2] SNPs or samples with call rate ≤$90\%$; [3] SNPs deviating from Hardy–*Weinberg equilibrium* ($p \leq 10$−7); [4] SNPs with minor allele frequency ≤ 0.01; and [5] first‐degree relatives. Supervised admixture analysis was performed with ADMIXTURE version 1.3.0 (Alexander et al., 2009). The analysis was conducted with $K = 3$ clusters to infer ancestry fractions for individuals in the study dataset via comparison with African, European, and Native American reference populations. In AA individuals, mean African admixture was $84.64\%$ and ranged from $67.25\%$ to $99.99\%$. In EA individuals, mean African admixture was $0.59\%$ and ranged from $0\%$ to $6.03\%$. Since DNA data were missing for nine individuals, models were adjusted for self‐reported race rather than admixture to maximize sample size. In those participants with genetic admixture data, it was confirmed that results were similar regardless of whether self‐reported race or admixture was used as a covariate.
## Statistical analyses
Group differences in descriptive characteristics were assessed by chi‐square test for categorical variables and an independent samples t‐test for continuous variables. Differences in NT‐proANP by self‐reported race were evaluated by analysis of covariance (ANCOVA) while adjusting for age, sex, BMI, and 30‐min insulin AUC. Multiple linear regression analysis was used to investigate associations of NT‐proANP with measures of insulin concentration and sensitivity (fasting insulin, 30‐min and 2‐h insulin AUC, HOMA‐IR, Matsuda index, and SIClamp) with age, sex, and BMI included as covariates. ANCOVA was used to evaluate differences in adipose depots by self‐reported race while adjusting for total fat mass (abdominal SAT, thigh SAT, IAAT, liver fat, and RSF) or total fat mass and thigh skeletal muscle (IMAT and PMAT). Associations of NT‐proANP with adipose depots (abdominal SAT, thigh SAT, IAAT, liver fat, RSF, IMAT, and PMAT) were evaluated with multiple linear regression analysis, with total fat mass, age, and sex included as covariates. Models for IMAT and PMAT were additionally adjusted for thigh skeletal muscle. For linear regression models, linear relationships between the outcomes and continuous variables were confirmed with scatterplots. Residual normality was verified with histograms and QQ plots, and residual plots were examined to confirm homoscedasticity. Residuals ±3 standard deviations were considered outliers and were excluded. No evidence of multicollinearity was observed in any of the models (all variance inflation factors <5). Assumptions for all other statistical tests (data normality and equal variances) were verified prior to analysis. Any non‐normally distributed variables, including NT‐proANP, were log transformed to achieve a normal distribution. Analyses were conducted with RStudio Statistical Software (R Core Team, 2022, v4.1.3). Statistical tests were two‐tailed with significance set at $p \leq 0.05.$
## RESULTS
A total of $$n = 112$$ individuals were included in the current analysis. Descriptive characteristics are presented in Table 1 by self‐reported race. The study sample was $48.2\%$ male, with a mean ± SD age of 29.21 ± 8.06 years, and mean BMI of 27.41 ± 5.6. The median ($95\%$ CI) NT‐proANP concentration for the entire sample was 6.3 ng/mL (5.56, 7.11). BMI was significantly higher in AA participants; however, total body fat mass did not differ between the two groups. SBP was significantly higher in AA participants, while EA participants had significantly higher triglycerides. AA participants were less insulin sensitive (SIClamp and Matsuda index) and had greater 30‐min and 2‐h insulin AUC.
**TABLE 1**
| Variable | African American n = 54 | European American n = 58 | p‐value* |
| --- | --- | --- | --- |
| Age, years | 30.33 ± 8.27 | 28.17 ± 7.79 | 0.16 |
| Male/female, n | 24/30 | 30/28 | 0.44 |
| African admixture, % | 84.63 ± 6.36 | 0.6 ± 1.12 | <0.0001 |
| BMI, kg/m2 | 29.21 ± 5.91 | 25.73 ± 4.76 | <0.001 |
| Total fat mass, kg | 28.69 ± 12.17 | 24.45 ± 10.7 | 0.05 |
| Total lean mass, kg | 52.93 ± 11.3 | 48.97 ± 9.2 | <0.05 |
| SBP, mmHg | 120.6 ± 11.4 | 114.2 ± 10.77 | <0.01 |
| DBP, mmHg | 69.15 ± 9.28 | 67.69 ± 7.9 | 0.37 |
| NT‐proANP, ng/mL | 6.04 (5.3, 6.88) | 6.76 (6.01, 7.6) | 0.2 |
| Cholesterol, mg/dL | 168.67 (160.19, 177.6) | 166.57 (157.69, 175.95) | 0.74 |
| Triglycerides, mg/dL | 64.59 (56.48, 73.87) | 80.64 (70.95, 91.66) | <0.05 |
| LDL‐C, mg/dL | 94.8 (87.4, 111) | 85.2 (77.6, 95) | 0.18 |
| HDL‐C, mg/dL | 57.3 ± 10.4 | 60.2 ± 12.1 | 0.18 |
| SIClamp, [10−4 min−1/(μIU/ml)] | 4.53 ± 1.51 | 7.18 ± 1.59 | <0.0001 |
| HOMA‐IR | 1.55 ± 1.74 | 1.27 ± 1.89 | 0.13 |
| Matsuda index | 4.89 ± 1.67 | 6.26 ± 1.85 | <0.05 |
| Fasting insulin, μIU/mL | 6.97 ± 1.7 | 5.88 ± 1.79 | 0.11 |
| 30‐min Insulin AUC, μIU/mL | 1077.8 (911.45, 1274.51) | 822.44 (700.5, 965.57) | <0.05 |
| 2‐h Insulin AUC, μIU/mL | 7584.9 (6588.39, 8732.15) | 5632.13 (4803.34, 6603.91) | <0.01 |
| Fasting glucose, mg/dL | 88 ± 8.08 | 87.5 ± 9.54 | 0.75 |
| 30‐min Glucose AUC, mg/dL | 3083.88 (2971.34, 3200.68) | 3199.18 (3090.36, 3311.84) | 0.15 |
| 2‐h Glucose AUC, mg/dL | 13515.94 (12822.7, 14246.66) | 13747.13 (13067.42, 14462.19) | 0.64 |
Figure 1 shows a scatterplot of NT‐proANP concentrations and 30‐min insulin AUC, as well as ANCOVA results for differences in NT‐proANP concentrations by self‐reported race. AA participants had significantly lower NT‐proANP concentrations compared to EA participants independent of age, sex, and BMI (5.75, $95\%$ CI [5.16, 6.42] vs. 6.96, $95\%$ CI [6.23, 7.69] ng/mL in AA and EA, respectively; $$p \leq 0.02$$; Figure 1). Upon further adjustment for 30‐min insulin AUC, this difference was no longer significant (5.93, $95\%$ CI [5.26, 6.55] vs. 6.9, $95\%$ CI [6.17, 7.61] ng/mL in AA and EA, respectively; $$p \leq 0.07$$).
**FIGURE 1:** *(a) NT‐proANP concentrations by self‐reported race. Data are presented as adjusted means and 95% confidence intervals. Model 1 is adjusted for age, sex, and BMI. Model 2 is adjusted for all covariates in Model 1 plus 30‐min insulin AUC. *p < 0.05. (b) Scatterplot of NT‐proANP concentrations and 30‐min insulin AUC in AA and EA participants. Data are adjusted for age, sex, and BMI. AA is African American, EA is European American.*
Serum glucose and insulin responses to the OGTT are shown in Figure 2, and regression results for associations between NT‐proANP and insulin measures are reported in Table 2. In AA participants, only 30‐min insulin AUC was inversely associated with NT‐proANP concentrations in unadjusted and adjusted models. In EA participants, fasting insulin and HOMA‐IR were inversely associated with NT‐proANP concentrations in unadjusted and adjusted models. SIClamp was positively associated with NT‐proANP concentrations in EA participants; however, this association was attenuated upon further adjustment for age, sex, and BMI.
**FIGURE 2:** *Serum glucose and insulin response to oral glucose tolerance test in African American and European American participants. Means ± SE shown. AA is African American, EA is European American.* TABLE_PLACEHOLDER:TABLE 2 Adipose depots by self‐reported race and regression results for associations between NT‐proANP and adipose depots are shown in Tables 3 and 4. EA participants had greater IAAT volume than AA participants, and AA participants had greater IMAT and PMAT volume than EA participants. In AA participants, RSF, IMAT, and PMAT were positively associated with NT‐proANP concentrations; however, none of these associations were independent of total fat mass, age, and sex. In EA adults, thigh SAT and PMAT were positively associated with NT‐proANP concentrations independent of total fat, but not age and sex. Total fat mass was not associated with NT‐proANP in either AA or EA participants (results not shown).
## DISCUSSION
In the present study, we sought to test the hypothesis that higher post‐challenge insulin in AA individuals would contribute to lower NT‐proANP concentrations. We observed that lower NT‐proANP concentrations in AA participants were not independent of 30‐min insulin AUC, suggesting that higher post‐challenge insulin in AA individuals likely contributes to the lower ANP observed in this population. We additionally found differential relationships of NT‐proANP with measures of insulin concentration and insulin sensitivity/resistance among AA and EA participants, suggesting that higher fasting insulin, secondary to insulin resistance, contributes to lower ANP in EA individuals. Secondary findings indicated differential associations of NT‐proANP with adipose depots in AA and EA participants, however, these associations were not independent of age and sex, suggesting that previously reported observations of associations between NPs and adipose depots were likely mediated by other factors.
Similar to previous studies, we observed AA participants to have lower NT‐proANP concentrations than EA participants (Patel et al., 2019). While it remains unclear why AA individuals have lower NP levels, it has been postulated to be partly due to differences in gene expression for enzymes involved in the processing, clearance, and regulation of NPs (Patel et al., 2019). Despite AA individuals having lower insulin sensitivity (Gower et al., 2002; Osei & Schuster, 1994), slower hepatic insulin clearance (Gower et al., 2002; Osei & Schuster, 1994), and higher acute insulin response to glucose (Ellis et al., 2012; Haffner et al., 1996; Osei & Schuster, 1994), no previous study to our knowledge has investigated the possibility that these physiological characteristics of insulin metabolism could contribute to the differences observed in NP concentrations among AA and EA individuals. We found that upon the adjustment of NT‐proANP levels for 30‐min insulin AUC, NT‐proANP levels were no longer significantly different between AA and EA participants, suggesting that the elevated acute insulin response to glucose in AA individuals contributes to the lower ANP concentrations observed in this population. NPs exert their metabolic effects by binding and activating the NP receptor A (NPR‐A) and are cleared by the NP receptor C (NPR‐C) via receptor‐mediated internalization (Kone, 2001). Insulin has been shown to downregulate NPR‐A and upregulate NPR‐C expression, with the NPR‐A‐to‐NPR‐C ratio as a major determinant of NP bioactivity (Collins, 2014; Kovacova et al., 2016). It is possible that the elevated post‐challenge insulin response in AA participants results in a lower NPR‐A‐to‐NPR‐C ratio, contributing to lower ANP concentrations. While the adjustment of NT‐proANP levels for 30‐min insulin AUC only modestly attenuated the difference between AA and EA participants, the long‐term effect of an exaggerated acute insulin response to glucose on ANP concentrations in AA individuals, and potential chronic disease risk, warrants further investigation.
Low levels of NPs are associated with insulin resistance and T2D development (Jujić et al., 2016; Khan et al., 2011; Sujana et al., 2021; Walford et al., 2014; Wang et al., 2007). Many of the studies demonstrating these associations were conducted in predominantly White populations (Jujić et al., 2016; Khan et al., 2011; Wang et al., 2007), with few including other racial/ethnic groups (Gupta et al., 2020; Walford et al., 2014), making it difficult to extend these findings to other populations. In the present study, we demonstrate unique associations between various measures of insulin and NT‐proANP in AA and EA adults. We observed NT‐proANP to be inversely associated with 30‐min insulin AUC in AA participants, potentially strengthening the observation that the acute insulin response to glucose in AA individuals contributes to racial differences in observed in NP concentrations. In EA participants, HOMA‐IR (a measure of hepatic insulin resistance) and fasting insulin were inversely associated with NT‐proANP, and SIClamp (a measure of skeletal muscle insulin sensitivity) was positively associated with NT‐proANP. It is interesting that fasting insulin was inversely associated with NT‐proANP only in EA participants. This is most likely because fasting insulin is known to be associated with insulin sensitivity, and we also observed that insulin sensitivity was associated with NT‐proANP in EA participants, but not AA participants. A recent study in the Diabetes Prevention Program (DPP) was the first to investigate whether racial differences in NPs persist over time or change in response to an intervention targeting cardiometabolic health. Results indicated that AA individuals had the lowest NT‐proBNP concentrations at baseline, and that after 2 years of follow‐up NT‐proBNP concentrations decreased only in the AA group in all study arms (placebo, lifestyle intervention, and metformin) (Gupta et al., 2020). Interventions in the DPP targeted glucose control, rather than insulin response and beta‐cell function, which could be why NP levels continued to decline in the AA group. It is also possible that differences in gene expression for enzymes involved in the processing, clearance, and regulation of NPs contributed to the continual decline of NT‐proBNP levels in the AA individuals (Patel et al., 2019). Nonetheless, the race‐specific associations observed in the present analysis indicate that while insulin acts to lower ANP in both AA and EA individuals, the mechanism behind the elevated insulin differs. In AA individuals, it is the elevated post‐challenge insulin response and in EA individuals it is insulin resistance resulting in high fasting insulin.
NPs have a metabolic role in human adipose tissue via lipolytic actions (Bordicchia et al., 2012; Collins, 2014). Because of this, some investigators have suggested that NPs might affect adipose tissue distribution. Only a handful of studies have investigated the relationship between NP concentrations and adipose tissue distribution, finding NP concentrations (NT‐proBNP and BNP) to be inversely associated with visceral fat and liver fat, and positively associated with lower body fat (Cheng et al., 2011; Johansen et al., 2019; Neeland et al., 2013; Sugisawa et al., 2010). These findings, along with the knowledge of NP receptors in human visceral and subcutaneous adipose tissue (Pivovarova et al., 2012), have led prior investigators to postulate that NPs influence adipose tissue distribution (Neeland et al., 2013). Results from the present study may suggest otherwise. Our results could be interpreted to suggest that insulin resistance, rather than ANP, is related to fat distribution, and that the elevated insulin that accompanies insulin resistance leads to a reduction in ANP. In EA participants we observed NT‐proANP to be inversely associated with insulin resistance and fasting insulin and positively associated with thigh SAT and PMAT. Peripheral fat expansion is positively associated with insulin sensitivity, whereas ectopic fat accumulation is associated with insulin resistance (Goedecke et al., 2009; Goss & Gower, 2012; Kelley et al., 2000). Therefore, it may be surmised that the positive association we observed of NT‐proANP with thigh SAT and PMAT suggests that greater insulin sensitivity, as reflected in greater peripheral adipose tissue and lower fasting insulin, underlies the previously observed relationship between adipose tissue distribution and NPs. Additionally, insulin resistance has been shown to have a genetic basis that relates to the inability to expand subcutaneous adipose tissue (Lotta et al., 2017). *This* genetic insulin resistance may explain the associations observed among NT‐proANP, insulin resistance and fasting insulin, and thigh SAT and PMAT in EA participants. Insulin resistance likely leads to elevated fasting insulin which upregulates NPR‐C and results in increased NP clearance (Kovacova et al., 2016; Pivovarova et al., 2012). Therefore, the most plausible explanation for the associations observed in the present study and previous studies, is that the association between NPs and adipose depots is driven by insulin (Figure 3a). However, the associations in our study between NT‐proANP and adipose depots were not independent of age and sex. Age and sex are known to influence insulin resistance, NP concentrations, and fat distribution, and our sample was relatively young and healthy (no T2D) compared to other studies (Cheng et al., 2011; Johansen et al., 2019; Neeland et al., 2013; Sugisawa et al., 2010). Larger studies are needed to investigate the mediating effect of insulin on the association between fat distribution and NPs, particularly in older populations with metabolic disease.
**FIGURE 3:** *Proposed relationships among ANP, insulin, and adipose tissue in AA and EA individuals. (a) In AA adults, the elevated post‐challenge insulin response leads to an increase in NPR‐C expression resulting in increased ANP clearance and lower circulating ANP concentrations. (b) In EA adults, insulin resistance contributes to inappropriate adipose tissue expansion and ectopic fat accumulation, which leads to elevated fasting insulin. High fasting insulin increases NPR‐C expression resulting in increased ANP clearance and lower circulating ANP concentrations.*
Strengths of the present study include rigorous measures of insulin sensitivity, DXA‐ and MRI‐derived measures of adipose depots, and a diverse sample. Limitations of the present study must be taken into consideration. Most importantly, as a cross‐sectional study, causality cannot be inferred. Other limitations include the secondary analysis and sample size. We also did not measure the mature peptide (ANP), which is thought to have a different clearance mechanism than the N‐terminal NP (Potter, 2011), or NP receptor expression (NPR‐A and NPR‐C). However, N‐terminal NPs and mature NPs have been demonstrated to be highly correlated (Austin et al., 2006), and thus lower NT‐proANP concentrations likely indicates lower ANP concentrations. Further, NT‐proANP was measured using fasting samples. Additional measurements of NT‐proANP following glucose challenge would have allowed for the assessment of potential associations between NT‐proANP and corresponding insulin levels. Future work will be needed to investigate the relationship between ANP and insulin over the entire course of an OGTT in AA and EA individuals. Possibly one of the greatest limitations of this work is the lack of sociocultural and environmental factors, which influence race disparities in chronic disease. It is likely that the basis of the differences observed here are due in part to the plethora of sociocultural and environmental factors that contribute to health disparities. It is also possible that these differences could be genetic or even epigenetic (potentially from sociocultural/environmental factors). Future work will be vital in identifying and understanding how all relevant factors that play a role in health disparities (i.e., genetic, epigenetic, physiological, and sociocultural) interact to contribute to differences in NT‐proANP concentrations and disease risk.
In conclusion, our results suggest that the lower NT‐proANP observed in AA individuals is related to a higher post‐challenge insulin response (Figure 3a). Insulin resistance, which is rooted in fat distribution, likely contributes to lower NT‐proANP in EA individuals via elevated fasting insulin (Figure 3b). Future studies will be needed to investigate the mediating effect of insulin on the relationship between NPs and fat distribution, and the implications of these findings for chronic disease risk in diverse populations.
## AUTHOR CONTRIBUTIONS
Catharine A. Couch and Barbara A. Gower: analysis and interpretation of data, writing – original draft; Barbara A. Gower: study design and funding acquisition; Lauren A. Fowler: genetic admixture analysis; Pankaj Arora and Vibhu Parcha: writing – review and editing. All authors reviewed and edited the manuscript and approved the final version. Catharine A. Couch and Barbara A. Gower are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
## FUNDING INFORMATION
This study was supported by the National Institute of Diabetes and Digestive and Kidney Diseases at the National Institutes of Health (R01DK096388), UAB Nutrition Obesity Research Center (P30DK56336), UAB Diabetes Research Center (P30DK079626), and UAB CCTS Pilot Grant. CAC was supported by award number T32HL105349 by the National Heart, Lung, and Blood Institute of the National Institutes of Health.
## CONFLICT OF INTEREST STATEMENT
The authors have no conflict of interest to declare.
## ETHICS STATEMENT
This study was approved by the UAB Institutional Review Board.
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|
---
title: Clinical factors influencing knowledge and self‐care practice among adults
with type 2 diabetes mellitus
authors:
- Richard Adongo Afaya
- Victoria Bam
- Alberta Yemotsoo Lomotey
- Agani Afaya
journal: Nursing Open
year: 2022
pmcid: PMC10006582
doi: 10.1002/nop2.1506
license: CC BY 4.0
---
# Clinical factors influencing knowledge and self‐care practice among adults with type 2 diabetes mellitus
## Abstract
### Aim
The objective of the study was to determine the clinical factors associated with knowledge and self‐care practice among adults living with type 2 diabetes mellitus.
### Design
Descriptive cross‐sectional design.
### Methods
A convenience sample of 330 participants was recruited over 3‐months in 2018 and data were collected using a structured instrument.
### Results
Participants on insulin treatment modality had four times higher odds of knowledge on diabetes ($B = 4.17$, $$p \leq 0.023$$) while those on combined therapy (both oral hypoglycaemic agent and insulin) had 7.26 times higher odds of knowledge ($B = 7.26$, $p \leq 0.001$). Participants without medically confirmed diabetic complications had 3.66 higher odds of knowledge of diabetes ($B = 3.66$, $$p \leq 0.002$$). Participants on insulin treatment modality had a 1.4‐fold higher odds of self‐care practice ($B = 1.4$, $$p \leq 0.028$$). It was revealed that participants with hypertension and diabetic foot had lower odds of self‐care practice (B = −1.13, $$p \leq 0.021$$).
### Conclusion
In particular, participants who were on insulin and combined therapy (tablet and insulin) had higher knowledge and better self‐care practice. Self‐care was significantly influenced among those with, than those without diabetic foot and hypertension as complications.
## INTRODUCTION
The increasing prevalence of diabetes mellitus (DM) imposes a huge economic burden all over the world, particularly in sub‐Saharan Africa where there are limited resources to manage the disease and its impact (Herrington et al., 2018; Islam et al., 2015). In 2017, it was estimated that 425 million people had diabetes worldwide with a corresponding global prevalence of $8.8\%$ (International Diabetes Federation Report [IDF], 2017). The rates of DM have risen. An overwhelming $80\%$ reside in low‐ and middle‐income countries (International Diabetes Federation, 2013) that are not well equipped to tackle this emerging catastrophe (International Diabetes Federation, 2013; Islam et al., 2015; Sarfo‐Kantanka et al., 2016). In sub‐Saharan Africa including Ghana, type 2 diabetes mellitus (T2DM) accounts for most cases. The International Diabetes Federation's report indicated that Ghana had a raw national prevalence of $3.6\%$ in 2017 (IDF, 2017). Diabetes prevalence studies done in Ghana vary across different regions ranging from $6.2\%$ to $9.2\%$ (Asamoah‐Boaheng et al., 2019; Cook‐Huynh et al., 2012). A recent comprehensive systematic review and meta‐analysis established a high overall diabetes prevalence rate of $6.4\%$ among the Ghanaian adult population (Asamoah‐Boaheng et al., 2019). The surge in the prevalence of T2DM is alarming as *Ghana is* faced with inadequate healthcare facilities, an inadequate number of skilled health personnel and limited resources, and all these significantly affect the healthcare system and the economy (Mogre et al., 2017; World Health Organization, 2016).
Uncontrolled T2DM is associated with many irreversible complications affecting both micro‐vascular (nephropathy, retinopathy, and neuropathy) and macro‐vascular (heart diseases) systems of the body (Karaoui et al., 2018). These complications have led to increased disability, high mortality rates, decreased quality of life and severe economic burden for every society (IDF, 2015).
Having adequate knowledge of T2DM is very crucial in diabetes management. In order to reach optimal glycaemic targets and attenuate complications, people with T2DM should have adequate knowledge of diabetes to enhance adherence to self‐care activities such as diet, exercise, self‐monitoring of blood glucose, foot care and taking of medication (Gautam et al., 2015; Kassahun et al., 2016). Self‐care is the most important facet of diabetes management and studies have shown that it improves glycaemic control, reduces healthcare expenditure, prevents complications and enhances quality of life (Iregbu & Iregbu, 2016; Jannoo & Mamode Khan, 2018). However, for people with T2DM, self‐care which is made up of $98\%$ of diabetes management is very demanding, multifaceted and intricate (Chali et al., 2018; Jannoo & Mamode Khan, 2018). Good self‐care practice is required to keep the disease under control (Abate et al., 2018) and evidence on the knowledge and self‐care practice of adults with T2DM will provide vital insight for developing well‐targeted preventive strategies to reduce the burden of diabetes (Herath et al., 2017; Karaoui et al., 2018).
Various studies have found clinical factors such as duration of diabetes (Alhaik et al., 2019; D'Souza et al., 2017) and treatment type (Abate et al., 2018; Alhaik et al., 2019) to have an influence on knowledge and self‐care practice. Moreover, patients who have suffered from complications such as amputation, impaired vision, may have challenges with self‐care (Sparring et al., 2013). Healthcare providers' identification of factors associated with knowledge and self‐care practice in people with T2DM will inform decisions regarding specific areas and populations of patients that will require specific attention and support. Though studies have been conducted on diabetes knowledge and practice in developing countries (Karaoui et al., 2018), the available literature focusing on clinical factors associated with knowledge and self‐care practice of diabetes is limited. It was against this background that we sought to determine clinical factors influencing knowledge and self‐care practice of adults with T2DM in government hospitals in Tamale, Northern Ghana. Thus, the research question is, what are the clinical factors associated with knowledge and self‐care practice among adults living with T2DM in northern Ghana?
## Objectives of the study
To determine clinical factors influencing knowledge and self‐care practice. To evaluate the association between diabetic complications and self‐care practice.
## Study design and setting
This was a multicentre analytical cross‐sectional study conducted at three government hospitals in Tamale, the capital of the northern region of Ghana. The *Tamale metropolis* has a total population of 233,252 with $80.8\%$ of the inhabitants being urban dwellers (Ghana Statistical Service [GSS], 2014). People from all parts of Ghana are living and working in the city. The hospitals have diabetes clinics providing routine outpatient services for persons with diabetes from both urban and rural settings. The clinics are largely patronized by urban dwellers.
## Study population
All persons with T2DM who presented to the diabetes clinics of the selected hospitals for routine visits constituted the target population for the study. The patients were eligible to participate if they were aged 30 years or more, had T2DM for at least 1 year and were registered with the particular hospital. Type 1 diabetes mellitus patients and those with some form of mental incapacity were excluded from the study.
## Outcome measures
The main outcome variables in this study were diabetes knowledge and self‐care practice. Knowledge was assessed with the Diabetes knowledge test developed by scholars from the University of Michigan (Fitzgerald et al., 1998). Self‐care practice was measured using the revised version of the Summary of Diabetes Self‐Care Activities (SDSCA) questionnaire (Toobert et al., 2000).
## Explanatory variables
Clinical characteristics including family history, blood pressure, body mass index, waist circumference, smoking, duration of diabetes and diabetic complication were the main explanatory variables. Family history of diabetes was assessed using a question; ‘Has any of your family member (mother or father) had diabetes?’ Participants were also asked if they had ever smoked in their lifetime. Participants were asked whether they had diabetic complications and this was subsequently confirmed by chart review. Socio‐demographic characteristics (age, gender, education status, occupation, marital status, religion and family support) were excluded as explanatory variables.
## Sampling and sample size determination
Non‐probability convenience sampling technique was employed to recruit 360 participants from September to November of 2018. The sample size for the survey was determined using the *Yamane formula* for sample size calculation (Yamane, 1967).
Using an estimated population of 1800 cases based on the institutions' registry, a sample size of 327 participants was needed for this study. The possibility of making a type I error was estimated at $5\%$ with a $95\%$ confidence interval. A $10\%$ non‐response rate was calculated and added to the required sample size, thus, increasing it to 360.
## Study instrument
The study instrument was a structured questionnaire and consisted of four sections (A, B, C and D). The first section (Section A) had demographic characteristics including age, sex, marital status, level of income, level of education, occupation, place of residence and family history of diabetes. Section B consisted of clinical characteristics such as duration of diabetes, treatment type, family history, weight, height, body mass index and blood pressure.
Section C assessed diabetes‐related knowledge among participants using the Diabetes Knowledge Test (DKT) (Fitzgerald et al., 1998). This is a 23‐item tool with questions related to diagnosis, signs and symptoms, causes, risk factors, prevention and complications of diabetes. Each question had one correct answer from two answer choices. The answer choices for each question were ‘Yes’ and ‘No’. The internal consistency measured by Cronbach's alpha for the items of the knowledge was 0.69 in the present study.
Section D consisted of respondents' practice of self‐management activities. The patients' self‐care practices were measured by the revised version of the Summary of Diabetes Self‐Care Activities (SDSCA) questionnaire (Toobert et al., 2000). The SDSCA is a valid and reliable tool popularly used in diabetes management with a reliability of 0.74 (Vincent et al., 2008). It was used to assess respondents' self‐reported frequency of adherence to self‐care practice. The SDSCA had five important subscales including diet (general and specific), exercise, self‐monitoring of blood glucose, foot care and not smoking cigarettes. Respondents were required to rate the number of days they performed a specific self‐management practice during the last 7 days. The scale ranged from 0 to 7 with greater scores corresponding to better self‐care.
## Data collection procedure
Three registered nurses were trained as research assistants to ensure uniformity in the administration of the questionnaire. All research assistants were trained on the study protocol, the aim and objectives of the study, how to take anthropometric measures and blood pressure, how to approach and recruit participants and how to translate questions to participants during the data collection without revealing answers. Data were collected at the outpatient diabetes clinics weekly on days scheduled by the health facilities to provide routine care to patients. They approached patients who presented to the health facilities for their routine clinic visits and invited them to participate in the study. All patients who volunteered to take part in the study were screened for eligibility. The purpose of the study was explained and written informed consent was obtained from participants before questionnaire administration. The questionnaires were paper‐based and self‐administered to participants who were able to read and write in English. For those who were unable to read or write in English, trained research assistants assisted them to complete the questionnaire by translating the questions into their respective local dialects verbally. It took an average of about 30 min to complete a questionnaire.
## Anthropometric measures
The patients' weights were taken with the patients wearing light clothing and without their shoes/sandals using an electronic scale produced by Seca. Height was also taken and body mass index (BMI) was calculated. Weight was measured to the nearest kilogram and height in centimetres. In calculating BMI, height was converted to metres. The BMI was computed in accordance with standard guidelines by WHO and categorized as underweight, normal weight, overweight and obese (WHO, 2000).
Waist circumference (WC) was assessed with the patients in an upright position and measurement was taken midway between the inferior angle of the ribs and the supra‐iliac crest (WHO, 2008) to the nearest 1 cm using a non‐stretchable fibre‐glass measuring tape (Butterfly, China). Abdominal obesity was measured as a waist circumference >102 cm in men and >88 cm in women based on the WHO guidelines (WHO, 2008).
## Data analysis
Out of the 360 participants that were invited, 330 of them consented to participate, resulting in $91.7\%$ response rate. The 30 who declined participation expressed their disinterest in the study. The research assistants glanced through all sections of the questionnaires to ensure their completeness. Subsequently, there were no missing data in the collected questionnaires. All statistical analysis was done with the Statistical Package for Social Sciences (SPSS) version 25.0. Descriptive statistics were used to describe the demographic and clinical characteristics of the respondents. Multivariable logistic regression was done to predict the influencing clinical factors of knowledge and self‐care practice. Statistical significance was considered at a p value of less than 0.05.
## Reliability and validity
The opinion of six experts with extensive experiences in the disciplines of nursing and medicine was sought to establish the face and content validity of the items. The experts assessed the face and content validity of each item on the questionnaires by evaluating their clarity, comprehensibility, relevance, simplicity and grammatical construction. After the validation process, a pretest was done among 25 participants whose data were not included in the analysis. The *Cronbach alpha* coefficient for the instrument was 0.78, showing good internal consistency.
## Ethical considerations
Approval for this study was granted by the Committee on Human Research, Publication and Ethics of the Kwame Nkrumah University of Science and Technology/Komfo Anokye Teaching Hospital (CHRPE/AP/$\frac{576}{18}$). Permission was sought from the heads of the hospitals to conduct the study in their facilities. The purpose and objectives of the study were explained to respondents and written informed consent was obtained before data collection. The patients were informed that participation is voluntary and that they can opt‐out of the study at any time without any consequences to them. Respondents were assured of confidentiality and that no personal identifiers will be used in the questionnaire.
## Socio‐demographic characteristics of participants
Table 1 shows the socio‐demographic characteristics of participants. The mean age was 57.5 ± 11.8 (range = 30–91) years and females formed about two‐thirds, 225 ($68.2\%$) of the study population. More than half, 200 ($60.6\%$) of the respondents had no formal education and 143 ($43.3\%$) of them were self‐employed while 106 ($32.1\%$) were unemployed.
**TABLE 1**
| Variable | Frequency (%) |
| --- | --- |
| Age | Age |
| Mean age + SD | 57.5 + 11.8 |
| Gender | Gender |
| Male | 105 (31.8) |
| Female | 225 (68.2) |
| Education | Education |
| Tertiary | 50 (15.1) |
| Senior high school | 33 (10.0) |
| Junior high school | 24 (7.3) |
| Primary school | 23 (7.0) |
| No formal education | 200 (60.6) |
| Occupation | Occupation |
| Private sector employment | 25 (7.6) |
| Public sector employment | 56 (17.0) |
| Self‐employment | 143 (43.3) |
| No employment | 106 (32.1) |
| Marital status | Marital status |
| Single | 16 (4.8) |
| Married | 223 (67.6) |
| Divorced | 29 (8.8) |
| Widowed | 62 (18.8) |
| Family support | Family support |
| Yes | 265 (80.3) |
| No | 65 (19.7) |
| Religion | Religion |
| Christian | 84 (25.5) |
| Muslim | 246 (74.5) |
## Clinical characteristics of participants
Table 2 shows the clinical characteristics of respondents. Almost three‐quarters, 247 ($74.8\%$) of the patients were on oral hypoglycaemic treatment while a tenth, 33 ($10\%$) were taking insulin injections and the remaining 50 ($15.2\%$) were on both oral hypoglycaemic agents and insulin. A large proportion, 130 ($39.4\%$) had medically confirmed diabetic complications including hypertension, foot ulcer, retinopathy, kidney disease and neuropathy. The mean BMI was 26.4 ± 6.3 and a substantial proportion was not within the normal weight range. Thus, obese, overweight and underweight were 81 ($24.6\%$), 107 ($32.4\%$) and 25 ($7.6\%$) respectively. About two‐thirds, 227 ($68.8\%$) of the respondents were abdominally obese.
**TABLE 2**
| Characteristics | Frequency (%) |
| --- | --- |
| Family History of diabetes | Family History of diabetes |
| Yes | 130 (39.4) |
| No | 130 (39.4) |
| Do not Know | 70 (21.2) |
| Duration of Diabetes | Duration of Diabetes |
| 1–3 years | 126 (38.2) |
| 4–6 years | 99 (30.0) |
| 7–9 years | 45 (13.6) |
| 10+ years | 60 (18.2) |
| Type of treatment | Type of treatment |
| Oral hypoglycaemic | 247 (74.8) |
| Insulin | 33 (10.0) |
| Both oral and Insulin | 50 (15.2) |
| Medically confirmed diabetic complications | Medically confirmed diabetic complications |
| Yes | 130 (39.4) |
| No | 193 (58.5) |
| Not sure | 7 (2.1) |
| Body Mass Index (BMI) | Body Mass Index (BMI) |
| Mean BMI + SD | 26.4 + 6.3 |
| Obese | 81 (24.6) |
| Overweight | 107 (32.4) |
| Normal weight | 117 (35.4) |
| Underweight | 25 (7.6) |
| Waist Circumference | Waist Circumference |
| Mean WC + SD | Mean WC + SD |
| Abdominally obese | 227 (68.8) |
| No abdominal obesity | 103 (31.2) |
## Association between clinical characteristics and knowledge of participants
In Table 3, multivariable regression analysis was used and achieved an adjusted R2 of 0.22, $p \leq 0.001.$ It was revealed that a 1‐year increase in the years of visits to diabetic clinic would increase the level of knowledge of diabetes patients by 2.28 keeping all other variables constant ($B = 2.28$, $$p \leq 0.002$$). Respondents on only insulin treatment modality would have an increased knowledge of diabetes by 4.17 ($B = 4.17$, $$p \leq 0.023$$) while those on combined therapy (both oral hypoglycaemic agent and insulin) would have an increased knowledge of diabetes by 7.26 ($B = 7.26$, $p \leq 0.001$) relative to those on oral hypoglycaemic agent (OHA) treatment modality, holding all other variables constant. Respondents without medically confirmed diabetic complications had an increase in knowledge of diabetes by 3.66 ($B = 3.66$, $$p \leq 0.002$$), keeping all other variables constant. Holding all other variables constant, respondents with no family history of diabetes and those with an unknown family history had a decreased knowledge of diabetes by 2.82 (B = −2.82, $$p \leq 0.023$$) and 3.04 (B = −3.04, $$p \leq 0.037$$), respectively, relative to respondents with a family history of diabetes.
**TABLE 3**
| Variable | B1 ± SE | Beta2 | p value | 95% CI for B | 95% CI for B.1 |
| --- | --- | --- | --- | --- | --- |
| Variable | B1 ± SE | Beta2 | p value | Lower | Upper |
| Waist circumference | −0.03 ± 0.05 | −0.04 | 0.449 | −0.123 | 0.055 |
| BMI | −0.10 ± 0.10 | −0.06 | 0.288 | −0.299 | 0.089 |
| No. of years with diabetes | 0.38 ± 0.80 | 0.04 | 0.631 | −1.189 | 1.958 |
| No. of years of visits to the diabetes clinic | 2.28 ± 0.71 | 0.25 | 0.002 | 0.878 | 3.691 |
| Type of treatment | Type of treatment | Type of treatment | Type of treatment | Type of treatment | Type of treatment |
| OHA | Ref | | | | |
| Insulin | 4.17 ± 1.83 | 0.12 | 0.023 | 0.568 | 7.777 |
| Both OHA and insulin | 7.26 ± 1.60 | 0.24 | <0.001 | 4.110 | 10.408 |
| Medically confirmed complication | Medically confirmed complication | Medically confirmed complication | Medically confirmed complication | Medically confirmed complication | Medically confirmed complication |
| Yes | Ref | | | | |
| No | 3.66 ± 1.16 | 0.17 | 0.002 | 1.378 | 5.941 |
| Not sure | 4.43 ± 3.83 | 0.06 | 0.249 | −3.111 | 11.971 |
| Smoking | Smoking | Smoking | Smoking | Smoking | Smoking |
| Yes | Ref | | | | |
| No | 4.93 ± 2.53 | 0.10 | 0.053 | −0.054 | 9.907 |
| Family history | Family history | Family history | Family history | Family history | Family history |
| Yes | Ref | | | | |
| No | −2.82 ± 1.23 | −0.13 | 0.023 | −5.241 | −0.392 |
| Do not know | −3.04 ± 1.46 | −0.11 | 0.037 | −5.911 | −0.178 |
## Association between clinical characteristics and self‐care practice of participants
In Table 4, multivariable regression analysis revealed an adjusted R2 of 0.13, $p \leq 0.001.$ It was revealed that keeping all other variables constant, a 1‐year increase in years of visits to the diabetic clinic would increase diabetes self‐care practice by 0.69 ($B = 0.69$, $$p \leq 0.005$$). Respondents on insulin treatment modality had a $40\%$ increase in self‐care practice score ($B = 1.4$, $$p \leq 0.028$$) relative to respondents on oral hypoglycemic agents. Respondents without medically confirmed diabetic complications had $12\%$ increased self‐care practice relative to respondents with medically confirmied diabetic complications ($B = 1.12$, $$p \leq 0.005$$). Self‐care practice in those with an unknown family history of diabetes decreased by 1.24 times (B = −1.24, $$p \leq 0.014$$) relative to participants with a family history of diabetes.
**TABLE 4**
| Variable | B1 ± SE | Beta2 | p value | 95% CI for B | 95% CI for B.1 |
| --- | --- | --- | --- | --- | --- |
| Variable | B1 ± SE | Beta2 | p value | Lower | Upper |
| Waist circumference | 0.01 ± 0.02 | 0.01 | 0.829 | −0.027 | 0.034 |
| BMI | −0.05 ± 0.03 | −0.08 | 0.174 | −0.112 | 0.020 |
| No. of years with diabetes | −0.09 ± 0.27 | −0.03 | 0.736 | −0.633 | 0.447 |
| No. of years of visits to the diabetes clinic | 0.69 ± 0.24 | 0.23 | 0.005 | 0.208 | 1.167 |
| Type of treatment | Type of treatment | Type of treatment | Type of treatment | Type of treatment | Type of treatment |
| Oral hypoglycemic | Ref | | | | |
| Insulin | 1.40 ± 0.63 | 0.12 | 0.028 | 0.155 | 2.639 |
| Both oral and insulin | 0.78 ± 0.56 | 0.08 | 0.161 | −0.313 | 1.880 |
| Medically confirmed complication | Medically confirmed complication | Medically confirmed complication | Medically confirmed complication | Medically confirmed complication | Medically confirmed complication |
| Yes | Ref | | | | |
| No | 1.12 ± 0.39 | 0.16 | 0.005 | 0.344 | 1.897 |
| Not sure | −2.09 ± 1.31 | −0.09 | 0.112 | −4.666 | 0.487 |
| Smoking | Smoking | Smoking | Smoking | Smoking | Smoking |
| Yes | Ref | | | | |
| No | 0.88 ± 0.87 | 0.05 | 0.310 | −0.825 | 2.589 |
| Family history | Family history | Family history | Family history | Family history | Family history |
| Yes | Ref | | | | |
| No | −0.57 ± 0.42 | −0.08 | 0.178 | −1.394 | 0.260 |
| Do not know | −1.24 ± 0.50 | −0.14 | 0.014 | −2.229 | −0.256 |
## Association between self‐care practice and diabetes complications
In Table 5, the multivariable regression analysis show the relationship between self‐care practice and diabetes complications. It was revealed that having hypertension would decrease self‐care practice by $13\%$ (B = −1.13, $$p \leq 0.021$$) while having diabetic foot would decrease self‐care practice by $4.46\%$ (B = −4.46, $$p \leq 0.001$$), keeping all other variables constant.
**TABLE 5**
| Variable | B1 ± SE | Beta2 | p value | 95% CI for B | 95% CI for B.1 |
| --- | --- | --- | --- | --- | --- |
| Variable | B1 ± SE | Beta2 | p value | Lower | Upper |
| Retinopathy (Yes) | −0.75 ± 0.81 | −0.05 | 0.359 | −2.349 | 0.853 |
| Neuropathy (Yes) | −1.06 ± 0.93 | −0.06 | 0.253 | −2.892 | 0.763 |
| Nephropathy (Yes) | −0.10 ± 1.75 | −0.01 | 0.955 | −3.544 | 3.348 |
| Cognitive impairment (Yes) | 4.40 ± 3.48 | 0.07 | 0.206 | −2.44 | 11.244 |
| Heart disease (Yes) | −0.80 ± 1.57 | −0.03 | 0.612 | −3.888 | 2.292 |
| Hypertension (Yes) | −1.13 ± 0.49 | −0.13 | 0.021 | −2.086 | −0.169 |
| Hypoactive sexual arousal (Yes) | 0.31 ± 1.07 | 0.02 | 0.772 | −1.803 | 2.425 |
| Diabetic foot (Yes) | −4.46 ± 1.33 | −0.18 | 0.001 | −7.080 | −1.831 |
## DISCUSSION
In this study, the association of clinical factors with knowledge and self‐care practice among people with T2DM in Ghana has been established. It was observed that participants on insulin treatment modality, combined therapy (oral hypoglycaemic agent and insulin), participants without diabetic complication and participants without family history of diabetes had higher knowledge of diabetes.
Participants on combined treatment were found to have higher knowledge of diabetes compared with those on monotherapy. These patients might have had uncontrolled diabetes/comorbidities with monotherapy. This may result in more frequent contact with their healthcare providers. Healthcare workers would probably pay particular attention to these patients in terms of providing adequate education to enable them to achieve optimal glycaemic targets, thereby increasing their knowledge of the disease. The study showed that participants on insulin therapy and those without diabetic complications had higher self‐care practices. Consistent with a study done in Addis Ababa Ethiopia (Mamo & Demissie, 2016), our findings had shown that patients treated with insulin were more likely to have better self‐care practices compared with those on a tablet. The patients on insulin therapy probably had uncontrolled diabetes which may account for the relatively better self‐care practice. It is evident from this study that the patients using insulin had higher knowledge of diabetes compared to those who used only oral hypoglycaemic agents. Previous studies have demonstrated that knowledge of diabetes is positively correlated with self‐care practice (Karaoui et al., 2018). This means that the higher the knowledge of diabetes, the more likely participants are to perform self‐care. A contrasting result was found in a study done in Ethiopia where participants using combined therapy (insulin and oral hypoglycaemic treatment) were more likely to have better self‐care practices compared to their counterparts who used only oral medications (Abate et al., 2018).
Patients without diabetic complications were more likely to have good self‐care practices compared to those with complications. In this study, participants with diabetic foot and hypertension as complications had higher odds of poor self‐care practice. Patients with T2DM particularly with impaired vision, amputation or other complications may have challenges performing activities of daily living (Sparring et al., 2013) and may be hospitalized frequently which can affect their self‐care (Comino et al., 2015) than those without complications. To improve self‐care practice, there is a need for coordinated educational programs targeting patients with complications. Social support is required to improve self‐care practice among patients with complications. It is recommended that healthcare workers identify the specific needs of these patients and provide supportive care. Moreover, the findings of the present study show that participants without family history of diabetes had decreased self‐care practice. Having a family history of diabetes may provide patients with the opportunity to learn from other members who have had DM. A previous study found a family history to be associated with knowledge of diabetes (Kassahun et al., 2016). Information obtained from family members with DM could be very useful for effective self‐care practice.
According to a study done in Australia, BMI is associated with diabetes knowledge (Dixon et al., 2014). However, in the present study, there was no correlation between BMI and knowledge of diabetes or self‐care practice for the disease. The majority of participants in this study had lower educational status and may not know the health risks associated with obesity. Moreover, participants herein and of the Australian study may vary in their perception of the health implications of excess body weight. Overweight and obesity are usually seen as a mark of beauty and wealth in many developing countries including Ghana (Addo et al., 2009; Wahab et al., 2011) but mostly considered a health risk in several industrialized countries. Thus, the population of diabetes patients in the present study might not be conscious of the health implications of excess weight and may not take measures to control it and this has implications for targeted educational strategies by health providers.
## Limitations of the study
The cross‐sectional design does not show temporal relationship or causality though it demonstrates associations between variables. The use of self‐report to measure adherence to self‐care practice can lead to recall and social desirability bias. Although social desirability bias could have occurred, the study identified factors that influence diabetes knowledge and self‐care practice which can be used to improve diabetes management. The convenience sampling procedure is less likely to produce an accurate and representative sample.
## CONCLUSION
We have described several vital factors influencing diabetes knowledge and self‐care practice among people with T2DM in Ghana. Treatment modality and diabetic complications were significantly associated with both diabetes knowledge and self‐care practice. In particular, those who were on insulin and combined therapy (tablet and insulin) had higher knowledge and better self‐care practice. Self‐care was significantly influenced among those with, than those without diabetic foot and hypertension as complications. Thus, we recommend that health professionals providing care to people with diabetes evaluate these factors and provide appropriate education and support as well as training in self‐care especially for patients with diabetic complications.
## AUTHOR CONTRIBUTIONS
All authors contributed significantly to to the conception and design, collection of data, data analysis and interpretation. First author drafted the manuscript and all authors reviewed and provided their approval for publication of the final version.
## FUNDING INFORMATION
No funding was received for this study.
## CONFLICT OF INTEREST
The authors have no conflict of interest.
## ETHICAL STATEMENT
Approval for this study was granted by the Committee on Human Research, Publication and Ethics of the Kwame Nkrumah University of Science and Technology/Komfo Anokye Teaching Hospital (CHRPE/AP/$\frac{576}{18}$). Permission was sought from the heads of the hospitals to conduct the study in their facilities. The purpose and objectives of the study were explained to respondents and written informed consent obtained before data collection. The patients were informed that participation is voluntary and that they can opt‐out of the study at any time without any consequences to them. Respondents were assured of confidentiality and that no personal identifiers will be used in the questionnaire.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author.
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|
---
title: 'Muscle sympathetic nerve activity during pregnancy: A systematic review and
meta‐analysis'
authors:
- Kelly M. Greenwall
- Áine Brislane
- Brittany A. Matenchuk
- Allison Sivak
- Margie H. Davenport
- Craig D. Steinback
journal: Physiological Reports
year: 2023
pmcid: PMC10006587
doi: 10.14814/phy2.15626
license: CC BY 4.0
---
# Muscle sympathetic nerve activity during pregnancy: A systematic review and meta‐analysis
## Body
Novelty and relevance What Is New? The first systematic review and meta‐analysis on muscle sympathetic nerve (re) activity in uncomplicated and complicated pregnancy. What Is Relevant? Uncomplicated pregnancy is associated with sympathoexcitation (versus the non‐pregnant state) that is associated with prevailing blood pressure. Blunted baroreflex engagement and neuro‐cardiovascular transduction may stabilize blood pressure. Further augmentation in sympathetic nerve activity is observed in pregnancy complications and may precede diagnosis. Clinical/Pathophysiological Implications? Understanding sympathetic regulation during pregnancy may help identify those at risk of pregnancy complications, aid treatment, improve pregnancy outcomes, and reduce cardiovascular disease risk.
## Abstract
We conducted a systematic review and meta‐analysis to quantify the impact of healthy and complex pregnancy on muscle sympathetic nerve activity (MSNA) at rest, and in response to stress. Structured searches of electronic databases were performed until February 23, 2022. All study designs (except reviews) were included: population (pregnant individuals); exposures (healthy and complicated pregnancy with direct measures of MSNA); comparator (individuals who were not pregnant, or with uncomplicated pregnancy); and outcomes (MSNA, BP, and heart rate). Twenty‐seven studies ($$n = 807$$) were included. MSNA burst frequency was higher in pregnancy ($$n = 201$$) versus non‐pregnant controls ($$n = 194$$) (Mean Differences [MD], MD: 10.6 bursts/min; $95\%$ CI: 7.2, 14.0; I 2 = $72\%$). Accounting for the normative increase in heart rate with gestation, burst incidence was also higher during pregnancy (Pregnant $$n = 189$$, non‐pregnant $$n = 173$$; MD: 11 bpm; $95\%$ CI: 8, 13 bpm; I 2 = $47\%$; $p \leq 0.0001$). Meta‐regression analyses confirmed that although sympathetic burst frequency and incidence are augmented during pregnancy, this was not significantly associated with gestational age. Compared to uncomplicated pregnancy, individuals with obesity, obstructive sleep apnea, and gestational hypertension exhibited sympathetic hyperactivity, while individuals with gestational diabetes mellitus or preeclampsia did not. Uncomplicated pregnancies exhibited a lesser response to head‐up tilt, but an exaggerated sympathetic responsiveness to cold pressor stress compared to non‐pregnant individuals. MSNA is higher in pregnant individuals and further increased with some, but not all pregnancy complications. PROSPERO registration number: CRD42022311590.
## INTRODUCTION
Pregnancy is a period of rapid and profound cardiovascular adaptation that is necessary to support a successful pregnancy (Brislane et al., 2021). In healthy pregnancy, blood volume increases by $50\%$ until term which is accompanied by peripheral vasodilation. Resting muscle sympathetic nerve activity (MSNA) has been reported to progressively increase compared to non‐pregnant controls (Charkoudian et al., 2017; Fischer et al., 2004; Greenwood et al., 2001, 2003; Hissen et al., 2017; Jarvis et al., 2012; Okada et al., 2015; Reyes et al., 2018; Reyes, Usselman, Skow, et al., 2018; Usselman, Skow, et al., 2015; Usselman, Wakefield, et al., 2015). It is thought that this increase in MSNA may arise in order to regulate blood pressure in the face of vasodilation (Fu & Levine, 2009). However, as we previously discussed in Reyes, Usselman, Skow, et al. [ 2018], the findings across studies appear heterogeneous with different research groups reporting variable changes in basal MSNA (Reyes et al., 2020). Further, the increase in sympathetic activity across pregnancy has yet to be systematically quantified.
In non‐pregnant populations, basal MSNA appears to be related endothelial function, arterial stiffness, and the development of overt cardiovascular disease, including chronic hypertension. However, these relationships have been demonstrated in mixed cohorts of participants (males and females). Further, the influence of prevailing MSNA on blood pressure appears less in young females compared to males (Baker et al., 2016). Thus, while the “normal” increase in MSNA reported during healthy pregnancy may or may not have a negative influence on cardiometabolic function, it remains possible that excessive sympathoexcitation may contribute to the development of pregnancy related complications including gestational hypertension, preeclampsia, and gestational diabetes. Therefore, investigating sympathetic regulation during pregnancy may be important for the prediction and understanding of gestational complications.
Although there are correlates between sympathetic hyperactivity and cardiometabolic disorders, (Grassi et al., 2020; Maier et al., 2022) sympathetic cardiovascular responsiveness to stress testing may be of equal, or greater importance (Chida & Steptoe, 2010; Kasagi et al., 1995). In pregnancy, reactivity testing imposes additional demands on an already stressed cardiovascular and sympathetic nervous systems. Dysfunctional MSNA responses to summative stress may be indicative of physiological maladaptation of the neurovascular system during pregnancy and may therefore be of additional clinical importance. Nonetheless, few studies have examined MSNA responses to reactivity testing in pregnancy, nor has there been synthesis of reactivity responses among uncomplicated and complicated pregnancies (e.g., those affected by health complications including gestational diabetes, preeclampsia, and obesity). Therefore, we conducted a systematic review and meta‐analyses to quantify (A) basal MSNA in “healthy” pregnancy compared to the non‐pregnant state and across gestation, (B) the influence of pregnancy complications (including gestational hypertension, preeclampsia, gestational diabetes, obstructive sleep apnea, and obesity) on basal MSNA, and (C) MSNA reactivity to stress testing in normotensive and complicated pregnancies.
## METHODS
We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines on systematic reviews and meta‐analysis. The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO; Registration no. CRD42022311590).
## Information sources
A structured search of electronic databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, Cochrane Library, Trip, and ProQuest Dissertations, and Theses) up to February 28, 2022, was performed by a research librarian (A.S.; see the online supplement for complete search strategies; DOI: 10.6084/m9.figshare.20362170; private link: https://figshare.com/s/72ad3090256b475466d0). We also used manual searches of reference lists of reviews and included papers to identify relevant studies. Authors were contacted when data were missing from primary sources. No language or date restrictions were applied.
## Eligibility criteria
The study was guided by the participants, interventions, comparisons, outcomes, and study design (PICOS) framework.
## Study design
All study designs were eligible for meta‐analysis except for narrative or systematic reviews, letters, commentaries, and editorials. Where relevant, these types of studies have been incorporated narratively.
## Population
The population of interest was pregnant women at any stage of pregnancy.
## Intervention/exposure
The exposures of interest included any direct measurement of MSNA in pregnancy at baseline and/or during perturbations (i.e., cold pressor test, isometric handgrip exercises, upright tilts, and lower body negative pressure).
## Comparison
Eligible comparators included non‐pregnant controls, as well as comparisons between uncomplicated pregnancies, and those with cardiovascular and metabolic complications.
## Outcomes
Eligible outcomes included direct measures of MSNA using microneurography. Secondary outcomes included mean arterial pressure, systolic and diastolic blood pressure, and heart rate.
## Study selection
After the removal of duplicates, two reviewers (K.M.G. and C.D.S.) independently assessed the titles and abstracts of articles by Covidence. Studies were selected for full‐text review by at least one reviewer. The full texts of included studies were independently screened by two reviewers (K.M.G. and C.D.S.). Disagreements were resolved through discussion and by decision of a third reviewer where necessary.
## Data extraction
Two reviewers independently extracted data in Microsoft Excel. If the study had multiple publications, the most recent or complete publication was selected in data synthesis; however, relevant data from all publications related to each unique study were extracted. In order to prevent “overcounting” of participants, all authors who published multiple studies in the area were contacted to clarify whether participants were included from multiple studies (please see Online Supplement pages 7 and 8 for more details). Study characteristics (e.g., study period, study design, and country), population characteristics (e.g., number of participants, pregnancy related complication, weight, body mass index [BMI], and age, parity), intervention/exposure, and outcomes (MSNA burst frequency, burst incidence, total activity, amplitude) were extracted and recorded in a table. For missing data, attempts were made to contact the first, or corresponding authors for additional information. Data were synthesized narratively if authors were unable to provide additional data. In cases where data were not presented numerically, the online application WebPlotDigitizer was used to extract data from figures (Web Plot Digitizer, V.3.11. Texas, USA).
## Quality assessment and certainty assessment
Two reviewers (K.G. and Á.B.) independently assessed the risk of bias of each study that was included. We assessed methodological quality of prospective cohort, retrospective cohort, and cross‐sectional studies by using standardized critical appraisal instruments from the Joanna Briggs Institute (JBI) Critical Appraisal of Evidence Effectiveness tool. JBI checklists were used for each study design to determine the extent to which a study had addressed the possibility of bias in its design, conduct, and analysis. Specifically, all studies were screened for potential sources of bias including inappropriate sampling, flawed measurement of exposure, flawed measurement of outcomes, selective/incomplete outcomes, unidentified confounding factors, and inappropriate statistical analysis. The differences in ratings were resolved through discussion. The overall risk of bias of a study was defined as high risk when more than one third of the factors (cross‐sectional and case control studies: ≥3 of 8 factors: cohort studies: ≥4 of 12 factors) were marked as high risk.
## Data synthesis
Reviewer Manager v5.4 (Cochrane Collaboration, Copenhagen, Denmark) was used to conduct the statistical analysis. For continuous outcomes, mean values and their standard deviation (SD) were used in the meta‐analysis. When the reported outcome was analyzed using multiple methods (e.g., total MSNA), data were reported as standard mean difference (SMD). SMD effect sizes (SMD) were calculated in Review Manager v5.4 using Hedges' g method (similar to Cohen's d). As a general rule, an effect size of 0.2, 0.4, and 0.8 is considered small, moderate, and large, respectively. We calculated $95\%$ CIs using the random‐effects model with inverse variance weighting of pool estimates of the association between MSNA outcomes in pregnancy. Statistical significance was defined as a p‐value <0.05. Statistical heterogeneity was assessed using I 2 statistics. In the case of I 2 > $50\%$, heterogeneity was explored further with preplanned sensitivity analysis. A priori‐determined subgroup analysis were conducted, when possible, for pregnancy related complications (gestational hypertension, preeclampsia, gestational diabetes, obesity). Data that could not be transformed to be included in a meta‐analysis, the data were narratively synthesized.
## Study characteristics
A PRISMA diagram of the study search and selection process is shown in Figure 1. Twenty‐seven studies reporting on 807 participants (663 pregnant) from six countries met the inclusion criteria of the systematic review. Data from these studies were identified from the first, second, or third trimesters of pregnancy. Pregnancy complications included individuals diagnosed with hypertensive disorders of pregnancy (preeclampsia, gestational hypertension, gestational diabetes, obesity, and obstructive sleep apnea) (OSA, Table S1; DOI: 10.6084/m9.figshare.20362170). Gestational hypertension is de novo hypertension (>$\frac{140}{90}$ mmHg no earlier than 20 weeks gestation) while preeclampsia is similarly diagnosed plus the presence of proteinuria, or evidence of end‐organ dysfunction (e.g., thrombocytopenia, renal insufficiency, impaired liver function, pulmonary edema, and cerebral/visual symptoms). The majority of studies combined both nulliparous and multiparous women (or did not clearly report on parity). Only six studies included exclusively nulliparous (Greenwood et al., 1998, 2001, 2003; Hissen et al., 2017) or multiparous women (Fischer et al., 2004; Stickford et al., 2015).
**FIGURE 1:** *Study flow diagram.*
Five papers reported that at least one participant within control/healthy groups had a personal history of a prior complicated pregnancy. The control group in Reyes et al. [ 2021] was matched to the group diagnosed with preeclampsia by co‐morbidities (e.g., obesity and GDM). In Fischer et al. [ 2004], all women had a history of preeclampsia. Badrov et al. [ 2019] included a low‐risk group of individuals with no history of preeclampsia and a high risk group that had a personal history of preeclampsia. Skow et al. [ 2020] reported one person with a history of gestational hypertension, and Skow et al. [ 2021] reported one person with a personal history of GDM and gestational hypertension each.
The studies reporting on individuals with obesity were based on either preconception or early pregnancy BMI. Studies reporting on individuals with OSA were diagnosed with the condition during pregnancy.
Three corresponding authors were sent emails requesting additional information or clarification of data. Two responded to the emails, and one provided additional information (see the online supplement for additional information; DOI: 10.6084/m9.figshare.20362170). Some studies included within the review reported on the same participants across multiple studies. In these cases, steps were taken to prevent overcounting of participants (Reyes et al., 2021). Steps included contacting authors to provide specific information related to overlapping data; the paper reporting on the most number of participants was selected and others removed, or in cases of partial overlap of data, this was mathematically accounted for in the total number of participants (described in detail in the online supplement DOI: 10.6084/m9.figshare.20362170). The majority of papers arise from Dr. Qi Fu's research group, as well as our research group. Our team was able to identify and adjust numbers based on on our own data, and Dr. Qi *Fu* generously provided detailed information about overlap across studies from their lab.
## Quality assessment (risk of bias)
JBI tools for observational studies were used to assess the quality of each individual study (see Tables S2–S5; DOI: 10.6084/m9.figshare.20362170). Common sources of bias included lack clarity regarding the validity and reliability of measuring MSNA, lack of confounding factors identified and inability to determine whether appropriate statistical analysis had been used
## Basal sympathetic activity in uncomplicated pregnancy
Overall, baseline MSNA burst frequency was higher in pregnant ($$n = 201$$) compared with non‐pregnant individuals ($$n = 194$$; MD: 10.6 bursts/min; $95\%$ CI: 7.2, 14.0; I 2 = $72\%$; $p \leq 0.0001$; see Figure 2). This was apparent as early as trimester 1, and subgroup analyses did not identify any difference between trimesters (χ2 = 0.34, $$p \leq 0.84$$, I 2 = $0\%$) (Figure 2). This pattern of elevated burst frequency in pregnancy is reaffirmed by three longitudinal case studies (reported in two papers) that could not be included in the meta‐analysis (Hissen et al., 2017; Reyes, Usselman, Skow, et al., 2018).
**FIGURE 2:** *Basal MSNA burst frequency (bursts per minute; top) and incidence (bursts/100 heart beats; bottom) across trimesters during uncomplicated pregnancy. Mean differences are in relation to nonpregnant control groups. MD, mean difference; df, degrees of freedom; GH, subsequently developed gestational hypertension; HR, high risk; IV, inverse variance; LR, low risk.*
To account for the gestationally dependent change in resting heart rate (pregnant $$n = 189$$, non‐pregnant $$n = 173$$; MD: 11 bpm; $95\%$ CI: 8, 13; I 2 = $47\%$; $p \leq 0.0001$; see Figure S1; DOI: 10.6084/m9.figshare.20362170), we analyzed sympathetic burst incidence (bursts/100 heart beats). In this analysis, sympathetic activity remained significantly higher in pregnancy ($$n = 196$$) compared with the non‐pregnant state ($$n = 199$$; MD: 11.2 bursts/100 hb; $95\%$ CI: 7.9, 14.4; I 2 = $64\%$, $p \leq 0.0001$; see Figure 2). As with burst frequency, subgroup analyses indicated significant increases in burst incidence as early as the first trimester, but subgroup analyses indicated no differences between trimesters 1, 2, or 3 for basal sympathetic burst incidence (χ2 = 0, I 2 = $0\%$, $$p \leq 1.0$$) (Figure 2).
Meta‐regression analyses confirmed that although sympathetic burst frequency and incidence are augmented during pregnancy, this was not significantly related to gestational age (see Figures S2 and S3; DOI: 10.6084/m9.figshare.20362170).
Few studies reported total MSNA activity, but these limited data also indicated higher sympathetic activity during pregnancy ($$n = 119$$) compared to non‐pregnant individuals ($$n = 118$$; SMD: 0.8 AU/min; $95\%$ CI: 0.5, 1.7; I 2 = $30\%$; $p \leq 0.001$ large effect size). This was observed in both trimester 1 (SMD: 0.6 AU/min; $95\%$ CI: 0.2, 0.9; χ2 = 4.06, I 2 = $75.4\%$, $$p \leq 0.002$$; moderate effect size) and trimester 3 (SMD: 1.2 AU/min; $95\%$ CI: 0.7, 1.7, $p \leq 0.001$; see Figure S4; DOI: 10.6084/m9.figshare.20362170).
Despite clearly higher sympathetic activity, our analyses indicated modest reductions in mean blood pressure during trimester 1 (pregnant $$n = 22$$, non‐pregnant $$n = 22$$; MD: −4 mmHg; $95\%$ CI: −8, 0; I 2 = $0\%$; $$p \leq 0.05$$), trimester 2 (pregnant $$n = 11$$, non‐pregnant $$n = 11$$; MD: −12 mmHg; $95\%$ CI: −23, −2; I 2 = N/A; $$p \leq 0.02$$), and trimester 3 (pregnant $$n = 124$$, non‐pregnant $$n = 107$$; MD: −3 mmHg; $95\%$ CI: −5, −1; I 2 = $12\%$, $$p \leq 0.01$$). However, there were no statistical differences between time points for mean blood pressure (χ2 = 2.9; I 2 = $31\%$; $$p \leq 0.23$$; see Figure S5; DOI: 10.6084/m9.figshare.20362170). Overall systolic and diastolic blood pressure was reduced by ~3 mmHg during pregnancy (see Figures S6 and S7; DOI: 10.6084/m9.figshare.20362170). Although there was a modest change in mean arterial pressure, meta‐regression identified that this was related to basal SNA burst frequency during uncomplicated pregnancy, but not in non‐pregnant individuals (Figure 3).
**FIGURE 3:** *Relationship between MSNA burst frequency and mean arterial pressure in uncomplicated pregnancy (left) and nonpregnant controls (right). A significant relationship between MSNA and mean pressure was observed in uncomplicated pregnancy but not nonpregnant individuals. Weighted meta‐regressions are denoted by the dashed line, with 95% confidence intervals indicated by the shaded region. The size of each dot represents the weight of the particular study within the regression analysis.*
## Basal sympathetic activity in complicated pregnancy
When considering pregnancy complications as a whole, baseline MSNA burst frequency is higher for complicated ($$n = 144$$), compared with uncomplicated pregnancies ($$n = 192$$; MD: 10.8 bursts/min; $95\%$ CI: 4.2, 17.4; I 2 = $85\%$; $p \leq 0.0001$; see Figure 4). When pregnancy complications were evaluated independently, gestational hypertension and obesity exhibited augmented sympathetic activity compared to uncomplicated controls. However, a single study on gestational diabetes indicated no difference in burst frequency versus uncomplicated pregnancy. Contrary to expectation, burst frequency was not higher in preeclampsia ($$n = 35$$) compared to uncomplicated pregnancy ($$n = 53$$; MD: 7.07 bursts/min; $95\%$ CI: −10.1, 24.2; I 2 = $93\%$; $$p \leq 0.42$$). Further, Schobel et al. [ 1996] was deemed an outlier within this subgroup (i.e., the lowest CI for this study did not cross the highest CI for the other studies). Following a sensitivity analysis removing Schobel et al. [ 1996] from the analysis, there was no difference between groups ($$n = 71$$; MD −0.68 burst/min; $95\%$ CI: −9.57, 8.22; see Figure S8; DOI: 10.6084/m9.figshare.2036217; private link: https://figshare.com/s/72ad3090256b475466d0).
**FIGURE 4:** *Basal MSNA burst frequency (bursts per minute; top) and incidence (bursts/100 heart beats; bottom) in uncomplicated and complicated pregnancies. df, degrees of freedom;GDM, gestational diabetes mellitus; IV, inverse variance; PE, preeclampsia. Greenwood (2001) utilized old terminology (pregnancy induced hypertension) that falls within the current definition of gestational hypertension.*
A similar pattern and interpretation were observed for burst incidence where pregnancy complications as a whole ($$n = 123$$) had higher burst compared with uncomplicated pregnancies ($$n = 144$$; MD: 14.9 bursts/100 hb; $95\%$ CI: 6.1, 23.7; I 2 = $88\%$; $p \leq 0.0001$; see Figure 4). When evaluated independently, burst incidence was higher with gestational hypertension and obesity. Patients with gestational diabetes and preeclampsia had similar burst incidence as uncomplicated pregnancy. Limited data also reported that pregnant women with obesity and obstructive sleep apnea exhibit higher SNA that those obesity alone (see Figures S9 and S10; DOI: 10.6084/m9.figshare.20362170).
As expected, hypertensive pregnancies (gestational hypertension, preeclampsia) exhibited higher blood pressures compared to normotensive pregnant women. Patients with obesity exhibited higher systolic pressure and heart rates compared to normal weight pregnant women (see Figures S11–S14; DOI: 10.6084/m9.figshare.20362170).
## Baroreflex stress
Studies were identified that evaluated sympathetic responses to sympathetic baroreflex engagement using various degrees of head‐up tilt (30°, 3 studies; 60°, 4 studies); lower body negative pressure and Valsalva's maneuver. MSNA burst frequency (bursts per minute) was elevated in response to 30° and 60° upright tilting to a similar degree in both uncomplicated pregnant and non‐pregnant individuals (see Figures S15 and S16; DOI: 10.6084/m9.figshare.20362170). When accounting for concurrent cardiovagal baroreflex engagement (an increase in heart rate), the change in burst incidence from supine to 30° upright tilting was not different between groups. However, pregnant individuals exhibited a lesser increase in burst incidence compared to non‐pregnant individuals at 60° upright tilting ($$p \leq 0.03$$ for subgroup differences, see Figure 5). Stickford et al. [ 2015] could not be incorporated within our meta‐analysis; however, they demonstrated that women with a history of preeclampsia had a blunted sympathetic response to 60° upright tilting (Δ15 ± 7 bursts/min) compared to women without a history of preeclampsia (Δ26 ± 9 bursts; $$p \leq 0.014$$).
**FIGURE 5:** *MSNA burst incidence in response to 30 °head‐up tilt (top) and 60° head‐up tilt (bottom) in uncomplicated pregnant and nonpregnant women. MD values are in units per minute. df, degrees of freedom; IV, inverse variance; TM1, trimester 1; TM3, trimester 3.*
One study could not be meta‐analyzed as only change scores were reported. Merill et al. [ 1995] used −20 mmHg lower body negative pressure as a modest baroreflex stressor. They observed a ∆8.9 ± 13 bursts per minute increase in burst frequency in third trimester pregnant participants ($$n = 6$$) versus a ∆8.8 ± 3.2 bursts per minute increase in non‐pregnant participants ($$n = 6$$; $$p \leq 0.99$$). These data align with that observed in upright tilt studies.
Schobel et al. [ 1996] used a 15 s Valsalva maneuver (expiratory pressure of 40 mmHg) to assess baroreflex engagement and found no difference in the sympathetic burst frequency response in normotensive pregnant ($$n = 8$$, ∆11 ± 6 bursts/min), normotensive non‐pregnant ($$n = 6$$, ∆12 ± 7 bursts/min), and in women with preeclampsia ($$n = 9$$, ∆9 ± 6 bursts/min).
Consistent across studies and baroreflex, interventions were similar blood pressure and heart rate responses between pregnant and non‐pregnant women (Figures S17–S24; DOI: 10.6084/m9.figshare.20362170).
## Cold pressor test
Our analyses indicated that the increase in Burst frequency during the CPT was greater in uncomplicated pregnancies ($$n = 84$$; MD: 13.9 bursts/min; $95\%$ CI: 9.8, 18.0; I 2 = $0\%$; $p \leq 0.0001$) versus a limited sample of non‐pregnant individuals ($$n = 13$$; MD: 5.0 bursts/min; $95\%$ CI: −2.6, 12.6; $$p \leq 0.20$$) (subgroup analysis: χ2 = 4.1, I 2 = $75.8\%$; $$p \leq 0.04$$, Figure 6). The MSNA burst frequency response to the CPT was similar between women with complicated pregnancies (including GDM and PE) ($$n = 28$$; MD: 10.5 bursts/min; $95\%$ CI: 3.6, 17.4; I 2 = $0\%$; $$p \leq 0.003$$) and those with uncomplicated pregnancies ($$n = 84$$; MD: 13.9 bursts/min; $95\%$ CI: 9.8, 18.0; I 2 = $0\%$; $p \leq 0.0001$); (Group comparison: χ2 = 0.68; I 2 = $0\%$; $$p \leq 0.41$$). This result remained the same when only women with preeclampsia were considered in the “complicated pregnancy” group. One study could not be included in the meta‐analysis. Schobel et al. reported a similar increase in burst frequency in normotensive pregnant ($$n = 8$$, Δ 9 ± 8 bursts/min) and pregnant women with preeclampsia ($$n = 9$$, Δ 9 ± 9 bursts/min) women (Schobel et al., 1996).
**FIGURE 6:** *MSNA burst frequency (Top) and incidence (bottom) in response to cold pressor test in nonpregnant, uncomplicated pregnant, and complicated pregnant women. MD values are in units per minute. df, degrees of freedom; GDM, gestational diabetes mellitus; IV, inverse variance; TM1, trimester 1; TM3, trimester 3; PE, preeclampsia.*
A similar outcome was observed for burst incidence, which was unchanged in non‐pregnant individuals ($$n = 13$$; MD: 2.0 bursts/100 hb; $95\%$ CI: −6.9, 10.9; $$p \leq 0.66$$), but increased in uncomplicated pregnancies ($$n = 68$$; MD: 12.3 bursts/100 hb; $95\%$ CI: 7.9, 16.6; I 2 = $1\%$; $p \leq 0.0001$), with significant differences between the two groups (subgroup analysis: χ2 = 4.12, I 2 = $75.7\%$; $$p \leq 0.04$$; Figure 6). Similarly, the MSNA burst incidence in response to the CPT in women with complicated pregnancies ($$n = 17$$; MD: 10.9 bursts/100 hb; $95\%$ CI: −0.2, 21.9; I 2 = $17\%$; $$p \leq 0.05$$) was similar to responses in uncomplicated pregnancies ($$n = 68$$; MD: 12.3 bursts/100 hb; $95\%$ CI: 7.9, 16.6; I 2 = $1\%$; $p \leq 0.0001$) (Group comparison: χ2 = 0.05; I 2 = $0\%$; $$p \leq 0.82$$). One study could not be included in the meta‐analysis. Greenwood et al. [ 1998] also reported a Δ 20.4 ± 23 bursts/min increase in burst frequency in normotensive pregnant women ($$n = 11$$) and a Δ 37.6 ± 29 burst/min increase in women with gestational hypertension ($$p \leq 0.140$$).
The total MSNA activity response appeared similar between uncomplicated pregnancy ($$n = 47$$; SMD: 0.89 AU/min; $95\%$ CI: 0.38, 1.39; I 2 = $33\%$; $$p \leq 0.0007$$; large effect size) and non‐pregnant individuals ($$n = 13$$; SMD: 0.60 AU/min; $95\%$ CI: −0.12, 1.32; $$p \leq 0.10$$; moderate effect size) (χ2 = 0.4; I 2 = $0\%$; $$p \leq 0.43$$; see Figure S25; DOI: 10.6084/m9.figshare.20362170).
Three other studies included information on CPT responsiveness which we were unable to include in our analyses. Foltmar‐Sander [2003] suggested no difference in the sympathetic responses to CPT during or following pregnancy (data not reported). Schobel et al. [ 1996] also reported a similar increase in burst frequency in normotensive pregnant ($$n = 8$$, Δ9 ± 8 bursts/min) and normotensive non‐pregnant ($$n = 6$$, Δ13 ± 7 bursts/min) women. Greenwood et al. [ 1998] reported a Δ20.4 ± 23 bursts/min increase in burst frequency in normotensive pregnant women ($$n = 11$$) with no non‐pregnant comparator group.
Across all studies, there were no differences in the cardiovascular responses to CPT between complicated pregnancy, uncomplicated pregnant and non‐pregnant individuals (see Figures S26–S29; DOI: 10.6084/m9.figshare.20362170).
## Acute exercise stress
Only one study compared responses to isometric hang‐grip and postexercise circulatory occlusion between non‐pregnant and normotensive pregnant women (Skow et al., 2020). This precluded the meaningful meta‐analyses. However, this study reported no differences in the burst frequency or burst incidence responses to isometric hand grip (IHG) and post exercise circulatory occlusion (PECO) in pregnant and non‐pregnant women. There were also no differences in cardiovascular response to IHG and PECO between groups (see Figures S30–S37; DOI: 10.6084/m9.figshare.20362170).
Data from one study reported no difference in the change in MSNA burst frequency during IHG for individuals with gestational hypertension ($$n = 13$$; Δ 37.8 ± 44 bursts/min) versus those with uncomplicated pregnancies ($$n = 11$$; Δ 23.6 ± 36.8 bursts/min; $$p \leq 0.425$$).
## Chemoreflex deactivation
In response to hyperoxia, burst frequency was reduced in complicated pregnancies ($$n = 19$$; MD: −9.60 bursts/min, $95\%$ CI: −17.2, −2.01; I 2 = $0\%$; $$p \leq 0.01$$). Although hyperoxia did not reduce burst frequency in uncomplicated pregnancies ($$n = 42$$; MD: −2.45 bursts/min, $95\%$ CI: 2.82, −7.72; I 2 = $0\%$; $$p \leq 0.83$$), there were no apparent differences between groups (χ2 = 2.3; I 2 = $56.5\%$; $$p \leq 0.13$$; see Figure S38; DOI: 10.6084/m9.figshare.20362170). The MSNA burst incidence response during hyperoxia exhibited a similar pattern, with no differences in responsiveness between groups (χ2 = 2.4; I 2 = $59\%$; $$p \leq 0.12$$; see Figure S9; DOI: 10.6084/m9.figshare.20362170).
There were no differences in cardiovascular responses to the hyperoxia in complicated pregnancies compared to uncomplicated pregnancies (see Figures S40–S43; DOI: 10.6084/m9.figshare.20362170).
## DISCUSSION
In this systematic review and meta‐analyses, we aimed to quantify basal MSNA in uncomplicated and complicated pregnancies compared to the non‐pregnant state. We also sought to quantify MSNA reactivity to stress testing in both uncomplicated and complicated pregnancies. The key finding of these analyses were as follows: Clear sympathoexcitation during uncomplicated pregnancy, manifesting within the first trimester. Pregnancies complicated by gestational hypertension and obesity (but not preeclampsia or gestational diabetes) exhibit augmented sympathetic activity compared to their peers with uncomplicated pregnancies. Pregnant women having uncomplicated pregnancies appear to have a lesser response to head‐up tilt but an exaggerated sympathetic responsiveness to cold pressor stress.
## A normative increase in sympathetic activity during pregnancy
Our analyses quantify the normative increase in SNA observed in healthy pregnancy during each trimester, equating to roughly +$75\%$, +$135\%$, and +$65\%$ increases sympathetic burst frequency in trimesters 1, 2, and 3, respectively. Further, our meta‐regression demonstrated that changes in sympathetic activity were not associated with gestational age. Looking to those studies that include longitudinal data across trimesters, there is also significant heterogeneity in the time course of sympathetic activation across pregnancy. Additional studies using longitudinal designs are needed, particularly including prepregnant and postpartum assessments to better characterize the trajectory of sympathetic activity with advancing gestation. We also note that three case studies and one cross‐sectional study, including 11 pregnant individuals and a non‐pregnant control group (matched by BMI, age), reported basal MSNA during the second trimester. It is also worth noting that the cohort study by Fischer et al. [ 2004] reporting data during the second trimester enrolled high‐risk women with a history of preeclampsia, who therefore may not strictly represent “normal” pregnancy. Thus, additional data are critical to evaluate sympathetic activity during the often cited, nadir in arterial blood pressure during this time‐period to understand the contribution of MSNA to this pregnancy adaptation.
Due to the concurrent gestationally dependent increases in heart rate, the reporting both burst frequency as an indicator of the kinetic influence of the sympathetic nervous system and burst incidence as a marker of descending neural drive is critical during pregnancy. That said, we demonstrate augmented burst incidence during healthy pregnancy. This augmentation emphasizes an elevated descending sympathetic drive that is independent of concurrent increases in heart rate. However, these data suffer the same limitations noted above. It is worth highlighting the known limitations of comparing total sympathetic activity (which incorporates burst amplitude) between participants and studies. These include differing recording amplitudes due to variations in proximity to active neurons and differences in normalization procedures between laboratory groups. Nonetheless, normalization of burst amplitudes (e.g., largest burst = $100\%$) allows for comparison of relative burst amplitude distribution (and total activity) between groups within a given study. Further, the calculation of standardized mean difference for these data accounts for variation in normalization and data presentation between laboratory groups.
## The influence of pregnancy complications on sympathetic activity during pregnancy
According to our analysis, basal MSNA is elevated in gestational hypertension, obesity and those with combined obesity and obstructive sleep apnea. This augmentation aligns with that observed in non‐pregnant populations with similar disorders (i.e., hypertension, obesity, and obstructive sleep apnea). While these data can be interpreted as a simple “overlaying” of conditions, Badrov et al. [ 2019] recently reported that augmented MSNA in the first trimester precedes the development of gestational hypertension. This suggests that augmented sympathoexcitation may be causal in this disorder. In light of this, the adaptation of the sympathetic nervous system across pregnancy may be clinically relevant in identifying those at risk of pregnancy related complications and its management may help reduce this risk. The observation that obesity was related to augmented MSNA in pregnant women suggests that management of weight and weight gain prior to conception and during pregnancy may be important for cardiovascular health. An unexpected finding was that preeclampsia was not associated with augmented sympathetic activity compared to uncomplicated controls. Schobel et al. [ 1996] were the first to suggest that basal MSNA is higher in women with preeclampsia versus those with a normotensive pregnancy; however, subsequent data that nearly quadruple the included sample size do not replicate this result. It is worth highlighting that Schobel et al. [ 1996] did not identify a difference in sympathetic activity between normotensive pregnant and non‐pregnant women. In fact, the authors documented a lower MSNA in third trimester women compared to non‐pregnant controls. It is therefore possible that this discrepancy manifested as an apparently supra‐elevated MSNA in women with preeclampsia. Considering that gestational hypertension and preeclampsia are distinct pathologies, it may not be surprising that the involvement of the sympathetic nervous system may differ in these disorders. We found a single study investigating sympathetic regulation in gestational diabetes that indicating no difference in basal MSNA compared to uncomplicated peers. In light of the observed influence of obesity on MSNA during pregnancy noted above, further research is warranted to examine sympathetic regulation during metabolic disorders of pregnancy. Overall, our analyses highlights a significant lack of data from all complicated pregnancies and as such, a huge knowledge gap in this area of research.
## Implications for blood pressure control
While a review of the mechanisms underlying the increase in MSNA during pregnancy is beyond the scope of this investigation, one hypothesis is that this increase manifests to counteract a fall in vascular resistance and to maintain stable basal blood pressures. Indeed, our meta‐regression demonstrated a strong relationship between augmented MSNA burst frequency and mean arterial pressure, that is a higher basal MSNA was associated with a higher basal MAP. However, this remains independent of reflex changes in MSNA due to alterations in blood pressure via the baroreflex or vascular responses to fluctuations in MSNA (transduction). Although there were no differences in the sympathetic response to low levels of head‐up tilt (30°), pregnant women exhibited a blunted burst incidence response at 60°, indicative of a lesser baroreflex engagement. This aligns with data that indicate a reduction in sympathetic baroreflex gain during human pregnancy (Usselman, Skow, et al., 2015). Despite augmented basal sympathetic activity a lesser reflex engagement of MSNA during orthostatic stress may be one mechanism contributing to the greater incidence of presyncope/syncope during pregnancy which Gibson et al. estimate to be approximately $30\%$ of pregnant women (Gibson, 2001). When considering reflex sympathetic activation via other stressors such as the cold pressor test, a non‐specific nociceptive stressor, MSNA burst frequency and incidence were greater in pregnant compared with nonpregnant individuals. This occurred without appreciable cardiovascular responses, namely blood pressure, indicating a reduced neurovascular transduction in pregnancy. This is consistent with data from specific studies that indicate a reduced SNA:cardiovascular outcome ratio and blunted neurovascular transduction calculated from signal averaging approaches. Although concurrent changes in input (baroreflex) and output (transduction) gain may serve to attempt to stabilize blood pressure control across gestation, baroreflex dysfunction and or altered transduction may play a role in augmented sympathetic activity and development of pregnancy complications. We have recently reviewed mechanisms that may contribute to altered sympathetic outflow and vascular reactivity, including augmented sex hormones, altered central processing and descending drive, decreased receptor sensitivity or reduced concentrations of sympathetic neurotransmitters (Brislane et al., 2022; Reyes, Usselman, Davenport, & Steinback, 2018). We also refer the reader to a comprehensive review of alterations in baroreflex signaling and brainstem integration which may underly the observed reduction in baroreflex‐mediated MSNA activation during the third trimester of pregnancy (Brooks et al., 2020). Further research is needed to direct linkages between these potential mechanism and functional blood pressure control (presyncope–hypertension) during pregnancy.
## Remaining gaps within the literature
A key consideration is how differences in sympathetic activity and reactivity may influence cardiovascular function and health during pregnancy. There are currently little data reported during the second trimester, and a lack of benchmark data on reflex sympathetic activation and control from normotensive pregnancy. Despite the obvious clinical relevance, there was scant research on reflex sympathetic control (particularly baroreflex regulation) in complicated pregnancies. The most general limitation within the current literature is the lack of data from this critically important life stage.
It is also important to note that the pregnancy literature suffers from some of the same limitations as general physiology literature. Importantly, the majority (but not all) data have been reported from Caucasian women. This severely limits the extrapolation of findings between populations. Important, but limited data have started to address this knowledge gap, suggesting that ethnicity (Asian American, African American populations), may be an important underlying modulator of MSNA during pregnancy.
## PERSPECTIVES
This systematic review and meta‐analysis quantified the elevated basal MSNA in uncomplicated pregnancy and identified a relationship between sympathetic activity and mean blood pressure. Women with an uncomplicated pregnancy exhibit a reduced baroreflex responsiveness to head‐up tilt, and a similar cardiovascular response despite augmented sympathetic responsiveness, to the cold pressor test (indicative of reduced neuro‐cardiovascular transduction) compared to nonpregnant women. Both are clinically relevant for the control of blood pressure. We demonstrated that gestational hypertension and obesity, but not preeclampsia nor gestational diabetes, appear associated with exacerbated MSNA. We also identified important gaps within the literature related to understanding longitudinal changes in sympathetic regulation during pregnancy. This includes additional investigations related to reflex control during uncomplicated and complicated pregnancies, and improved ethnic diversity. The outcomes from our analyses should provide quantitative benchmarks for field of research.
## FUNDING INFORMATION
This research has been funded by the Natural Sciences and Engineering Research Council of Canada (NSERC; CDS RGPIN‐2020‐05385; MHD RGPIN‐2019‐07219). C.D. Steinback is funded by a HSFC Joint National and Alberta New Investigator Award (HSFC NNIA Steinback). M.H. Davenport is supported by the Christenson Professorship in Healthy Active Living and an HSFC Joint National and Alberta Improving Hearth Health for Women New Investigator award (HSFC NNIA Davenport). B. Matenchuk is supported by a CIHR Doctoral Research Award and WCHRI Doctoral research Award. Á. Brislane is supported by a WCHRI Postdoctoral Research Award. These analyses stem from the larger PLASMA initiative (C.D. Steinback, M.H. Davenport).
## DISCLOSURES
None.
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---
title: Central angiotensin 1–7 triggers brown fat thermogenesis
authors:
- F. S. Evangelista
- T. J. Bartness
journal: Physiological Reports
year: 2023
pmcid: PMC10006595
doi: 10.14814/phy2.15621
license: CC BY 4.0
---
# Central angiotensin 1–7 triggers brown fat thermogenesis
## Abstract
We tested the hypothesis that third ventricular (3V) injections of angiotensin 1–7 (Ang 1–7) increases thermogenesis in brown adipose tissue (BAT), and whether the Mas receptor mediates this response. First, in male Siberian hamsters ($$n = 18$$), we evaluated the effect of Ang 1–7 in the interscapular BAT (IBAT) temperature and, using selective Mas receptor antagonist A‐779, the role of Mas receptor in this response. Each animal received 3V injections (200 nL), with 48 h intervals: saline; Ang 1–7 (0.03, 0.3, 3, and 30 nmol); A‐779 (3 nmol); and Ang 1–7 (0.3 nmol) + A‐779 (3 nmol). IBAT temperature increased after 0.3 nmol Ang 1–7 compared with Ang 1–7 + A‐779 at 20, 30, and 60 min. Also, 0.3 nmol Ang 1–7 increased IBAT temperature at 10 and 20 min, and decreased at 60 min compared with pretreatment. IBAT temperature decreased after A‐779 at 60 min and after Ang 1–7 + A‐779 at 30 and 60 min compared with the respective pretreatment. A‐779 and Ang 1–7 + A‐779 decreased core temperature at 60 min compared with 10 min. Then, we evaluated blood and tissue Ang 1–7 levels, and the expression of hormone‐sensitive lipase (HSL) and adipose triglyceride lipase (ATGL) in IBAT. Male Siberian hamsters ($$n = 36$$) were killed 10 min after one of the injections. No changes were observed in blood glucose, serum and IBAT Ang 1–7 levels, and ATGL. Ang 1–7 (0.3 nmol) increased p‐HSL expression compared with A‐779 and increased p‐HSL/HSL ration compared with other injections. Ang 1–7 and Mas receptor immunoreactive cells were found in brain regions that coincide with the sympathetic nerves outflow to BAT. In conclusion, 3V injection of Ang 1–7 induced thermogenesis in IBAT in a Mas receptor‐dependent manner.
Central injection of Ang 1‐7 induces thermogenesis in the brown adipose tissue in a Mas receptor‐dependent manner. Our data contribute to the investigation of new therapies for obesity.
## INTRODUCTION
Effective strategies to treat obesity and its comorbidities are a critical point facing medical science. The stimulation of metabolically active brown adipose tissue (BAT) or the increase of brown‐like adipocytes (browning) can improve energy expenditure, decrease adiposity, and ameliorate metabolic complications of obesity and Type 2 Diabetes Mellitus (T2DM; Gaspar et al., 2021; Yoneshiro et al., 2013), since BAT is a tissue that consumes amounts of fatty acids and glucose as fuel for thermogenesis. In fact, in obese individuals, both increased BAT activation and browning could improve lipid oxidation to generate heat and prevent lipid accumulation (Scheel et al., 2022). Additionally, by increasing peripheral glucose uptake, the BAT activation has become a potential target for improving insulin sensitivity in patients with T2DM (Hanssen et al., 2015).
The renin–angiotensin system (RAS) has emerged as an important target to treat obesity (Morimoto et al., 2018; Oliveira Andrade et al., 2014). The components of the RAS represented by angiotensin converting enzyme 2 (ACE2)/angiotensin 1–7 (Ang 1–7)/ Mas receptor axis expanded the role of the RAS in many physiological and pathophysiological processes. ACE2 can cleave Ang 1 to generate the inactive Ang 1–9 peptide, which then can be converted to the peptide Ang 1–7 by ACE or other peptidases. ACE2 also can directly generate Ang 1–7 from Ang 2. By binding to its specific Mas receptor, Ang 1–7 can cause vasodilation, and antiproliferative, antihypertrophic, antifibrotic, and antithrombotic effects (Santos et al., 2003, 2018; Xia & Lazartigues, 2008).
Ang 1–7 treatment has been shown to enlarge BAT and increase the expression of UCP1, PRDM16, and prohibitin. Also, Ang 1–7 treatment induces brown adipocyte differentiation leading to upregulation of thermogenesis and better metabolic profile in high‐fat‐fed mice (Morimoto et al., 2018). Recently, Cao et al. [ 2022] demonstrated that Ang 1–7‐treated Leprdb/db and the high‐fat‐fed‐induced obese mice were better able to regulate their body temperature during cold stress and had increased UCP1 expression in the BAT compared to the control. They also showed that the increase in UCP1 brown adipocytes was induced by ACE2 pathway activated Akt/FoxO1 and PKA pathway (Cao et al., 2022).
Although the potential therapeutic properties of Ang 1–7 for the treatment of obesity have been elucidated, a knowledge gap still exists about the effect of central Ang 1–7 on adipose tissue metabolism. The first study involving Ang 1–7 peptide in the central nervous system was developed from Fitzsimons [1971], which showed that Ang 1–7 has no dipsogenic effect when injected into the rat brain. Subsequent studies showed the role of Ang 1–7 in the hypothalamic paraventricular nucleus (PVH) and its interaction with neurons that play a pivotal role in cardiovascular regulation (Ambuhl et al., 1994; Han et al., 2012; Silva et al., 2005).
Considering that BAT is innervated by the sympathetic nervous system (Bamshad et al., 1999), the sympathetic neuronal release of norepinephrine activates thermogenesis in the BAT (Contreras et al., 2014), and the potential of central Ang 1–7 to activate sympathetic neurons (Ambuhl et al., 1994; Fitzsimons, 1971; Han et al., 2012; Silva et al., 2005), we hypothesized that central Ang 1–7 modulates the metabolic activity of BAT. Thus, the aim of this study was to test whether third ventricular (3V) injections of Ang 1–7 increases BAT thermogenesis, and whether the Mas receptor mediates this response.
## Animals
Fifty‐four ($$n = 54$$) adult male Siberian hamsters (Phodopus sungorus), 3–4 months old, were obtained from the breeding colony of the laboratory of Prof. Timothy Bartness. The hamsters were single housed in plastic, and maintained under a long‐day photoperiod (16‐h light, 8‐h dark, and lights on at 3:00 h) at 22 ± 1.5°C. Food (Purina Rodent Chow no. 5001) and tap water were available ad libitum throughout the experiment. Housing and all procedures were approved by the Georgia State University Institutional Animals Care and Use Committee (protocol #A12056), and were in accordance with the Public Health Service and U.S. Department of Agriculture guidelines.
## Intraventricular cannula implantation
Cannulae were stereotaxically implanted into the 3V, as described previously (Day & Bartness, 2004). Briefly, the animals were anesthetized with isoflurane $2\%$, and the fur at the top of the head was removed to expose the area to be incised. After exposure of the skull, a hole was trephined at the intersection of bregma and the midsagittal sinus and the guide cannula (26 gauge stainless steel; Plastics One) was positioned using the following stereotaxic coordinates: level skull, anterior‐lateral from bregma, 0 mm; medial‐lateral from midsagittal sinus, 0 mm; and dorsal–ventral, −5.5 mm from the top of the skull, which targeted placement just above the 3V. The guide cannula was secured to the skull with $\frac{3}{16}$ mm jeweler's screws, cyanoacrylate glue, and dental acrylic. A removable obturator (Plastics One) sealed the opening in the guide cannula throughout the experiment, except when it was removed for the injections. After the surgeries, the hamsters were transferred to clean biohazard cages and received subcutaneous injections of ketofen (5 mg/kg; Fort Dodge Animal Health), an analgesic, for 3 days. They also received apple slices to supply readily consumed calories and water.
## Temperature transponder and iButton implants
In a group of hamsters ($$n = 18$$), temperature transponder and iButton were also implanted at the same time as the cannula implantation. These animals were assigned to the procedures of Experiment 1.
The temperature transponder (Implantable Programmable Temperature Transponder 300 [IPTT‐300]; BioMedic Data Systems) was implanted under the IBAT, such that temperature from both pads could be measured. For this, the fur around the scapula was shaved, and the skin was wiped with povidone iodine (Ricca Chemical) and alcohol, and then again with povidone iodine. The hamsters were placed in ventral recumbency, and a subcutaneous incision was made to reveal IBAT. The IBAT was gently pulled out onto an isotonic saline‐soaked sterile surgical drape to prevent desiccation, and the transponder was positioned under the IBAT, and secured to the surrounding muscle with sterile suture (Ethicon, Johnson & Johnson). The skin was closed with sterile wound clips (Stoelting), and nitrofurazone powder (nfz Puffer, Hess and Clark) was applied to minimize infection. Then, the animal was shaved on the ventral side, and placed supine. A vertical incision was made through the skin and the peritoneum to expose the abdominal cavity. The temperature probe iButton was introduced in the abdominal cavity and positioned above the intestines. The peritoneum and skin were closed with sterile sutures and sterile wound clips, respectively. Nitrofurazone powder was applied to minimize infection. The iButton was programmed to monitor the abdominal temperature every 10 min, automatically and continuously. Animals were excluded from the analysis if the temperature transponder or iButton were out of position at the time of death.
## Experiment 1: Injection protocol and temperature measurements
Experiment 1 aimed to determine the IBAT temperature response to doses of Ang 1–7 and, by the use of selective Mas receptor antagonist A‐779, the role of the Mas receptor in changes of IBAT temperature induced by Ang 1–7. Two weeks postcannulation, the animals were adapted to the handling procedure for the ICV injections, once each day, for 10 days. In addition, the temperature sensing wand also was used to adapt the animals to several low beeping sounds produced by the recording apparatus (DAS 5002 Notebook System; BioMedic Data Systems), when acquiring the IBAT temperature.
On the test day at 0700 h, food was removed from the pouches of the hamsters and from their cages, but water was present. Two hours later, the temperature of IBAT was measured to determine the beginning baseline (time 0). After that, hamsters received immediately a single injection of either vehicle (sterile 0.15 M NaCl) or one of the four doses (0.03, 0.3, 3.0, or 30 nmol) of Ang 1–7 (Bachem), or 3 nmol of (D‐Ala7)‐Angiotensin I/II (1–7; A‐779) selective Mas receptor antagonist (Bachem), or Ang 1–7 (0.3 nmol) + A‐779 (3 nmol). The order of the vehicle and drug injections was counterbalanced to minimize drug order effects. The injection volume for the vehicle (sterile saline) or drugs was 200 nL, and each animal received all injections with an interval of 48 h between injections to minimize carryover effects. IBAT temperature was assessed at 10, 20, 30, and 60 min by passing the temperature sensing wand 10–20 mm above the back of the animal in its cage. At the end of the series of injections, an additional 48 h washout period occurred before each hamster was injected peripherally with 0.8 mg/kg of the β‐adrenoceptor agonist, norepinephrine, and IBAT temperature recorded for 60 min as a positive control to assess transponder function.
After the end of the last test, the hamsters were killed by overdose with an intraperitoneal injection of pentobarbital sodium (300 mg/kg), and transcardial perfusion was performed with heparinized $0.02\%$ saline (75 mL), followed by $4\%$ paraformaldehyde (150 mL) solution. Evans blue dye (200 nL) was injected into the cannulae to confirm placement of the cannula in the 3V. The brains were removed and placed in the same fixative overnight, and then immersed in a cryoprotectant solution of $30\%$ sucrose. Each brain was sliced at 40 μm in a freezing stage sliding microtome, then stained with cresyl violet. Animals were included in the analysis if the dye was visible in any part of the 3V.
## Experiment 2: Injection protocol and metabolic measurements
Another set of hamsters ($$n = 36$$) was used to evaluate blood and tissue concentration of Ang 1–7, and the expression of hormone‐sensitive lipase (HSL) and adipose triglyceride lipase (ATGL), which are markers of lipolysis due to their role in triacylglycerol hydrolysis. All fitting procedures followed as described above, except for the absence of temperature transponder and iButton implant. On the test day at 0700 h, food was removed from the pouches of the hamsters and from their cages, but water was present. The hamsters were weighted and 2 h later, they received one single injection (200 nL) of vehicle (sterile 0.15 M NaCl), 0.3 nmol of Ang 1–7 (dose determined from its effect on IBAT temperature in the Experiment 1), and 3 nmol of A‐779 or Ang 1–7 (0.3 nmol) + A‐779 (3 nmol). The hamsters were decapitated 10 min postinjection (time determined from the IBAT temperature response in the Experiment 1), and trunk blood was collected and stored on ice. Blood glucose was measured using a glucometer (AccuChek Advantage Roche Diagnostics®). IBAT was excised, immediately minced on dry ice, snap frozen in dry ice, and stored at −80°C until analysis. Immediately after decapitation, 200 nL of Evans blue dye was injected into the cannulae to confirm placement of the cannula in the 3V. The brains were removed and postfixed in $4\%$ paraformaldehyde for a minimum of 1 week before the slice procedure. Each brain was sliced at 40 μm in a freezing stage sliding microtome, then stained with cresyl violet. Animals were included in the analysis if the dye was visible in any part of the 3V.
## Ang 1–7 dosage
The concentration of Ang 1–7 was measured in serum and IBAT using a commercial peptide enzyme immunoassay kit (MyBioSource Inc), following the manufacturer's instructions (Dilauro et al., 2010).
## Western blotting
IBAT was homogenized in lipid‐associated protein extraction buffer according to (Sherestha et al., 2010) containing 50 mM HEPES, 100 mM NaCl, $10\%$ SDS, 2 mM EDTA, 0.5 mM DTT, 1 mM benzamidine, protease inhibitor cocktail (Calbiochem, EMD Chemicals) at 50 μL/g of tissue, and phosphatase inhibitor cocktail (Halt; Pierce, Thermo Fisher Scientific, Rockford, IL). After the incubation and centrifugation procedures, the infranatant containing the protein extract was aliquoted, and stored at −80°C. Protein content was determined by bicinchoninic acid protein assay kit (Thermo Fisher Scientific). Samples were loaded and subjected to electrophoresis, then were transferred to the membrane. The blot membrane was incubated with $4\%$ nonfat dry milk in Tris‐buffered saline, for 2 h, at 4°C, and then incubated overnight, at 4°C, with anti‐phospho‐HSL (Ser660; Cell Signaling Technology, Boston, MA, USA), anti‐HSL (Cell Signaling Technology), anti‐ATGL (Cell Signaling Technology), and anti‐β‐actin (Cell Signaling Technology). Binding of the primary antibody was detected with the use of anti‐rabbit IgG HRP‐linked secondary antibody (Cell Signaling Technology), at 4°C, for 2 h. The antibodies were diluted at 1:1000. Finally, the membranes were washed in TTBS for 3 × 10 min, and incubated with the chemiluminescent Lumiglo Reagent (Cell Signaling Technology), for 5 min. The bands on the membrane were visualized using an Image Quant LAS 4000 mini system (GE Healthcare Life Sciences®). Band intensities were quantified based on optical densitometry measurements using the ImageJ program (version 1.43 for Windows).
## Ang 1–7 and Mas receptor immunostaining
This procedure was done in four hamsters ($$n = 4$$) to evaluate the anatomical localization of Ang 1–7 and Mas receptor in the brain. The animals were killed and transcardially perfused with heparinized $0.02\%$ saline (100 mL), followed by $4\%$ paraformaldehyde solution pH 7.4 (150 mL). The brains were removed and postfixed in $4\%$ paraformaldehyde for 3 h, and then placed in $30\%$ sucrose solution. Coronal sections (30 μm) were prepared using a freezing stage sliding microtome.
Free‐floating brain sections were rinsed in 0.1 M PBS (2 × 15 min) followed by a 10 min incubation in $10\%$ MeOH, $0.3\%$ H2O2, and 0.1 M PBS. Next, sections were rinsed again in 0.1 M PBS (2 × 15 min), followed by a 1 h incubation in $2\%$ normal goat serum (NGS) in 0.1 M PBS. Sequentially, sections were incubated with primary antibody for rabbit anti‐Ang 1–7 (1:500; Phoenix Pharmaceuticals Inc.) or anti‐Mas receptor (1:200; Alomone Labs), with $0.3\%$ Triton X‐100, $2\%$ NGS in 0.1 M PBS at 40 for 16 h. Next, slices were rinsed in 0.1 M PBS (2 × 15 min), incubated for 1 h in 2‐biotinylated goat anti‐rabbit secondary antibody (1:200; Vector Laboratories) in $2\%$ NGS in PBTx, rinsed in 0.1 M PBS (2 × 15 min), and incubated for 1 h in avidin and biotin (1:800) in 0.1 M PBS. Thereafter, the sections were rinsed in 0.1 M phosphate buffer (PB; 2 × 15 min), following incubation with 3,3′ diaminobenzidine (DAB) in 0.1 M PB, during 5 min. Finally, the sections were rinsed in 0.1 M PB (2 × 15 min), mounted onto slides, and cover slipped using Permount.
## Statistical analyses
Data were reported as mean ± SE. The results of IBAT were compared using two‐way analyses of variance (ANOVA) for repeated measures. The other variables were compared using one‐way ANOVA. The Tukey post hoc test was used to determine differences among means when a significant change was observed with ANOVA. A p ≥ 0.05 was statistically significant.
## RESULTS
The results of Experiment 1 are shown in the Figure 1. Comparisons among different treatments were performed at the same time point, and IBAT temperature was higher after 0.3 nmol Ang 1–7 than Ang 1–7 + A‐779 at 20, 30, and 60 min. The 0.3 nmol Ang 1–7 injection also significantly increased IBAT temperature compared with 30 nmol Ang 1–7 at 10 min, and 30 nmol Ang 1–7 injection significantly increased IBAT temperature compared with Ang 1–7 + A‐779 at 60 min (Figure 1a). Comparisons in each time point of the treatment versus immediately before the same treatment (pretreatment) revealed that IBAT temperature from animals injected with 0.3 nmol Ang 1–7 significantly increased at 10 min and 20 min compared with pretreatment but decreased at 60 min compared with pretreatment. The injection of A‐779 decreased IBAT temperature at 60 min compared with pretreatment, while the injection of Ang 1–7 + A‐779 decreased IBAT temperature at 30 min and 60 min compared with pretreatment (Figure 1a). Third ventricular A‐779 and Ang 1–7 + A‐779 injections significantly decreased abdominal temperature at 60 min compared with 10 min (Figure 1b). Intra‐treatment differences were not observed in abdominal temperature (Figure 1b). The test with norepinephrine revealed proper temperature transponder functioning because IBAT temperature significantly increased at 5, 10, 15, 20, 25, 30, 35, 40, and 45 min after norepinephrine injection (Figure 1c).
**FIGURE 1:** *Difference in IBAT temperature (a) and core temperature (b) before (time 0) and after 3V injections; IBAT temperature after norepinephrine injection (c).Error bars indicate the SE. Saline (n = 18), Ang 1–7 0.03 nmol (n = 16), 0.3 nmol (n = 17), 3 nmol (n = 16), 30 nmol (n = 13), A‐779 (n = 8), and Ang 1–7 + A‐779 (n = 8). *p ≤ 0.05 0.3 nmol Ang 1–7 versus Ang 1–7 + A‐779 at 20′, 30′ and 60′; #
p ≤ 0.05 0.3 nmol Ang 1–7 versus 30 nmol Ang 1–7 at 10′; &
p ≤ 0.05 30 nmol Ang 1–7 versus Ang 1–7 + A‐779 at 60′; a
p ≤0.05 0.3 nmol Ang 1–7 at 0′ versus 10′, 20′ and 60′; b
p ≤ 0.05 A‐779 at 0′ versus 60′; c
p ≤ 0.05 Ang 1–7 + A‐779 at 0′ versus 30′ and 60′; d
p ≤0.05 A‐779 and Ang 1–7 + A‐779 60′ versus 10′; e
p ≤ 0.05 versus 0′; f
p ≤ 0.05 versus 5′. IBAT , interscapular brown adipose tissue; 3V = third ventricular. IBAT and core temperature were compared by two‐way ANOVA and IBAT temperature after norepinephrine was compared by one‐way ANOVA. The Tukey post hoc test was used to determine differences among values.*
No differences in body weight and fat pads weight were found among animals in the Experiment 2 (Table 1). Blood glucose concentration did not change after 10 min of all 3V injections (Table 2). Also, the concentration of Ang 1–7 in the serum and BAT did not change in the animals after 3V injections (Table 2).
As shown in Figure 2a, the p‐HSL expression increased in IBAT after Ang 1–7 injection compared with A‐779. We also found a significant increase in the ratio of p‐HSL to total HSL in IBAT after Ang 1–7 injection compared with saline, A‐779, and Ang 1–7 + A‐779. Injection of Mas receptor antagonist A‐779 + Ang 1–7 blocked the effect of Ang 1–7 on the expression of p‐HSL to total HSL ratio (Figure 2a). The level of ATGL expression in IBAT did not modify after injections of Ang 1–7, A‐779, and Ang 1–7 + A‐779 (Figure 2b).
**FIGURE 2:** *p‐HSL, total HSL, p‐HSL/HSL (a), and ATGL (b) expression in the IBAT.Immunoblotting data are shown as the percentage change compared with saline (n = 4–7). Error bars indicate the SE. *p ≤ 0.05 versus A‐779; #
p ≤ 0.05 versus saline, A‐779, and Ang 1–7 + A‐779. HSL = hormone‐sensitive lipase; ATGL = adipose triglyceride lipase. The results were compared by one‐way ANOVA plus Tukey post hoc test.*
The distribution of Ang 1–7 immunoreactive cells is shown on representative sections through the forebrain in Figure 3b–e. Labeled cells were observed in the hypothalamic paraventricular nucleus (PVH), supraoptic nucleus (SON), arcuate hypothalamic nucleus (Arc), and rostroventral lateral reticular nucleus (RVL). Omission of the primary antibody did not reveal any specific staining (negative control; Figure 3a). In addition, Mas receptor immunoreactive cells were found in different areas of the Siberian hamster brain, including hippocampus (HIPPO), PVH, SON, Arc, RVL, reticular nucleus (Rt), cerebellum, and medial parabrachial nucleus (MPB; Figure 3f–m).
**FIGURE 3:** *Ang 1–7 (b–e) and Mas receptor (f–m) immunostaining in the brain. Control section was incubated without primary antibodies (a). Arc, arcuate hypothalamic nucleus; HIPPO, hippocampus; MPB, medial parabrachial nucleus. PVH, hypothalamic paraventricular nucleus; Rt, reticular nucleus; RVL, rostro ventrolateral reticular nucleus; SON, supraoptic nucleus;*
## DISCUSSION
In this research, we investigated whether 3V injection of Ang 1–7 could increase IBAT temperature in a conscious hamster model. Our results showed that 0.3 nmol Ang 1–7 significantly increased IBAT temperature at 10 and 20 min after injection. In addition, the blockade of Mas receptor with A‐779 did not change IBAT temperature in the first 20 min, but the effect of 0.3 nmol Ang 1–7 was lost when the 3V injection was combined with A‐779, confirming the action of Ang 1–7 in a Mas receptor‐dependent manner.
In a previous study, Silva et al. [ 2005] showed that injection of A‐779 into the PVN reduced sympathetic activity and that the pressor effect produced by Ang 1–7 was blocked by A‐779. These results indicated that the actions of endogenous Ang 1–7 are mediated by the Mas receptor, since A‐779 is a selective Mas receptor antagonist. In addition, Santos et al. [ 2018] described that Ang 1–7 actions in the brain are mainly mediated by its interaction with the Mas receptor. In line with previous studies, our results reinforce the role of the Mas receptor in mediating the central actions of Ang 1–7.
The effect of central Ang 1–7 seems to be short according to our study and the literature. Silva et al. [ 2005] found significant increase in the renal sympathetic nerve activity after 30 min of Ang 1–7 injection in the PVH. The same effect was observed by Han et al. [ 2012] between 7 and 12 min. It is important to note that the Ang 1–7 concentration used in our study was less than the concentration used by Han et al. [ 2012], which observed bigger response in renal sympathetic nerve activity and blood pressure after PVH injection of 3 nmol Ang 1–7 compared with 0.03 nmol and 0.3 nmol.
IBAT temperature started to drop after 30 min with some significant differences in 0.03 nmol Ang 1–7, 3 nmol Ang 1–7, A‐779, and Ang 1–7 + A‐779 compared with the pretreatment values (time 0). Both A‐779 and Ang 1–7 + A‐779 injections significantly decreased core temperature at 60 min compared with 10 min, which can be associated with IBAT temperature reduction in these groups. The drop in IBAT temperature in response to A‐779 alone is likely due to the antagonist having an opposing action on IBAT relative to the agonist and the blockade of endogenous Ang 1–7, since the endogenous Ang 1–7 acting in the PVN neurons may contribute to the maintenance of sympathetic activity, as showed by Silva et al. [ 2005]. Other factors can explain the lower core temperature such as reductions in stress and movement. In fact, some of the animals slept at the end of the measurement period. Although they had an adaptation period before the temperature tests, handling the animals during injection can cause a minimum stress reflecting in the temperature. Interestingly, we did not observe a decrease in IBAT temperature after 0.3 nmol Ang 1–7 injection.
According to the IBAT temperature responses, we determined the concentration of 0.3 nmol of Ang 1–7 to develop the second set of experiment in animals, separated in groups matched for body weight and fat mass.
Peripheral Ang 1–7 has been associated with increases in glucose uptake in insulin‐target tissues and insulin resistance prevention (Cao et al., 2014; Liu et al., 2011; Munoz et al., 2012). Our data demonstrated that 3V injection of Ang 1–7 did not change blood glucose concentration. If the central Ang 1–7 is indeed increasing the sympathetic drive, a possible explanation for unchanged glycemia is the augment in liver glycogenolysis, which counterbalances the reduction in blood glucose concentration induced by BAT sympathetic activation (Chaves et al., 2006; La Fleur et al., 2000). Considering that sympathetic nerve activity was not evaluated in this study, further experiments are needed to confirm this response.
Lipolysis provides substrates that are required for BAT thermogenesis (Labbé et al., 2015). The phosphorylated HSL (p‐HSL) is a potential intracellular marker of catecholamine‐stimulated lipolysis due to the critical role in triacylglycerol hydrolysis (Sherestha et al., 2010). Specifically, catecholamines induce lipolysis in adipocytes after binding to adrenoceptor subtypes beta‐1, beta‐2, and beta‐3 (in rodents, primarily beta 3; Lonnqvist et al., 1995), which are linked to G proteins. Beta‐adrenoceptors are coupled to stimulatory G proteins and activate adenylate cyclase, increasing the production of cyclic adenosine monophosphate (cAMP), and thus activating the protein kinase A (PKA). PKA phosphorylates HSL and perilipin, a protein on the surface of lipid droplets in adipocytes, allow the hydrolysis of TAG (Arner, 2005; Duncan et al., 2007). In addition, PKA‐induced p‐HSL also is necessary for the BAT thermogenesis induced by sympathetic activity (Souza et al., 2007). Therefore, it seems that levels of p‐HSL could serve as fat pad‐specific in vivo indicator of catecholamine‐induced BAT thermogenesis (Sherestha et al., 2010). We showed that central Ang 1–7 increased p‐HSL in IBAT compared with A‐779 and increased the ratio of p‐HSL to total HSL in IBAT compared with saline, A‐779, and Ang 1–7 + A‐779. These results allowed us to speculate that central Ang 1–7 can control BAT thermogenesis through the sympathetic nervous activity in a Mas receptor‐dependent manner. However, new studies are still necessary to demonstrate the Mas receptor expression in sympathetic neurons that project to BAT.
Other proteins are responsible for lipolysis together with HSL, such as ATGL and monoacylglycerol lipase (MAGL). ATGL initiates lipolysis by breaking the first fatty acid from TAG, and then HSL and MAGL act on diacylglycerol and monoacylglycerol, respectively (Lafontan & Langin, 2009). ATGL is predominantly involved in basal lipolysis and appears to be required for all PKA‐stimulated free fatty acid release in the absence of HSL. Although ATGL seems important for cAMP‐dependent PKA stimulation of free fatty acid release, without the activation of HSL and perilipin A, the complete lipolysis of triacylglycerol to its hydrolytic end products of glycerol and free fatty acid cannot be achieved. We found no changes in the level of ATGL expression in IBAT after injections of Ang 1–7, A‐779 and Ang 1–7 + A‐779. This result corroborates the study by (Morimoto et al., 2018), in which it was shown that Ang 1–7 decreased lipid droplet size in the BAT concomitant with increased HSL phosphorylation, but without altering ATGL levels.
To advance knowledge about the components of RAS in the central nervous system, we performed an exploratory analysis to identify the distribution of Ang 1–7 and Mas receptor immunoreactive cells in the brain of the Siberian hamster. According to our data, both Ang 1–7 and Mas receptor cells were found in regions that coincide with the localization of sympathetic nerves that innervate both white and brown adipose tissue. In fact, previous studies showed connected neurons extending from the forebrain to the fat pads, including several hypothalamic, midbrain, and brain stem regions, as well as the spinal cord. Labeled neurons were found in the paraventricular nucleus, suprachiasmatic nucleus, medial preoptic area, dorsomedial hypothalamic nucleus, ventromedial hypothalamic nucleus, and arcuate hypothalamic nucleus (Bamshad et al., 1998, 1999). They also found cells in areas of the brain stem such as the nucleus of the solitary tract, raphe obscurus nucleus, lateral reticular nucleus and rostro ventrolateral reticular nucleus, and in the midbrain (central gray).
Although we have not evaluated the injections sites, the coexpression of Mas receptor with neurons that are part of the sympathetic outflow from brain to BAT, and the ability of Mas receptor agonism in these sites to stimulate lipolysis and thermogenesis can open perspectives for new discoveries about the connection between Ang 1–7 and sympathetic nervous system in the control of BAT metabolism. Our and other immunochemistry results demonstrated this connection.
## CONCLUSION
In conclusion, 3V injection of Ang 1–7 induced thermogenesis in BAT in a Mas receptor‐dependent manner. These data provide strong evidence of central Ang 1–7 for BAT thermoregulation and contribute to the investigation of new therapies for obesity.
## CONFLICT OF INTEREST STATEMENT
The authors declare that there are no conflicts of interest.
## ETHICAL STATEMENT
Studies involving animals in this article were approved by the Georgia State University Institucional Animals Care and Use Committee.
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|
---
title: 'Core competencies of healthcare professionals in Oman: Research and evidence‐based
practice needs attention'
authors:
- Fatma Al Jabri
- Tarja Kvist
- Hannele Turunen
journal: Nursing Open
year: 2022
pmcid: PMC10006615
doi: 10.1002/nop2.1453
license: CC BY 4.0
---
# Core competencies of healthcare professionals in Oman: Research and evidence‐based practice needs attention
## Abstract
### Aim
The aim of the study was to examine [1] the perceptions on core competencies of healthcare professionals working at clinical settings in Oman and [2] which demographic characteristics explain the overall core competency.
### Design
A cross‐sectional design.
### Methods
Healthcare Professional Core Competency Instrument, consisting of 11 sub‐scales with 81 items, was distributed to healthcare professionals ($$n = 1$$,543; 826 nurses and 717 physicians) who worked at primary, secondary and tertiary healthcare institutions. Descriptive statistics, t‐test, ANOVA and linear regression were used for data analysis.
### Results
Altogether 1,078 healthcare professionals (628 nurses and 450 physicians) responded representing $70\%$ overall response rate. Healthcare professionals perceived their overall core competence as excellent, safety being the highest, and research and evidence‐based practice was the lowest. The multiple linear regression analysis revealed that ethnicity, gender and years of working experience were the characters that explained the overall core competence, where expatriate senior professionals reported higher competency levels compared with counterparts.
## INTRODUCTION
Optimizing healthcare system continues to be on healthcare leaders' top agenda. Nevertheless, this can be challenging in view of escalating patients' expectations, increasing demand, advancing technological developments, worsening geopolitics, shortages in staff and increasing the number of errors (Gosling et al., 2019; Institute of Medicine [IOM], 2013). This issue is further magnified during times of pandemics, such as during COVID‐19, that require an unparalleled demand for the care of people in the wake of fear, stigma, misinformation and limitations on movement that disrupt the delivery of health care for all conditions (American Nurses Association [ANA], 2020; Ozdemir & Kerse, 2020). Such challenges can be exacerbated within the workforce if staff's competency is low in implementing appropriate measures despite adequate qualification and training (World Health Organization [WHO], 2020). These concerns create greater emphasis on the operational processes' efficiencies, integrated system optimisation and the competencies of professionals delivering care. The interplay of nurses and physicians – as the major constituents of the healthcare system – play a pivotal role in the integrated healthcare mission (Lalloo et al., 2017; Lombarts et al., 2014).
While nurses' and physicians' technical and functional competencies continue to evolve in line with the evidence‐based practices (EBP), technological developments and rising expectations, there is a great requirement to measure the level of the “core” competencies that are fundamental in assuring an extended value‐focused, efficiency‐based and transformable healthcare system (Flinkman et al., 2017; IOM, 2013). Professionals' core competency has been proposed as one of the factors that impact patient safety and job satisfaction (Albarqouni et al., 2018). These core competencies define a cluster of attributes of knowledge, skills and attitudes that allow the healthcare professionals (HCPs) to perform tasks following acceptable delivery standards (Albarqouni et al., 2018; IOM, 2013; Tromp et al., 2012).
This study aimed to examine the core competencies of both nurses and physicians working at clinical settings in Oman. This investigation is based on an adopted and extensive instrument that consisted of 11 core competencies while the available literature focuses predominantly on the core competencies of either an individual profession (nurses or physicians) (Albarqouni et al., 2018; Faraji et al., 2019; Keykha et al., 2016; Mirlashari et al., 2016; Wei et al., 2018) or covers selective competence sub‐scales (Al Lawati et al., 2019; Al‐Busaidi et al., 2019; Al‐Saadi et al., 2019; Alvarez et al., 2021; Fu et al., 2020; Kanerva et al., 2015; Kantanen et al., 2017; Khezri & Abdekhoda, 2019; Mert Boğa et al., 2020; Mohamed et al., 2020; Rouse & Al‐Maqbali, 2014; Suhonen et al., 2021; Ylitörmänen et al., 2019).
This shall add to the body of knowledge an insight for cross‐integration and a reflective measure of the overall quality care delivery system. This will also determine the training and upskilling requirements for the healthcare team (Ozdemir & Kerse, 2020; WHO, 2020).
## BACKGROUND
The healthcare system continues to endure transformation to improve delivery quality, reduce associated costs and increase stakeholder experience. Calls for such transformation continue to take centre stage at times of unparalleled challenges and pandemics, such as those of COVID‐19 (ANA, 2020; WHO, 2020). This transformation requires HCPs to continue upgrading their “core” competencies in line with the evolving relevant standards and certification programs (Accreditation Council for Graduate Medical Education (ACGME), 2020; American Nurses Credentialing Center [ANCC], 2020; European Commission, 2020; Singapore Nursing Board, 2018). These standards and programs tabulate the minimum core competencies that include [1] quality improvement, [2] professionalism, [3] communication, [4] teamwork and collaboration, [5] patient‐centred care, [6] research and EBP, [7] leadership and management, [8] personal and professional development, [9] ethical and legal practice, [10] safety and [11] health information technology. Core competencies are defined as the skills, values, attitudes and beliefs that the organization stands for and that all HCPs must uphold and demonstrate every day (Albarqouni et al., 2018).
Quality improvement refers to the use of historical and current performance to derive best practices for improving the healthcare delivery system (Gosling et al., 2019). This competence was measured among HCPs and patients in Ghana (Abuosi, 2015) and Germany (Willems & Ingerfurth, 2018) in which a significant difference in the overall perception of quality of care between patients and HCPs was reported in both studies.
Professionalism entails the mechanics of healthcare delivery in reference to the best humanistic, moral, ethical, regulatory and legal practices (Lombarts et al., 2014). Lombarts et al. [ 2014] measured this competence among physicians and nurses in European hospitals which concluded that physicians and nurses display equally high professionals' attitude.
Communication enables effective interaction between HCPs and patients to increase healthcare efficiency and to upgrade quality services and safe clinical practice (Sheldon & Hilaire, 2015). The role of this competence was investigated among nursing professionals in Oman (Rouse & Al‐Maqbali, 2014), Finland (Kanerva et al., 2015; Syyrilä et al., 2020) and Malaysia (Amudha et al., 2018). These studies highlighted the direct role of communication skills in developing patient safety practices and strategies.
Teamwork and collaboration are essential competencies that urge HCPs to exhibit a shared vision, mutual understanding, team bonding and integrated delivery (Ylitörmänen et al., 2019). Studies in Finland and Norway (Ylitörmänen et al., 2019), Egypt (El Sayed & Sleem, 2011) and Australia (Suryanto et al., 2016) indicated positive attitudes from nurses and physicians towards teamwork and collaboration.
Patient‐centred care (PCC) relates to the HCPs' ability to realize patients' expectations, preferences and values and to work together with patients to deliver compassionate, safe and effective care (Delaney, 2018). Suhonen et al. [ 2021] assessed the level of PCC mong Finnish nurses and indicated that PCC was rated as good.
Research and EBP competencies are fundamental for the continuous improvement of healthcare services and delivery processes based on learnings and best practices (Paudel et al., 2018). This competence was measured among HCPs in Oman (Al‐Busaidi et al., 2019; Ammouri et al., 2014), Jordan (AbuRuz et al., 2017), China (Fu et al., 2020) and Spain (Alvarez et al., 2021). Those studies showed that HCPs had positive attitudes towards research and EBP and they considered it as a key component to improve practice, nevertheless the levels of their perceived skills in this dimension were reported as low to moderate.
Leadership and management are another overarching competence that assures performance and drives organizational growth (Sfantou et al., 2017). Kantanen et al. [ 2017] conducted a study among nursing leaders to describe leadership and management competencies at healthcare settings in Finland – which indicated that leaders had a good leadership and management competence. Personal and professional development competence relates to the continuous learning and education and the determination towards positive change and criticism in accordance with professional standards (Manley et al., 2018).
Healthcare professionals have to maintain high levels of ethical and legal competence at all times in accordance with the code of ethics, code of conduct and associated laws and regulations (Pozgar, 2016). The participants' views of ethical and legal aspects were measured at healthcare settings in Oman (Al‐Saadi et al., 2019) that showed high awareness of the importance of patients' rights among nurses and physicians but with low actual adherence levels in practice. Other studies in Australia (Lamont et al., 2019) and Egypt (Aly et al., 2020) found that knowledge of patients' rights and associated decision‐making capacity was insufficient and had a negative influence on the occurrence of medical errors.
Safety is another competence that refers to HCPs' ability to prevent risks and adverse effects on patients through delivery competence and system effectiveness (Lavin et al., 2015). Al‐Mandhari et al. [ 2018] revealed that implementation of Patient Safety Friendly Hospital Initiative (PSFHI) in selected hospitals in Oman had successful outcomes in improving patient's safety. In another study in Oman, Al Lawati et al. [ 2019] evaluated this implementation in primary care institutions in Oman in which HCPs rated patient safety as very good to excellent. Other studies nevertheless indicated low perception of patient safety practice among healthcare providers in Ethiopia (Gizaw et al., 2018) and Egypt (Eldeeb et al., 2016).
Health information technology (HIT) resembles HCPs' ability to use information technology in the way that is most appropriate for improving healthcare quality and effectiveness (Lavin et al., 2015). This competence was evaluated by Khezri and Abdekhoda [2019] among 205 nurses working at Tabriz University of Medical Sciences' hospitals. This study stated that nurses have a good level of informatics skills ($62.98\%$) and knowledge ($59\%$).
This study's purposes were to examine [1] the perceptions on core competencies of HCPs working at clinical settings in Oman and [2] which demographic characteristics explain the overall core competency. This will add to the existing body of knowledge a comprehensive line of sight of the level of core competencies for both nurses and physicians.
## Study context
This study was conducted in Oman – a country located in the south‐eastern Arabian Peninsula with an area of 309,500 square kilometres. Oman has a population of around 4.6 million (National Centre for Statistics and Information [NCSI], 2020). Oman's healthcare system comprises both public and private sectors. The public sector is further sub‐divided into the Ministry of Health (MOH) and the non‐MOH. The MOH comprises 50 hospitals and 211 healthcare centres (Department of Health Information and Statistics, 2019).
The non‐MOH comprises hospitals that serve the employees and their families who belong to a certain sector such as Ministry of Defence Hospital, and Royal Oman Police Hospital. The private sector includes private hospitals, general and specialized clinics (Department of Health Information and Statistics, 2019). MOH hospitals provide the majority of health services. Oman's government covers all medical expenses for its citizens.
## Design
This study used a cross‐sectional study design. Study reporting followed STROBE checklist (see Table A1 in Appendix A).
## Participants and setting
This study targeting all nurses and physicians working at all healthcare institutions (primary, secondary and tertiary) under the MOH in Oman with at least 1 year of experience; excluding students and interns. The total target group was 20,966 (14,433 nurses and 6,533 physicians) (Department of Health Information and Statistics, 2019). Proportional stratified sampling was used to recruit different professions (nurses and physicians) working at primary, secondary and tertiary healthcare institutions. The total sample size was 1,543 (826 nurses and 717 physicians). A website calculator called “Raosoft” was used to calculate the sample size wherein the margin of error and confidence level were taken as $5\%$ and $95\%$, respectively. The resulting power analysis was 374 nurses and 363 physicians (Meysamie et al., 2014).
## Instrument
The Healthcare Professional Core Competency Instrument (HPCCI) was used for data collection. The HPCCI was adopted from the existing valid and reliable tools and the permission to use the tools was granted by their developers (see Table A2 in Appendix B). The instrument was in English and was piloted through convenient sampling of 56 (36 nurses and 20 physicians) HCPs at tertiary hospital in Oman. This sample size was calculated based on Raosoft calculation of sample size. Cronbach's alpha coefficient of the piloted study ranged from 0.697 to 0.980 for the 11 core competency sub‐scales. These values were considered acceptable and a good level of internal consistency in healthcare research projects (Tavakol & Dennick, 2011). The newly adopted instrument was fit for both nurses and physicians and can be applied in different settings. Based on the pilot, there were no changes in the tool.
The questionnaire had two parts. The first part had demographic characteristics: profession, age, gender, ethnicity, position, years of working experience, education, institution level and working area. The second part had 11 sub‐scales with 81 items to assess the participants' self‐rated core competencies as follows: quality improvement (four items such as “Anticipate the variability of clinical practice and acts proactively in the implementation of interventions that ensure quality”), professionalism (six items such as “Treat others respectfully even if they hold different opinions”), communication (10 items such as “Promote an environment to facilitate communication”), team work and collaboration (seven items such as “Collaborate with members of the multidisciplinary team”), patient‐centred care (10 items such as “Involve the patient and family in the planning and implementation of care”), research and EBP (eight items such as “Assess current clinical practice, on an individual and systemic level based on the latest research findings”), leadership and management (10 items such as “Delegate responsibility for care based on assessment of abilities of individuals”), personal and professional development (eight items such as “Identify your own professional development needs by reflecting on practice”), ethical and legal practice (eight items such as “Carry out clinical practice according to legal requirements and organizational policy”), safety (seven items such as “Report and response to an error”) and health information technology (three items such as “Use information and communication technologies in the delivery of patient/client care”). This study's total Cronbach's alpha coefficient was 0.917 and ranged from 0.792 to 0.926 for the 11 core competency sub‐scales (Table 2). A five‐point Likert scale (1: Never, 2: Rarely, 3: Sometimes, 4: Very Often, 5: Always) was used to answer the second part of questionnaire.
## Data collection
Questionnaires along with the fact sheets and consent forms were handed over to the research assistants, who were allocated as focal points for the study in each institution. Questionnaires were distributed for nurses and physicians who provided written informed consent in all working areas over a period of 1 – month – by end of 2018 and the beginning of 2019. Each completed questionnaire was inserted in an envelope at locked boxes allocated in each unit. During that period, the research assistants in each institution verbally announced reminders about the study to the HCPs.
## Analysis
Data were analysed with the Statistical Package for the Social Sciences Computer Program (SPSS version 27.0) using descriptive analysis (frequency, percentage, mean value, standard deviation). The overall core competence variable was a computed variable of 11 core competency sub‐scales with 81 items. ANOVA, t‐test and linear regression were utilized to explain the factors that contribute to the overall core competence level. Age was recorded into three groups: <30 years, 30–40 years and >40 years and working experiences were categorized as <8 years, 8–15 years and >15 years.
To aid the presentation of the results in this study, researchers have set a mean value of 4.0 or more to indicate an “excellent” core competence level. This is based on best practices in the literature and magnet hospital assessment scales, where four is defined as meeting Magnet standards (ANCC, 2017).
## Healthcare professionals' demographic characteristics
The overall response rate was $70\%$ (1,078 of 1,543 overall target). This response constitutes of $58.3\%$ nurses and $41.7\%$ physicians (Table 1). Most of the respondents fell within 30–40 age group and were females with a share contribution of $62\%$ and $71\%$, respectively. The response of Omani staff was $11\%$ higher compared with that of expatriates. Around $65\%$ of respondent HCPs worked at bed side, followed by those who had dual roles, that is clinician and management. Around two‐thirds of respondents had between 8–15 years of working experience. The majority of nurses and physicians had a diploma ($70\%$) and specialist ($56\%$) degrees, respectively, as an educational background. More than half ($52\%$) of the HCPs worked in tertiary hospitals seconded by primary healthcare centres.
**TABLE 1**
| Demographic characteristics | Demographic characteristics.1 | n | % |
| --- | --- | --- | --- |
| Profession | Nurses | 628 | 58.3 |
| Profession | Physicians | 450 | 41.7 |
| Age in (years) | <30 | 148 | 15.1 |
| Age in (years) | 30–40 | 609 | 62.0 |
| Age in (years) | >40 | 225 | 22.9 |
| Gender | Female | 762 | 71.0 |
| Gender | Male | 311 | 29.0 |
| Ethnicity | Omani | 592 | 55.3 |
| Ethnicity | Non‐Omani | 478 | 44.7 |
| Position | Clinician | 608 | 64.8 |
| Position | Management | 45 | 4.8 |
| Position | Both | 285 | 30.4 |
| Working experiences in (years) | <8 | 268 | 26.0 |
| Working experiences in (years) | 8–15 | 482 | 46.8 |
| Working experiences in (years) | >15 | 279 | 27.1 |
| Education for nurses | Diploma | 436 | 69.5 |
| Education for nurses | Bachelor | 180 | 28.7 |
| Education for nurses | Master | 11 | 1.8 |
| Education for physicians | Resident | 152 | 37.0 |
| Education for physicians | Specialist | 231 | 56.2 |
| Education for physicians | Adjunct | 7 | 1.7 |
| Education for physicians | Docent | 9 | 2.2 |
| Education for physicians | Professor | 12 | 2.9 |
| Level of care | Tertiary | 563 | 52.2 |
| Level of care | Secondary | 127 | 11.8 |
| Level of care | Primary healthcare | 388 | 36.0 |
| Working area | Medical | 173 | 16.2 |
| Working area | Surgical | 157 | 14.7 |
| Working area | Paediatrics | 106 | 10.0 |
| Working area | Obstetrics and gynaecology | 122 | 11.5 |
| Working area | Critical care | 156 | 14.6 |
| Working area | Outpatient clinics | 235 | 22.1 |
| Working area | Others | 116 | 10.9 |
## Core competence level of healthcare professionals
Table 2 presents the mean scores of 11 core competency sub‐scales showing that nine of them were above the target level (≥4.0). Safety sub‐scale ($M = 4.545$; SD = 0.484) was given the highest rating followed by ethical and legal practice ($M = 4.460$; SD = 0.511). The scores of quality improvement and research and EBP were under the target level, with 3.945 (SD = 0.666) and 3.720 (SD = 0.780), respectively, reflecting good levels rather than excellent. The overall mean (M) score and standard deviation (SD) for all 11 sub‐scales stood at excellent level ($M = 4.262$; SD = 0.423).
**TABLE 2**
| Sub‐scale | M | SD | Cronbach's alpha |
| --- | --- | --- | --- |
| Safety | 4.545 | 0.484 | 0.894 |
| Ethical and Legal Practice | 4.46 | 0.511 | 0.862 |
| Teamwork and Collaboration | 4.37 | 0.531 | 0.873 |
| Health Information Technology | 4.33 | 0.618 | 0.876 |
| Professionalism | 4.34 | 0.511 | 0.795 |
| Patient‐Centred Care | 4.325 | 0.514 | 0.885 |
| Communication | 4.315 | 0.484 | 0.837 |
| Personal and Professional Development | 4.285 | 0.559 | 0.905 |
| Leadership and Management | 4.245 | 0.569 | 0.913 |
| Quality Improvement | 3.945 | 0.666 | 0.792 |
| Research and EBP | 3.72 | 0.78 | 0.926 |
| Overall core competence | 4.262 | 0.423 | 0.917 |
## Relationship between HCPs' demographic characteristics and overall core competency level
There were no significant differences in nurses' ($M = 4.240$; SD = 0.421) and physicians' ($M = 4.280$; SD = 0.425) assessments concerning the overall core competence level (Table 3). HCPs aged 40 years or older rated themselves more competent ($M = 4.370$; SD = 0.432; $p \leq 0.001$) compared with younger age groups. The Omani staff scored their overall core competence lower than expatriates ($M = 4.180$; SD = 0.427; $$p \leq 0.038$$). HCPs with more than 15 years of work experience resembled a higher overall core competence level ($M = 4.330$; SD = 0.434; $p \leq 0.001$) relative to professionals with lesser experience. HCPs working in the secondary healthcare level ($M = 4.310$; SD = 0.440; $$p \leq 0.011$$) reported slightly higher overall core competence level compared with those working in tertiary or primary healthcare institutions. Participants from a critical care unit credited themselves with overall core competence ($M = 4.360$; SD = 0.375; $p \leq 0.001$) in comparison with participants from other working areas.
**TABLE 3**
| Demographic characteristics | Overall Core Competency Level | Overall Core Competency Level.1 | Overall Core Competency Level.2 |
| --- | --- | --- | --- |
| | M | SD | p |
| Profession | | | 0.822 |
| Nurses | 4.240 | 0.421 | |
| Physicians | 4.280 | 0.425 | |
| Age in (years) | | | <0.001 |
| <30 | 4.110 | 0.437 | |
| 30–40 | 4.240 | 0.408 | |
| >40 | 4.370 | 0.432 | |
| Gender | | | 0.312 |
| Female | 4.220 | 0.425 | |
| Male | 4.350 | 0.402 | |
| Ethnicity | | | 0.038 |
| Omani | 4.180 | 0.427 | |
| Non‐Omani | 4.360 | 0.397 | |
| Position | | | 0.355 |
| Clinician | 4.260 | 0.419 | |
| Management | 4.180 | 0.462 | |
| Both | 4.240 | 0.414 | |
| Working experiences in (years) | | | <0.001 |
| <8 | 4.130 | 0.426 | |
| 8–15 | 4.280 | 0.402 | |
| >15 | 4.330 | 0.434 | |
| Level of care | | | 0.011 |
| Tertiary | 4.280 | 0.425 | |
| Secondary | 4.310 | 0.440 | |
| Primary | 4.210 | 0.410 | |
| Working area | | | <0.001 |
| Medical | 4.270 | 0.379 | |
| Surgical | 4.320 | 0.470 | |
| Paediatrics | 4.220 | 0.394 | |
| Obstetrics and gynaecology | 4.290 | 0.474 | |
| Critical care | 4.360 | 0.375 | |
| Outpatient clinics | 4.170 | 0.413 | |
| Others | 4.190 | 0.435 | |
## Demographic characteristics explaining the overall core competency level
A multiple linear regression analysis was performed to examine which demographic characteristics were explaining the level of HCPs' overall core competency. The adjusted R square was 0.071, which means that $7\%$ of overall core competency is explained by the combined demographic characteristics. This analysis shows that ethnicity had the strongest relationship with the overall core competence level. The standardized regression coefficients were significantly higher in non‐Omani participants compared with Omani (β = 0.158; $p \leq 0.001$; Table 4).
**TABLE 4**
| Dependent variable: overall core competency level | Dependent variable: overall core competency level.1 | Dependent variable: overall core competency level.2 | Dependent variable: overall core competency level.3 | Dependent variable: overall core competency level.4 | Dependent variable: overall core competency level.5 | Dependent variable: overall core competency level.6 | Dependent variable: overall core competency level.7 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Independent variable | B | SE | β | t | p | 95% CI | 95% CI |
| Constant | 4.173 | 0.053 | | 78.163 | <0.001 | 4.069 | 4.278 |
| Profession: Physician (ref: Nurse) | −0.027 | 0.031 | −0.032 | −0.884 | 0.377 | −0.088 | 0.033 |
| Age in (years) | Age in (years) | Age in (years) | Age in (years) | Age in (years) | Age in (years) | Age in (years) | Age in (years) |
| 30–40 years (ref: <30 Years) | 0.006 | 0.036 | 0.007 | 0.172 | 0.863 | −0.064 | 0.076 |
| >40 Years | 0.009 | 0.050 | 0.009 | 0.181 | 0.857 | −0.089 | 0.107 |
| Gender: Female (ref: Male) | −0.085 | 0.034 | −0.091 | −2.506 | 0.012 | −0.151 | 0.018 |
| Ethnicity: Non‐Omani (ref: Omani) | 0.134 | 0.029 | 0.158 | 4.643 | <0.001 | 0.077 | 0.191 |
| Position | Position | Position | Position | Position | Position | Position | Position |
| Management (ref: Clinician) | −0.084 | 0.065 | −0.040 | −1.293 | 0.196 | −0.210 | 0.043 |
| Clinician and management | −0.005 | 0.029 | −0.005 | −0.168 | 0.867 | −0.062 | 0.052 |
| Working experiences in (years) | Working experiences in (years) | Working experiences in (years) | Working experiences in (years) | Working experiences in (years) | Working experiences in (years) | Working experiences in (years) | Working experiences in (years) |
| 8–15 Years (ref: <8 Years) | 0.105 | 0.034 | 0.123 | 3.123 | 0.002 | 0.039 | 0.171 |
| >15 Years | 0.132 | 0.042 | 0.137 | 3.127 | 0.002 | 0.049 | 0.215 |
| Level of Care | Level of Care | Level of Care | Level of Care | Level of Care | Level of Care | Level of Care | Level of Care |
| Secondary (ref: Tertiary) | 0.057 | 0.042 | 0.043 | 1.365 | 0.173 | −0.025 | 0.138 |
| Primary health care | 0.003 | 0.034 | 0.003 | 0.083 | 0.934 | −0.064 | 0.069 |
| Working Area | Working Area | Working Area | Working Area | Working Area | Working Area | Working Area | Working Area |
| Surgical (ref: Medical) | 0.066 | 0.046 | 0.055 | 1.441 | 0.150 | −0.024 | 0.157 |
| Paediatrics | −0.011 | 0.051 | −0.007 | −0.210 | 0.833 | −0.110 | 0.089 |
| Obstetrics and gynaecology | 0.043 | 0.050 | 0.032 | 0.856 | 0.392 | −0.055 | 0.141 |
| Critical care | 0.068 | 0.046 | 0.056 | 1.473 | 0.141 | −0.022 | 0.158 |
| Outpatient clinics | −0.067 | 0.043 | −0.066 | −1.555 | 0.120 | −0.152 | 0.018 |
| Other units | −0.027 | 0.049 | −0.020 | −0.559 | 0.576 | −0.124 | 0.069 |
| F | 5.865 | | | | < 0.001 | | |
| R 2 | 0.086 | | | | | | |
| Adjusted R 2 | 0.071 | | | | | | |
The results also revealed that HCPs who have worked for >15 years (β = 0.137; $p \leq 0.010$) and 8–15 years of experience (β = 0.123; $p \leq 0.010$), rated the overall competency level significantly higher than staff who were working for less than 8 years. Additionally, the results showed that female staff (β = −0.091; $p \leq 0.050$) scored the overall competency level significantly lower than male staff (Table 4).
## Perspectives on HCPs' self‐rated core competencies
Excellent core competence levels – in reference to Magnet standards (ANCC, 2017) – were measured in 9 out of 11 sub‐scales, namely safety, ethical and legal practice, teamwork and collaboration, health information technology, professionalism, patient‐centred care, communication, personal and professional development and leadership and management. While quality improvement and research and EBP sub‐scales were under the target level of Magnet standard.
Healthcare professionals across Oman rated safety as the highest competence indicating HCPs feel confident about their skills, knowledge and attitude towards patient safety. Oman takes patient safety very seriously and is already showing regional leadership in the Eastern Mediterranean Region by adopting the PSFHI to improve the safety of healthcare in public and private hospitals nationwide (Al‐Mandhari et al., 2018). It is therefore not surprising to see that this result coincides with the findings of Al‐Mandhari et al. [ 2016] and Al Lawati et al. [ 2019] who reported patient safety competence rating (based on self‐assessment) for HCPs as very good to excellent. Al‐Mandhari et al. [ 2016] stated that HCPs in Oman demonstrated awareness and implementation of the WHO's nine “Life‐Saving Patient Safety Solutions”.
Similar international case studies done in Saudi Arabia (Alshammari et al., 2019) and South Korea (Jang & Lee, 2017) showed mean values lower than that of Oman with 3.560 and 4.110, respectively.
Research and EBP was rated under the target level indicating its integration in clinical practice is still a challenge in Oman – or a potential “jack‐up” opportunity, as some studies have shown that research and EBP was a direct reason behind $28\%$ of the improvement in patient outcomes when clinical care was based on evidence rather than traditional practices (Melnyk et al., 2012). This finding was supported by other studies conducted in Oman (Al‐Busaidi et al., 2019; Ammouri et al., 2014), China (Hong & Chen, 2019) and Nepal (Paudel et al., 2018) which described the HCPs' practices, attitudes, knowledge/skills and perceived barriers in relation to EBP. These studies showed that while the attitudes of HCPs towards research and EBP were positive, their knowledge and implementation skills were low to moderate. These studies perceived that insufficient time for research and insufficient resources to change practice were barriers to developing the EBP among nurses and physicians.
## Demographic characteristics explaining the level of overall core competency
There was a strong relationship between overall core competency and demographic characteristics (age, ethnicity, working experience, institution level of care and working area). The overall core competency level proportionately increased with more age and working experience. This trend was in line with reported observations of Adib‐Hajbaghery and Eshraghi‐arani [2018], and Rizany et al. [ 2018], while another study reported no significant differences with age (Faraji et al., 2019).
This relationship was further explored with the multiple linear regression analysis of the combined demographic characteristics. This analysis showed that gender, ethnicity and working experience were explained nurses' and physicians' overall core competency. Ethnicity was the main contributing character of the overall core competency level wherein expatriate professionals reported a higher competency level compared to their Omanis counterpart. This finding was similar to a study conducted among nurses and physicians in Oman by Al‐Saadi et al. [ 2019] which aimed to determine the extent of awareness and adherence to patients' rights, and it found that Non‐Omani staff were more aware (OR = 1.696; $$p \leq 0.032$$) and adherent (OR = 2.769; p = <0.001) to patient rights than Omani staff. The available literature does not report the underpinning causes for this; nevertheless it may be that some of the expatriates had received additional and/or specialized education/qualification on patients' rights in their previous career paths.
The multiple regression analysis revealed that gender also was a factor that contributed to the overall core competency, where male staff evaluated themselves as having a slightly higher amount of competence compared with female staff. This study is supported by Wangensteen et al. [ 2012], who stated male nurses assessed their competence higher compared with female nurses' self‐assessment. This study is nevertheless contradicted with another self‐reported study conducted by Adib‐Hajbaghery and Eshraghi‐arani [2018], who believed that clinical competency was a skill that all female and male nurses should have.
This study also shows that the self‐rated overall core competency was higher for staff with more than 15 years of experience compared to those with less working experience. The finding was in line with the results of Chang et al. 's [2011] and Rizany et al. 's [2018] studies, but inconsistent with Faraji et al. 's [2019] findings.
Furthermore, this study presents that staff working at tertiary and secondary institutions rated their competence higher than those at primary healthcare centres. This might be related to the highly specialized facilities, greater expertise and knowledge sharing and more exposure to critical cases. This observation was also seen by Abdulhadi et al. [ 2007] who reported less patient satisfaction with patient‐provider interactions in primary healthcare in Oman. Another study conducted in China confirmed the patient preference to use secondary and tertiary hospitals than primary health centres due to the services and quality of care provided in the hospitals setting (Hu et al., 2016; Li et al., 2020).
The working area also associated with the HCP's overall core competency level. Staff who worked in the critical care units were more competent than other providers working in other units. This result matches Lakanmaa et al. 's [2015] findings, which stated that intensive care unit (ICU) staff rated their competencies in a higher level. This may be due to the high knowledge and technical skills required in the ICUs (Chang et al., 2011).
## Clinical implication
The subject of core competence continues to take top priority in Oman in line with the global paradigm shifts from volume to value as well as due to the sharply increasing expectations for ultra‐high‐care delivery (Undersecretariat for Planning Affairs, 2014).
This research has the following important implication dimensions: The excellent stance of the core competencies of HCPs in Oman using a comprehensive adopted instrument provide a good indicator of the overall healthcare system in Oman. The sustainability of such excellent levels could be the focus of the policy and the technical leadership at the Ministry of Health in Oman. The findings of the regression analysis can provide good insights for cross‐learning, upskilling and wider capacity building through establishing seamless avenues for younger professionals to learn from expatriates and experienced professionals. Implementation of research and practice EBP is a key competence that require further development. This competence can impact the capacity for renewal of healthcare institutions in Oman. The capacity for renewal is a measure of the readiness of an organization to be fully engaged, agile and futuristic to position itself in reference to the internal and external environments and transformations (Delaney, 2016; Salmond & Echevarria, 2017; WHO, 2017).
These implications directly impact the human resources at the Ministry of Health in Oman. Human resources constitute an important pillar of the health system in Oman, not only because they form more than $73\%$ of health expenditure but also because the quality of healthcare depends to a large extent on the quality of the workers (NCSI, 2020).
These implications will also provide important insights to the global community towards improving quality of care and patient safety from a core competence perspective.
## Strengths and limitations
This study used HPCCI instrument adopted from valid and reliable tools that was developed carefully and reviewed by experts in the nursing field and piloted in the healthcare setting. The response rate of this study was good ($70\%$).
The HPCCI scale was nevertheless based on a self‐assessment method that could influence the study's results. This suggests that HCPs' competence could be further verified through a complementing research work that utilizes observation‐based assessments and/or utilization of accredited competence consultants. It would also be very interesting to see how patients – as healthcare users – perceive such competence through the lens of healthcare quality and delivery.
## CONCLUSIONS
Assessing HCPs' core competence is important in the journey of healthcare transformation. This study has demonstrated that the self‐rated overall core competency level of HCPs (nurses and physicians) in Oman stand at an excellent level. This sets a good scene for healthcare leaders in Oman to drive transformation within the sector with great confidence. Nevertheless, the study revealed that research and EBP should be further enabled, resources skills/upskilled and outcomes introduced into practice.
Gender, ethnicity and working experience explained nurses' and physicians' overall core competencies. A subsequent research work is recommended to explore how the healthcare system can address these factors and may capitalize on possible opportunities such as the transfer of knowledge within the workplace and stimulation of cross learning. Developing HCPs' core competencies remain an important impetus to strengthen the human resource capabilities and to sustain a high level of quality patient outcomes.
## AUTHOR CONTRIBUTIONS
All authors listed meet the authorship criteria according to the latest guidelines of the International Council of Medical Journal Editors and that all authors are in agreement with the manuscript. All authors designed this study. FA conducted data collection. All authors analysed and interpreted the data. FA drafted the manuscript, and all authors contributed substantially to its revision. FA takes responsibility for the manuscript as a whole.
## FUNDING INFORMATION
The first author received a grant from Ministry of Health, Oman.
## CONFLICT OF INTEREST
No conflicts of interest have been declared by the authors.
## ETHICAL APPROVAL
An ethical statement was obtained from the University of Eastern Finland Committee on Research Ethics (Statement $\frac{16}{2018}$). In addition, an ethical statement including permission to conduct the study in the hospitals was obtained from the MOH in Oman (Proposal ID: MOH/CSR/18/XXXX). All data were treated confidentially, and the participants were allowed to withdraw at any time point.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request only from the corresponding author. The data are not publicly shared due to privacy and ethically restrictions.
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|
---
title: 'Electrical stimulation to regain lower extremity muscle perfusion and endurance
in patients with post‐acute sequelae of SARS CoV‐2: A randomized controlled trial'
authors:
- Alejandro Zulbaran‐Rojas
- Myeounggon Lee
- Rasha O. Bara
- Areli Flores‐Camargo
- Gil Spitz
- M. G. Finco
- Amir Behzad Bagheri
- Dipaben Modi
- Fidaa Shaib
- Bijan Najafi
journal: Physiological Reports
year: 2023
pmcid: PMC10006649
doi: 10.14814/phy2.15636
license: CC BY 4.0
---
# Electrical stimulation to regain lower extremity muscle perfusion and endurance in patients with post‐acute sequelae of SARS CoV‐2: A randomized controlled trial
## Abstract
Muscle deconditioning and impaired vascular function in the lower extremities (LE) are among the long‐term symptoms experienced by COVID‐19 patients with a history of severe illness. These symptoms are part of the post‐acute sequelae of Sars‐CoV‐2 (PASC) and currently lack evidence‐based treatment. To investigate the efficacy of lower extremity electrical stimulation (E‐Stim) in addressing PASC‐related muscle deconditioning, we conducted a double‐blinded randomized controlled trial. Eighteen ($$n = 18$$) patients with LE muscle deconditioning were randomly assigned to either the intervention (IG) or the control (CG) group, resulting in 36 LE being assessed. Both groups received daily 1 h E‐Stim on both gastrocnemius muscles for 4 weeks, with the device functional in the IG and nonfunctional in the CG. Changes in plantar oxyhemoglobin (OxyHb) and gastrocnemius muscle endurance (GNMe) in response to 4 weeks of daily 1 h E‐Stim were assessed. At each study visit, outcomes were measured at onset (t 0), 60 min (t 60), and 10 min after E‐Stim therapy (t 70) by recording ΔOxyHb with near‐infrared spectroscopy. ΔGNMe was measured with surface electromyography at two time intervals: 0–5 min (Intv1) and: 55–60 min (Intv2). Baseline OxyHb decreased in both groups at t 60 (IG: $$p \leq 0.046$$; CG: $$p \leq 0.026$$) and t 70 (IG = $$p \leq 0.021$$; CG: $$p \leq 0.060$$) from t 0. At 4 weeks, the IG's OxyHb increased from t 60 to t 70 ($p \leq 0.001$), while the CG's decreased ($$p \leq 0.003$$). The IG had higher ΔOxyHb values than the CG at t 70 ($$p \leq 0.004$$). Baseline GNMe did not increase in either group from Intv1 to Intv2. At 4 weeks, the IG's GNMe increased ($$p \leq 0.031$$), whereas the CG did not change. There was a significant association between ΔOxyHb and ΔGNMe ($r = 0.628$, $$p \leq 0.003$$) at 4 weeks in the IG. In conclusion, E‐Stim can improve muscle perfusion and muscle endurance in individuals with PASC experiencing LE muscle deconditioning.
This study indicates that self‐administered lower extremity (LE) electrical stimulation (E‐Stim) therapy is practical and effective at promoting the restoration of LE muscle perfusion and endurance in individuals with post‐acute sequelae of Sars‐CoV‐2 (PASC) who were previously hospitalized. The application of LE E‐Stim for 1 h daily over a 4 week period resulted in a significant increase in gastrocnemius muscle oxyhemoglobin levels, which led to an improvement in muscle endurance and recovery of excess postexercise oxygen consumption
## INTRODUCTION
The novel coronavirus disease 2019 (COVID‐19) pandemic has generated great illness, death, distress, and undefined sequelae on our society (Cucinotta & Vanelli, 2020). With vaccines and monoclonal therapies, moderate to severe cases of COVID‐19 infection diminished markedly (Hwang et al., 2022; Peng et al., 2021). However, COVID‐19 survivors that were inflicted with severe acute illness, in particular those who required prolonged bed rest, still suffer from post‐acute sequelae of Sars‐CoV‐2 (PASC) (Parker et al., 2021).
According to Centers for Disease Control and Prevention (Centers for Disease Control and Prevention, 2022), PASC can persist for up to 2 years after recovery (Huang et al., 2022), and even a mild course of acute infection may lead to long‐term disability (Taquet et al., 2021). Musculoskeletal sequelae are a key concern for clinicians treating PASC (Disser et al., 2020), as they can lead to debilitating outcomes for survivors who were hospitalized or immobilized for extended periods (de Andrade‐Junior et al., 2021; Nalbandian et al., 2021). Musculoskeletal sequelae are characterized by atrophy, weakness, pain, and fatigue, and since these issues are often located in the lower extremities (LE) (Heesakkers et al., 2022; Parry & Puthucheary, 2015), they can significantly impact daily activities. In particular, LE muscle weakness has been associated with reduced functional abilities in individuals with PASC (Shanbehzadeh et al., 2021).
The mechanism by which Sars‐CoV‐2 damages muscles is not yet fully understood. There is speculation that microcirculation deterioration may be a factor (Trinity et al., 2021), possibly resulting from vascular endothelial damage through ACE2 receptors (Amraei & Rahimi, 2020) or a viral‐induced hyper‐inflammatory state that can cause myofibrillar breakdown, mitochondrial dysfunction, and muscle degradation (Piotrowicz et al., 2021). Other studies suggest that during severe acute COVID‐19 infection, hyperlactemic states can lead to a deoxygenation of the musculoskeletal system, impairing the transportation of oxygen to distal tissues, and resulting in hypoxia/ischemia (Seixas et al., 2022). As a result, COVID‐19 patients who experienced severe illness have been shown to have lower vascular function and blood flow velocity, vessel inflammation, and arterial stiffness in the LE (Disser et al., 2020; Paneroni et al., 2021; Ratchford et al., 2021).
Physical therapy programs have been proposed for the management of musculoskeletal PASC (Righetti et al., 2020). However, they may not adequately address the vascular impairment induced by COVID‐19. Recent evidence suggests that individuals with PASC may experience marked hypoxia (Fuglebjerg et al., 2020; Singh et al., 2022) and a poor hemodynamic response to stress (HR2S) in the LE, which can affect exercise tolerance (Serviente et al., 2022). Waiting to engage in mobility programs may be detrimental (O'Sullivan et al., 2021), so safe, and effective solutions to improve HR2S are needed to support functional recovery in this population.
A number of studies support the effectiveness of electrical stimulation (E‐Stim) to improve LE vascular health (Gorgey et al., 2009; Hamid & Hayek, 2008; Li et al., 2017). E‐Stim involves the delivery of preprogrammed trains of stimuli to superficial muscles via adhesive pads, which can evoke submaximal muscle contractions by recruiting motor units in a nonselective, spatially fixed, and temporally synchronous pattern (Maffiuletti et al., 2019). This therapy has been shown to improve muscle endurance in hospitalized or limited‐mobility patients (Burgess et al., 2021), to reduce muscle loss (Burgess et al., 2021; Leite et al., 2018), and improve tissue perfusion (Zulbaran‐Rojas et al., 2021). Additionally, E‐Stim has been effective in improving muscle strength (Righetti et al., 2022) and endurance (Zulbaran‐Rojas et al., 2022) in severe acute COVID‐19 patients. However, the long‐term effects of E‐Stim on muscle perfusion have not been well studied, and its utility for the recovery of individuals with musculoskeletal PASC has not been explored.
Given the poor exercise tolerance and potential for unhealthy HR2S in individuals with PASC, safe and effective solutions for improving HR2S and supporting functional recovery are needed. Therefore, the purpose of our study was to investigate the potential benefits of E‐Stim in improving the recovery of individuals with musculoskeletal PASC. Our main hypothesis is that E‐Stim therapy will improve both HR2S and LE muscle endurance in this population. Moreover, we hypothesize that there will be a positive correlation between muscle perfusion and endurance, indicating that E‐Stim may improve both aspects of muscle function in individuals with musculoskeletal PASC.
## Study population
A double‐blinded randomized controlled trial of individuals experiencing persistent LE musculoskeletal PASC was conducted. Participants were recruited from the Baylor College of Medicine (BCM) Post‐COVID‐19 Care Clinic (Houston, TX, USA) between November 2021 and May 2022. All participants signed an informed consent approved by the local Institutional Review Board (IRB #H‐47781) before study enrollment. The study was registered on ClinicalTrials.gov (Identifier: NCT05198466) and followed the Consolidated Standards of Reporting Trials (CONSORT) guidelines for randomized clinical trials.
Participants were included if they were previously hospitalized due to acute COVID‐19 infection, aged 18–85 years old, diagnosed with PASC by a pulmonologist and critical care clinician (F.S., D.M), and reported persistent LE musculoskeletal symptoms such as atrophy, weakness, numbness, and/or pain at their first consultation. Those who had demand‐type cardiac pacemaker, implanted defibrillator, active wound infection, or below the knee amputation were excluded.
Demographic and clinical characteristics were recorded from the electronic medical records. Other baseline assessments included depression by the Center for Epidemiologic Studies Depression Scale (CES‐D) (Weissman et al., 1977), cognition by the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005), anxiety by the Beck Anxiety Inventory Scale (BAI) (Beck et al., 1988), pain by the visual‐analog‐scale (VAS) (Langley & Sheppeard, 1985), quality‐of‐life by the Patient‐Reported Outcomes Measurement Information System (PROMIS) (Cella et al., 2010), sleep quality by Pittsburgh‐Sleep‐Quality‐Index (PSQI) (Buysse et al., 1989), and activity of daily living by the Katz Index and Lawton scale (Katz, 1983; Lawton & Brody, 1969).
## Randomization, group allocation, and intervention
Participants were randomized (ratio: 1:1) to either control (CG) or intervention (IG) groups through a computer‐generated list followed by sequential allocation. Participants and care providers were blinded to the group allocation. Investigators who collected and analyzed the data were not blinded. The IG received E‐Stim to the gastrocnemius muscle (GNM) via four electrode adhesive pads (Avazzia Inc), two placed on each leg. One pad was placed on the proximal GNM (Silva et al., 2017) while the other was placed on the Achilles tendon. A four‐pin lead wire was used to connect the E‐Stim device (Tenant Biomodulator®) to the electrode pads in both legs simultaneously. The CG was provided with an identical, but nonfunctional device (sham). Participants were instructed to self‐manage daily 1 h E‐Stim therapy at a time of their convenience to both LE for a course of 4 weeks. Weekly support phone calls by research assistants (A.Z., R.B., A.F.) were performed to monitor adherence. There were no lifestyle or dietary restrictions needed to apply E‐Stim during the study period.
E‐stim application was delivered by an interactive high voltage pulsed alternative current (HVPAC) in the shape of an asymmetrical damped sinusoidal biphasic pulsed waveform (Senergy Medical Group, n.d.), which allows muscle relaxation and avoids fatigue during therapy (Zulbaran‐Rojas et al., 2021). E‐Stim pulse duration was set between 400 and 1400 microseconds (μs), with a pulse frequency between 20 and 121 hertz (Hz). The E‐Stim sham device did not elicit electrical currents.
## Procedures and outcome measures
Outcomes were measured at the BCM Post‐COVID‐19 Care Clinic at baseline and 4 weeks visits during regular work hours (9:00 a.m. – 5:00 p.m.). Upon arrival to the hospital, participants were located on a regular exam chair in Fowler's position (60 degrees) with the legs extended (Figure 1). After resting for 5–10 min, approximate real‐time muscle‐perfusion was measured in response to 1 h E‐Stim therapy using a validated near‐infrared‐spectroscopy (NIRS) camera (Snapshot NIR, KENT Imaging Inc.). Oxyhemoglobin (OxyHb, defined as % of oxygenated hemoglobin) (Barstow, 1985) was obtained from the distal foot by tracing the metatarsal area including the five toes. From an exercise perspective, OxyHb allows for calculation of muscular efficiency/work executed by the muscle, the amount of oxygen consumption to produce a certain amount of work, and the velocity of muscle recovery after the work has ceased (Parker, 2021; Steinberg, 2022). When oxygen consumption is constant during steady‐state levels (i.e., isometric muscle contraction for 1 h), changes in NIRS signals should primarily reflect changes in oxygen delivery or uptake of a specific area (Fadel et al., 1985). However, when oxygen consumption surpasses the muscle supply during activity, the levels of OxyHb decrease (Beerthuizen, 1993). Under this concept, pictures were collected at three different time points within the baseline and 4‐weeks visits: [1] pre‐therapy, t 0 (0 min) to record steady‐state basal levels, [2] end‐of‐therapy, t 60 (60 min) to assess oxygen consumption (Dobson & Gladden, 1985), and [3] 10 min after stopping therapy, t 70 to assess the reperfusion period (Meixner et al., 2022; Meneses et al., 2020) or HR2S.
**FIGURE 1:** *Study setup: electrical stimulation device, plugs and pads, and surface electromyography sensors. Participants received electrical stimulation through electrode adhesive pads placed on both proximal and distal gastrocnemius muscles using a bioelectric stimulation technology® (BEST) micro‐current platform (Tennant Biomodulator®). E‐Stim was active in the intervention group and nonfunctional in the control group. Two surface electromyography (Delsys Trino Wireless EMG System) sensors were placed on the proximal lateral gastrocnemius of each lower extremity to evaluate muscle endurance in response to E‐Stim. sEMG, surface electromyogram; E‐stim, electrical stimulation.*
During the 1 h E‐Stim session, changes in GNM endurance (GNMe, defined as sustained muscle involuntary contraction (Hagberg, 1981)) were recorded using surface electromyography (sEMG) at two time point intervals: [1] 0–5 min (Interval 1, Intv1), indicating therapy start; and [2] 55–60 min (Interval 2, Intv2), indicating end of therapy.
To evaluate GNMe, two sEMG sensors (Delsys Trino Wireless EMG System) were placed vertically next to each other at the lateral proximal GNM of each leg according to sEMG for a Non‐Invasive Assessment of Muscles (SENIAM) guidelines (Hermens et al., 2000). The sEMG data were collected at 2000 Hz and the raw sEMG signal was filtered using a fourth‐order Butterworth band‐pass filter with cutoff frequencies of 20 and 350 Hz from each sensor. Then, the sensor with less noise was used to quantify GNMe in response to E‐stim, the other sensor was discarded. Integrated EMG (iEMG) was calculated (Medved, 1999; Truong Quang Dang et al., 2012) to quantify the amount of muscle activation by motor units (Sleivert & Wenger, 1994). Then, iEMG was normalized by the average iEMG value extracted during the trial to compare the iEMG values in the baseline and 4 week visits (Allison et al., 1993; Morris et al., 1998).
## Safety, feasibility, and acceptability
For patient safety, body saturation of oxygen (SatO2) was measured pre‐ and during therapy using a pulse oximeter (Santamedical Dual Color OLED) to monitor exercise‐induced (silent) hypoxia (Fuglebjerg et al., 2020; Rahman et al., 2021). Protocol delivery was set as ≥$80\%$, accrual recruitment (≥2 patients/month), and ≥$80\%$ outcome measuring (Kho et al., 2019). Device acceptability was set as ≥$80\%$ assessed by ease of use questions based on a technology acceptance model questionnaire (Venkatesh & Davis, 2000). Moreover, adverse events throughout the study such as pain, skin damage, and discomfort were documented.
After 4 weeks of E‐Stim therapy, both groups showed no change in SatO2 values in response to 1 h E‐Stim (IG, t 0: $97.6\%$ vs. t 60: $97.6\%$, $$p \leq 0.7$$; CG: t 0: $97.8\%$ vs. t 60: $97.14\%$, $$p \leq 0.33$$, $d = 0.34$). There was a $100\%$ protocol delivery (no dropouts), accrual recruitment of 4–5 patients/month, $100\%$ outcome measuring (no missed baseline or 4 week visits), and $0\%$ device‐related adverse events. Both groups scored an average $92.8\%$ on ease of therapy self‐administration (strongly agreed on ease of use for pads placement and device operation).
## Sample size justification
Power analysis was conducted to calculate the minimum sample size using G*power software (version of 3.1.6) as follows: [1] Moderate effect size (Cohen's $d = 0.5$), [2] $80\%$ power, [3] Alpha of $5\%$, [4] two number of groups, [5] three repeated measurements, and [6] 0.5 correlation among the repeated measurements. As a result, 28 samples were required. However, considering a dropout rate of up to $10\%$, a total of 32 samples were required to detect significance.
## Statistical analysis
Each LE was considered as an independent sample due to the variability in muscular and vascular status (Häkkinen et al., 1997; Khan et al., 2019) as well as muscle strength asymmetry, dominance, and length discrepancy (Knutson, 2005; Laroche et al., 2012; Sadeghi et al., 2000). Shapiro–Wilk test was used to assess data normality ($p \leq 0.05$). Independent t‐tests, Chi‐square or Mann–Whitney U tests were used to compare baseline characteristics between groups. Effect size was measured using Cohen's d. Generalized Estimating Equations (GEE) was performed to assess the group*time interaction effect at baseline and 4 weeks represented by estimated means and standard errors. E‐Stim effect on GNMe (i.e., Intv1 and Intv2) and OxyHb (i.e., t 0, t 60, and, t 70) were assessed within and between groups. Normalized GNMe and OxyHb values at each time point within the 1 h E‐Stim session were estimated having the first time point (i.e., t 0 or Intv1) as $0\%$ reference (i.e., [GNMe at Intv1 – GNMe at Intv1]/[GNMe at Intv1] * 100; [OxyHb at t 0 − OxyHb at t 0]/[OxyHb at t 0] * 100) to all other time points (i.e., [GNMe at Intv2 − GNMe at Intv1]/[GNMe at Intv1] * 100; [OxyHb at t 60 or t 70 − OxyHb at t 0]/[OxyHb at t 0] * 100). Results adjusted to potential confounders are included in the Supplementary Material. To compare the treatment effect at 4 weeks (i.e., active, sham), Delta (Δ) values of gastrocnemius muscle endurance GNMe (i.e., ΔGNMe = GNMe at Intrv2 − GNMe at Intrv1) and OxyHb (i.e., ΔOxyHb = OxyHb at t 70 − OxyHb at t 0) were calculated according to the E‐Stim duration (t 0 ‐t 60) and additional reperfusion period (t 0 ‐t 70), respectively. Pearson's correlation analysis was performed to explore the association between ΔGNMe and ΔOxyHb. All statistical analyses were performed using SPSS 28.0 (IBM), and the statistical significance level was set at p ≤ 0.05.
## Clinical characteristics
Figure 2 illustrates the Consort flow diagram, outlining the recruitment and participation of study participants. Nineteen individuals initially met the inclusion and exclusion criteria; however, one participant withdrew from the study before baseline assessment due to time constraints. This led to a total of 18 participants (Age: IG = 51.10 ± 9.86 years, CG = 52.38 ± 7.44 years, $$p \leq 0.760$$; persistency of symptoms after clearance of acute infection: IG = 295.60 ± 224.92 days, CG = 304.50 ± 179.45 days, $$p \leq 1.0$$) including $$n = 20$$ LE in the IG and $$n = 16$$ LE in CG. Baseline clinical characteristics revealed that the IG had a higher incidence of pneumonia during COVID‐19 acute infection ($$p \leq 0.043$$), and higher levels of oxygen at home ($$p \leq 0.040$$) than the CG. Additionally, the IG had lower BMI ($$p \leq 0.016$$) and poorer cognitive function ($$p \leq 0.014$$), while other characteristics did not exhibit significant differences between groups (Table 1).
**FIGURE 2:** *Patient flowchart. N, number of patients; n, number of lower extremities.* TABLE_PLACEHOLDER:TABLE 1
## Muscle perfusion outcomes
At baseline, both groups showed a decrease in OxyHb between t 0 and t 60 (IG: 0.56 ± $0.02\%$ vs. 0.55 ± $0.01\%$, $$p \leq 0.046$$, $d = 0.145$; CG: 0.61 ± $0.02\%$ vs. 0.58 ± $0.01\%$, $$p \leq 0.026$$, $d = 0.490$) and between t 0 and t 70 (IG: 0.53 ± $0.01\%$, $$p \leq 0.021$$, $d = 0.520$; CG: 0.58 ± $0.01\%$, $$p \leq 0.060$$, $d = 0.423$, Figure 3a). The IG showed lower OxyHb at t 70 ($$p \leq 0.004$$, $d = 1.204$) compared to the CG. Group × time × effect interaction was not significant between groups ($$p \leq 0.179$$, Wald Chi‐square = 3.436). Normalized OxyHb values showed a similar but nonsignificant decline between t 0 and t 60 (IG: −2.02 ± $1.27\%$, $$p \leq 0.113$$, $d = 0.516$; CG: −4.20 ± $2.58\%$, $$p \leq 0.103$$, $d = 0.594$) and between t 0 and t 70 (IG: −4.79 ± $2.62\%$, $$p \leq 0.067$$, $d = 0.593$; CG: −3.64 ± $2.72\%$, $$p \leq 0.181$$, $d = 0.489$) in both groups (Figure 3b). Group × time × effect interaction was not significant between groups ($$p \leq 0.314$$, Wald Chi‐square = 2.316) for normalized OxyHb values at baseline.
**FIGURE 3:** *Oxyhemoglobin comparison at baseline and 4 weeks within and between groups. Oxyhemoglobin, OxyHb; E‐Stim, electrical stimulation; HR2S, hemodynamic response to stress; min, minutes; Δ, Delta. Generalized Estimating Equations were performed to assess the group × time × effect interaction of E‐Stim over OxyHb at 0, 60, and 70 min within and between groups. Baseline (a) Absolute and (b) Normalized to 0% change ΔOxyHb values (e.g., [OxyHb at t
0 − OxyHb at t
60 or t
70]/[OxyHb at t
0] * 100) in each time point. (c) A typical case of a patient in the intervention group showing a continuous decrease of OxyHb values after stopping 1 h E‐Stim for 10 min (70 min). Four weeks (d) Absolute and (e) Normalized to 0% change ΔOxyHb values (e.g., [OxyHb at t
0 − OxyHb at t
60 or t
70]/[OxyHb at t
0] * 100) in each time point. (f) A typical case of a patient from the intervention group showing a regain of OxyHb values after stopping 1 h E‐Stim for 10 min (70 min). * Statistically significant (p ≤ 0.05).*
After 4 weeks of intervention, both groups showed a decrease in OxyHb between t 0 and t 60 (IG: 0.58 ± $0.02\%$ vs. 0.55 ± $0.02\%$, $p \leq 0.001$, $d = 0.402$; CG: 0.59 ± $0.02\%$ vs. 0.55 ± $0.02\%$, $$p \leq 0.003$$, $d = 0.488$). However, at t 70, the IG showed a significant increase in OxyHb compared to t 60 (0.57 ± $0.02\%$, $$p \leq 0.040$$, $d = 0.334$), contrary to the CG, which continued to decline (0.54 ± $0.02\%$, $p \leq 0.001$, $d = 0.632$, Figure 3d). Group × time × effect interaction was significant between groups ($$p \leq 0.022$$, Wald Chi‐square = 7.639). Normalized OxyHb values in both groups showed a decrease in OxyHb between t 0 and t 60 (IG: −4.71 ± $1.39\%$, $p \leq 0.001$, $d = 1.099$; CG: −5.47 ± $2.01\%$, $$p \leq 0.006$$, $d = 0.993$). However, at t 70, the IG showed a significant increase in OxyHb compared to t 60 (0.62 ± $1.93\%$, $$p \leq 0.037$$, $d = 0.558$), contrary to the CG, which continued to decline (−7.06 ± $1.78\%$, $p \leq 0.001$, $d = 1.448$, Figure 3e). The IG showed higher OxyHb at t 70 ($$p \leq 0.004$$, $d = 0.828$) compared to the CG. Group × time × effect interaction was significant between groups ($$p \leq 0.022$$, Wald Chi‐square = 7.592) for normalized values at 4 weeks. Similar results were seen for muscle perfusion adjusted to potential confounders (Table S1).
## Muscle endurance outcomes
At baseline, neither group showed improvement in GNMe. The IG's GNMe did not change between Intv1 and Intv2 (360.84 ± 1.79 vs. 359.81 ± 0.97, $$p \leq 0.413$$, $d = 0.164$), while the CG showed a decline (364.36 ± 1.41 vs. 360.16 ± 1.78, $$p \leq 0.030$$, $d = 0.675$, Figure 4a). Group × time × effect interaction was not significantly different ($$p \leq 0.171$$, Wald Chi‐square = 1.871). Similar declining trends were observed for normalized GNMe values between Intv1 and Intv2 in the IG (−0.26 ± $0.35\%$, $$p \leq 0.465$$, $d = 0.241$) and the CG (−1.14 ± $0.53\%$, $$p \leq 0.032$$, $d = 0.785$, Figure 4b). No significant group × time × effect interaction was found for normalized GNMe values at baseline ($$p \leq 0.167$$, Wald Chi‐square = 1.909).
**FIGURE 4:** *Gastrocnemius muscle endurance at baseline and 4 weeks within and between groups. iEMG, integrated surface electromyogram unit; Intv1, Interval 1 (0–5 min); Intv2, Interval 2 (55–60 min); Δ: delta. Generalized Estimating Equations were performed to assess the group × time × effect interaction of E‐Stim over GNMe (i.e., Intv1 and Intv2) within and between groups. Baseline (a) Absolute and (b) Normalized to 0% change Δ GNMe values ([GNMe at Intrv2 − GNMe at Intrv1]/[GNMe at Intrv1] * 100) in each time point. Four weeks (c) Absolute and (d) Normalized to 0% change Δ GNMe values ([GNMe at Intrv2 − GNMe at Intrv1]/[GNMe at Intrv1] * 100) in each time point. * Statistically significant (p ≤ 0.05).*
After 4 weeks of intervention, the IG exhibited a significant increase in GNMe between Intv1 and Intv2 (359.88 ± 2.06 vs. 363.04 ± 1.56, $$p \leq 0.031$$, $d = 0.397$), while no significant changes were observed in the CG (360.50 ± 2.74, vs. 359.55 ± 2.04, $$p \leq 0.522$$, $d = 0.102$, Figure 4c). A significant group × time × effect interaction was found ($$p \leq 0.048$$, Wald Chi‐square = 3.893). Normalized GNMe values also increased between Intv1 and Intv2 in the IG (0.91 ± $0.42\%$, $$p \leq 0.029$$, $d = 0.703$), whereas no significant changes were observed in the CG (−0.23 ± $0.42\%$, $$p \leq 0.592$$, $d = 0.200$, Figure 4d). The IG exhibited a higher trend than the CG in GNMe at Intv2 ($$p \leq 0.055$$, $d = 0.654$). There was a trend for a group × time × effect interaction ($$p \leq 0.055$$, Wald Chi‐square = 3.674) for normalized GNMe values at 4 weeks. Similar results were observed for muscle endurance adjusted to potential confounders (Table S2).
## Association of distal lower extremity perfusion and GNM endurance
After 4 weeks of E‐Stim therapy, a significant correlation was observed between ΔOxyHb and ΔGNMe ($r = 0.628$, $$p \leq 0.003$$) in the IG. Such correlation was not observed in the CG ($r = 0.120$, $$p \leq 0.657$$, Figure 5).
**FIGURE 5:** *Four weeks correlation comparison between ΔOxyHb and ΔGNMe in both groups. Δ, Delta; OxyHb, Oxyhemoglobin; GNMe, gastrocnemius muscle endurance; ΔGNMe was calculated as GNMe at Intrv2 (55–60 min) − GNMe at Intrv1 (0–5 min); and ΔOxyHb was calculated as OxyHb at t
70 − OxyHb at t
0. The association between ΔGNMe and ΔOxyHb was explored with Pearson's correlation analysis.*
## DISCUSSION
This study investigated the efficacy of daily self‐administered E‐Stim in promoting LE muscle recovery and improving muscle perfusion and endurance in individuals with LE musculoskeletal PASC. This study utilized NIRS and sEMG assessments and found that after a 4 week intervention period of daily 1 h E‐Stim sessions, participants demonstrated a significant increase in both muscle perfusion and endurance. The findings of this study suggest that self‐administered E‐*Stim is* a safe and effective therapeutic option for individuals with LE musculoskeletal PASC seeking to improve muscle recovery.
During dynamic muscle‐stress (e.g., exercise), NIRS signals from the muscle tissue reflect myoglobin (Mb) (Bendahan et al., 2017; Davis & Barstow, 2013), a globular protein that stores oxygen intracellularly in the muscles (Meyer, 2004). Consequently, a decrease in muscle oxygen levels (i.e., OxyHb) indicates an increase in intracellular oxygen consumption (Van Beekvelt et al., 2001). In this study, we investigated changes in OxyHb by inducing 1 h of submaximal contraction (E‐Stim) to the GNM. The baseline assessment showed a significant drop in OxyHb in response to 1 h E‐Stim in both groups, indicating an increase in oxygen consumption due to continuous muscle activation.
In healthy individuals, muscle tissue typically shows an immediate recovery of oxygen levels after cessation of exercise‐induced stress (healthy HR2S) (Barron et al., 1997; Meixner et al., 2022; Meneses et al., 2020). This phenomenon, known as excess postexercise oxygen consumption (Børsheim & Bahr, 2003), represents the muscle's attempt to repay the oxygen “debt” incurred during prolonged contractions (Barron et al., 1997; Shang et al., 2013). Cettolo et al. [ 2007] observed that this recovery is slower in people with sedentary lifestyles. Notably, our study found that individuals with musculoskeletal PASC who had been hospitalized failed to show any such recovery, as none of the participants demonstrated an increase in OxyHb toward basal levels (Impaired HR2S at t 70, Figure 3a).
One important consideration is the heterogeneous composition of slow‐ and fast‐twitch fibers in the GNM, which have different oxygen metabolizing capabilities dependent on their levels of Mb and mitochondria (Edgerton et al., 1975). Slow‐twitch fibers have higher levels of both components, resulting in a larger NIRS signal capture (Jansson & Sylvén, 1983). Conversely, fast‐twitch fibers produce greater force but are quicker to fatigue and require longer recovery times (Lievens et al., 1985). When fast‐twitch fibers are pushed beyond their failure point, slow‐twitch fibers take over to continue muscle contraction, indicating that after 1 h of E‐Stim, the majority of the OxyHb NIRS signal after 10 min from stopping E‐Stim therapy (t 60–t 70) represents Mb and mitochondrial recovery (Schmitz, 2013). In COVID‐19 patients with a history of severe illness, there is myofibrillar breakdown related to mitochondrial autophagy (Piotrowicz et al., 2021). Baratto et al. suggested that this could lead to impaired muscle oxygen extraction (Baratto et al., 2021), which may be one reason for the observed baseline dysfunctional muscle HR2S in all participants of this study.
The evidence on post‐COVID‐19 exercise‐induced hypoxia in previously hospitalized patients is compelling. A recent cohort study of 26 hospitalized patients found a $50\%$ incidence of hypoxia during a 6 min walking test prior to discharge (Fuglebjerg et al., 2020). Other randomized studies on post‐COVID‐19 patients who underwent mild exercise showed impaired systemic oxygen extraction (Singh et al., 2022), and peripheral muscle oxygen extraction compared to controls (Baratto et al., 2021). Longobardi et al. [ 2022] suggested that peripheral metabolic factors affected by COVID‐19 may impair the rate at which oxygen uptake adjusts to changes in energy. While it is unclear whether this impairment is related to mitochondrial dysfunction, some studies suggest that prolonged periods of muscle inactivity, such as those experienced during hospitalization or bed rest, can worsen mitochondrial conditions (Faist et al., 2001; Powers et al., 2012). Taken together, these findings suggest that exercise may induce hypoxia in post‐COVID‐19 patients previously hospitalized, leading to rapid tissue oxygen desaturation.
To investigate the possible role of peripheral oxygen as a marker of muscle perfusion impairment or improvement in individuals with musculoskeletal PASC, we reexamined HR2S at 4 weeks for both groups. Consistent with previous findings (Hansen et al., 2000), both the IG and CG showed a similar drop in OxyHb at t 60. However, when the E‐Stim was stopped for 10 min, only the IG showed a recovery in OxyHb (Figure 3d,e). It has been previously reported that muscle activity can stimulate mitochondrial respiration (Tonkonogi et al., 1998), and enhanced mitochondrial capacity has been linked to endurance training, whether physical (Daussin et al., 2008; Porter et al., 2015) or through E‐Stim therapy (Daussin et al., 2008; Porter et al., 2015). Therefore, we speculate that the 4 week continuous muscle activation induced by E‐Stim therapy might have enhanced the mitochondrial recovery of gastrocnemius myocytes in the IG, leading to a reperfusion reaction similar to that observed in healthy subjects (Barron et al., 1997; Meneses et al., 2020; Shang et al., 2013). However, further studies are necessary to confirm this speculation.
An additional objective measure to assess the improvement in muscle endurance is through iEMG analysis (Zulbaran‐Rojas et al., 2022), which reflects increased muscle fiber activation (Cettolo et al., 2007). In our overall cohort, at baseline, we observed a decline in muscle endurance during the 1 h E‐Stim session (Figure 4a), which was expected given the impaired oxygen metabolism seen in PASC patients, leading to faster muscle fatigue (Nosaka et al., 2011). Previous studies have reported that muscle fatigue can be reduced after 2–4 weeks of E‐Stim therapy as a result of muscle adaptation to induced muscle damage (Clarkson et al., 1992; McHugh, 2003). Consistent with these findings, our study demonstrated that the intervention group had increased muscle endurance after 4 weeks of E‐Stim, in response to continuous muscle contraction. This suggests that only the intervention group regained muscle endurance as a response to E‐Stim therapy. Our results are also in line with our previous study, which reported improved muscle endurance in response to lower extremity E‐Stim therapy (Zulbaran‐Rojas et al., 2022).
Recent studies suggest that endurance training may lead to an increase in capillary density (Hirai et al., 2015; Hudlická et al., 1982; McGuire & Secomb, 2003) and angiogenesis in the LE in as little as 4 weeks (Hoier et al., 2012). However, post‐COVID rehabilitation guidelines recommend that patients with PASC limit their physical activity, making recovery of muscle deterioration challenging (Barker‐Davies et al., 2020). Fortunately, recent reviews suggest that E‐Stim therapy can improve muscle endurance (Nussbaum et al., 2017) and perfusion (Burgess et al., 2021), similar to light‐intensity exercise. In preclinical studies, E‐Stim has also been shown to induce angiogenesis in as little as 2 days (Clemente & Barron, 1993). Our study found that the IG demonstrated an association (Figure 5b) between an increase in GNM endurance in response to 1 h E‐Stim (Δ0‐60 min) and a greater recovery of OxyHb (Δ0‐70 min) after 4 weeks of therapy. This suggests that an increase in GNM endurance can lead to a higher recovery of muscle perfusion. Therefore, E‐Stim may be an effective therapy for improving muscle endurance and recovering a healthy HR2S (Hendrickse & Degens, 2019). However, as the study did not assess tissue samples or biopsies, the mechanism underlying this effect, such as angiogenesis, cannot be confirmed.
While this study used NIRS imaging of the plantar foot muscles rather than the gastrocnemius, it is important to note that this design does not necessarily introduce a confounding factor. Many exercises that involve the calf muscle, such as running, cycling, and calf raises, also involve muscular contribution from the foot muscles. However, since E‐Stim precludes muscular contributions outside the gastrocnemius, it is reasonable to assume that most changes observed pre‐ versus post‐E‐Stim therapy are attributable to the gastrocnemius itself. Nonetheless, it should be acknowledged that a limitation of this study is the lack of NIRS imaging specifically targeting the gastrocnemius muscle.
## Study limitations
This study has some limitations that should be considered when interpreting the results. First, the sample size may not be large enough to confirm all observations, and future larger studies are warranted to examine potential differences among COVID‐19 variants or measure specific indicators of muscle damage. Second, functional outcomes were not assessed, and exercise‐induced hypoxia was not measured with cardiopulmonary exercise testing. Additionally, future studies could directly measure OxyHb from the GNM or assess other LE muscles in addition to the GNM. Third, physiologic changes were based on clinical observations, and histologic studies are needed to test angiogenesis or intracellular changes. Fourth, three patients in the CG recognized they had a sham device during the study, but they were not unblinded. Fifth, adherence to therapy and compliance were monitored by weekly phone calls, but no objective or device‐tracking method was used. Finally, baseline parameters were significantly different in pneumonia during acute infection, and oxygen at home, suggesting that the IG was more ill than the CG. Despite these limitations, we observed medium to large effect sizes for the benefit of E‐Stim, which was safe, easy to administer, and highly acceptable. Future efforts are needed to confirm or refute the initial compelling findings of this study.
## Interpretation
Our study investigated the safety and potential benefits of a 4 week self‐administered E‐Stim therapy program in individuals with musculoskeletal PASC LE symptoms who were previously hospitalized. We found that a daily 1 h session of E‐Stim was both safe and well‐tolerated and may lead to improved muscle perfusion and endurance. Furthermore, we observed a potential benefit for GNM vascular improvement leading to a healthier HR2S. These findings suggest that E‐Stim therapy is a practical and promising intervention for individuals with musculoskeletal PASC LE symptoms seeking to improve their functional recovery.
## AUTHOR CONTRIBUTIONS
BN and AZ conceived and designed research; AZ, RB, and AF performed experiments; ML analyzed data; BN, AZ, AF, MGF, and GS interpreted results of experiments; AZ, ML, MGF, and AB prepared figures; AZ, RB, AF, ML, and GS drafted the article; BN, MGF, FS, AB, and DM edited and revised the article; AZ, ML, RB, AF, MGF, SG, AB, DM, FS, and BN approved final version of the article.
## FUNDING INFORMATION
This study was supported in part by Avazzia Inc. (DL, TX, US), which is the manufacturer of the Tennant Biomodulator®. The sponsor did not have any decision or contribution to the review, approval, and submission of this article.
## CONFLICT OF INTEREST STATEMENT
No conflicts of interest, financial or otherwise, are declared by the authors.
## ETHICS STATEMENT
This study was approved by the local Institutional Review Board (IRB #H‐47781) at Baylor College of Medicine. All participants signed the informed consent form before study enrollment. The study was registered on ClinicalTrials.gov (Identifier: NCT05198466) and followed the Consolidated Standards of Reporting Trials (CONSORT) guidelines for randomized clinical trials. The study was conducted in compliance with the Declaration of Helsinki.
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---
title: Short‐term semaglutide treatment improves FGF21 responsiveness in primary hepatocytes
isolated from high fat diet challenged mice
authors:
- Jia Nuo Feng
- Weijuan Shao
- Tianru Jin
journal: Physiological Reports
year: 2023
pmcid: PMC10006666
doi: 10.14814/phy2.15620
license: CC BY 4.0
---
# Short‐term semaglutide treatment improves FGF21 responsiveness in primary hepatocytes isolated from high fat diet challenged mice
## Body
New and NoteworthyThe novel GLP‐1/FGF21 axis has been suggested to play an important role in mediating beneficial effects of GLP‐1‐based drugs in obese subjects. We show here that 7‐day semaglutide treatment stimulated hepatic FGF21 expression and improved FGF21 sensitivity. Our findings have deepened our mechanistic understanding on functions of GLP‐1‐based drugs and the pathophysiological importance of the GLP‐1/FGF21 axis.
## Abstract
Metabolic functions of GLP‐1 and its analogues have been extensively investigated. In addition to acting as an incretin and reducing body weight, we and others have suggested the existence of GLP‐1/fibroblast growth factor 21 (FGF21) axis in which liver mediates certain functions of GLP‐1 receptor agonists. In a more recent study, we found with surprise that four‐week treatment with liraglutide but not semaglutide stimulated hepatic FGF21 expression in HFD‐challenged mice. We wondered whether semaglutide can also improve FGF21 sensitivity or responsiveness and hence triggers the feedback loop in attenuating its stimulation on hepatic FGF21 expression after a long‐term treatment. Here, we assessed effect of daily semaglutide treatment in HFD‐fed mice for 7 days. HFD challenge attenuated effect of FGF21 treatment on its downstream events in mouse primary hepatocytes, which can be restored by 7‐day semaglutide treatment. In mouse liver, 7‐day semaglutide treatment stimulated FGF21 as well as genes that encode its receptor (FGFR1) and the obligatory co‐receptor (KLB), and a battery of genes that are involved in lipid homeostasis. In epididymal fat tissue, expressions of a battery genes including Klb affected by HFD challenge were reversed by 7‐day semaglutide treatment. We suggest that semaglutide treatment improves FGF21 sensitivity which is attenuated by HFD challenge.
we revealed in this study that short‐term semaglutide treatment stimulates liver FGF21 expression and the sensitivity of FGF21.
## INTRODUCTION
Incretins are defined as gut‐produced hormones that function to augment insulin secretion from pancreatic β cells in a glucose‐concentration‐dependent manner (Tian & Jin, 2016). Glucagon‐like peptide‐1 (GLP‐1), produced by gut endocrine L cells, was recognized as the 2nd incretin in the middle of the 1980s (Baggio & Drucker, 2007; Holst, 2007; Kieffer & Francis Habener, 1999; Muller et al., 2019; Pederson & McIntosh, 2016; Petersen & Shulman, 2018; Tian & Jin, 2016). Following the recognition that GLP‐1 serves as an incretin, various incretin or GLP‐1‐based drugs including GLP‐1 receptor (GLP‐1R) agonists (GLP‐1RAs) and dipeptidyl peptidase‐4 inhibitors (DPP‐4i) have been developed for treating type 2 diabetes (T2D). Among them, liraglutide (Victoza®) and semaglutide (Ozempic®) are now FDA‐approved therapeutic agents for both T2D and chronic weight management (Deacon, 2020; Garber, 2011; Hinnen, 2017). Pieces of puzzles are still missing to explain how beneficial effects of them can be seen in subjects with severe insulin resistance if GLP‐1RAs purely act as incretin. Efforts have been made in numerous studies, showing the existence of extra‐pancreatic functions of GLP‐1 and GLP‐1RAs (Chiang et al., 2013, 2014; Chiang & Jin, 2014; Ip et al., 2012, 2013, 2015; Jin, 2016; Jin & Weng, 2016; Shao et al., 2015, 2020; Shao, Wang, Chiang, et al., 2013; Shao, Wang, Ip, et al., 2013; Zhou et al., 2020).
GLP‐1R is expressed in tissues including pancreas, lung, heart, gastric intestinal (GI) tract, brain, and kidney, which allows GLP‐1 or GLP‐1RAs to exert their metabolic and other effects directly (Pang et al., 2022; Viby et al., 2013; Zhou et al., 2020). In white adipose tissues (WAT), the effect of GLP‐1 or GLP‐1RAs could be mediated by a small portion of GLP‐1R+ cells of stromal vascular fraction (SVF), or certain lymphocytes or endothelial cells that do express GLP‐1R (Gu et al., 2022; McLean et al., 2021). Although hepatic functions of GLP‐1 and GLP‐1Rs on both glucose and lipid homeostasis have been broadly recognized, it is unlikely that GLP‐1R is expressed in hepatocyte (Jin & Weng, 2016; Liu et al., 2021; Panjwani et al., 2013). Hence, GLP‐1 and GLP‐1RAs may exert their hepatic functions either indirectly or via a small portion of GLP‐1R+ cells in the liver that are hematopoietic or endothelial origins (Jin & Weng, 2016; Liu et al., 2021; McLean et al., 2021).
Although the gene that encodes fibroblast growth factor 21 (FGF21) can be detected in mouse liver, adipose tissues, pancreatic islets, and elsewhere, circulating FGF21 is considered liver driven (Badakhshi & Jin, 2021). It is defined as metabolic hormone due to the lack of the conventional heparin‐binding domain. Since the discovery of FGF21, extensive investigations have been conducted on determining its role in metabolic homeostasis. FGF21 mediates its function through the binding of the heterodimeric receptor complex comprising mainly FGF receptor 1 or 3 (FGFR1 or FGFR3) and the obligatory co‐receptor, β‐klotho (KLB; Badakhshi & Jin, 2021; Geng et al., 2020; Shao et al., 2022). Metabolic beneficial effects of FGF21 have been established in both pre‐clinical investigations and in various clinical trials (Badakhshi & Jin, 2021; Shao & Jin, 2022). FGF21 knockout (KO) mice fed with ketogenic diet demonstrated impaired glucose tolerance with fatty liver and altered hepatic gene expression. Replenish FGF21 KO mice with human recombinant FGF21 (hFGF21) can attenuate dietary challenge‐induced metabolic impairment (Badman et al., 2009; Li et al., 2018).
Studies have shown that in various rodent models, GLP‐1RAs can positively regulate hepatic FGF21 production (Lee et al., 2014; Liu et al., 2019; Nonogaki et al., 2014; Yang et al., 2012). We have reproduced such observation and demonstrated that indeed GLP‐1R is not expressed in mouse liver, that in GLP‐1R KO mice, liraglutide virtually lost its metabolic beneficial effect and cannot stimulate liver FGF21 expression, and that in liver‐specific FGF21 KO mice, metabolic beneficial effects of liraglutide were severely attenuated (Liu et al., 2021). We hence suggest that this novel GLP‐1/FGF21 axis is patho‐physiologically important (Liu et al., 2021). In a more recent follow‐up study, we compared the effect of liraglutide and semaglutide on hepatic FGF21 expression in HFD‐challenged mice. Surprisingly, semaglutide, the long‐term effective GLP‐1RA showed no effect on stimulating hepatic FGF21 expression, although its metabolic homeostatic effects are highly appreciable (Liu et al., 2022). We hence wonder whether semaglutide can also improve FGF21 sensitivity or responsiveness and hence triggers a negative feedback loop in attenuating its stimulatory effect on hepatic FGF21 expression. Here, we assessed short‐term effect of semaglutide treatment in HFD‐challenged mice. Our observations indicate that daily semaglutide treatment for 7‐day increased hepatic FGF21 expression. The treatment also attenuated HFD challenge‐induced repression on Klb in both liver and white adipose tissue. More importantly, mouse primary hepatocyte (MPH) from HFD‐challenged mice showed attenuated response to hFGF21 treatment, while MPH from HFD‐fed mice with 7‐day semaglutide treatment showed improved response to hFGF21 treatment.
## Chemicals
Recombinant human FGF21 (hFGF21) was purchased from Cayman Chemical. Semaglutide was kindly provided by Novo Nordisk, as we have reported (Liu et al., 2022).
## Animals and animal experiments
Six‐week‐old male C57BL/6J mice, purchased from the Jackson laboratory, were either fed with low fat diet (LFD) or HFD (F3282 with $60\%$ fat calories, 5.49 kcal/g; BioServ. Flemington, New Jersey) for 13 weeks, followed by daily intraperitoneal (i.p.) semaglutide (600 μg/kg body weight) or control PBS injection for 1 week. By the end of dietary challenge and semaglutide treatment, mice were sacrificed with CO2 treatment followed by cervical dislocation. Plasma and tissues, including the liver, epididymal WAT (eWAT), inguinal WAT (iWAT), and brown adipose tissue (BAT), were collected for real‐time RT‐PCR and Western blotting. Mice were housed at constant temperature (22°C) under restricted light cycle with food and water ad labitum. The animal experiments were approved by the University Health Network Animal Care Committee Animal Resource Center (AUP# 2949.13).
## Glucose tolerance test, plasma adiponectin, and leptin measurements
For i.p. glucose tolerance test (IPGTT), mice were fasted overnight prior to receiving i.p. glucose injection (2 g/kg body weight). Mouse plasma adiponectin level was measured utilizing the mouse adiponectin immunoassay kit (Antibody and Immunoassay Services, The university of Hong Kong). The measurement of plasma leptin was conducted utilizing Mouse Leptin DuoSet ELISA Kit (R&D, USA; Tian et al., 2020).
## Mouse primary hepatocyte (MPH) isolation and hFGF21 treatment
MPH from indicated mice were isolated as we have reported previously (Liu et al., 2021). For real‐time RT‐PCR in assessing the effect of hFGF21 on gene expression, cells were treated with indicated dose of hFGF21 for 4 h. For Western blotting in determining effects of hFGF21 on AKT or ERK phosphorylation, cells were treated with indicated dose of hFGF21 for 1 h.
## RNA extraction, quantitative reverse transcription PCR
Tri reagent (Sigma‐Aldrich) was used for RNA isolation. RNA extraction, reverse transcription, and real‐time polymerase chain reaction (PCR) was performed as previously described (Shao et al., 2020). Primer sequences utilized for PCR are listed in Table 1.
**TABLE 1**
| Gene name (mouse) | Forward sequence (5′–3′) | Reverse sequence (5′‐3′) | Size (bp) |
| --- | --- | --- | --- |
| cFos | TCCCCAAACTTCGACCATG | GCACTAGAGACGGACAGATC | 189 |
| Egr1 | CAACCCTATGAGCACCTGAC | CCACTGACTAGGCTGAAAAGG | 197 |
| Fgfr1 | GTGGAGAATGAGTATGGGAGC | GGATCTGGACATACGGCAAG | 238 |
| Klb | ACGAGGGCTGTTTTATGTGG | CAGGTGAGGATCGGTAAACTG | 226 |
| Acox1 | CAGGAAGAGCAAGGAAGTGG | CCTTTCTGGCTGATCCCATA | 189 |
| Pdk4 | ACATCGCCAGAATTAAACCTCAC | TTTCCCAAGACGACAGTGGC | 191 |
| Ehhadh | AGCTGTTTATGTACCTTCGGG | CTGCTTTGGGTCTGACTCTAC | 236 |
| Ppargc1α | TGGATGAAGACGGATTGCCC | GTGTGGTTTGCTGCATGGTT | 220 |
| Fasn | AGAAGTGCAGCAAGTGTCC | GGTCGGATGAGGGCAATCTG | 258 |
| Srebf1 | TAGAGCATATCCCCCAGGTG | GGTACGGGCCACAAGAAGTA | 245 |
| Chrebp | CCCCCAGCTTTGGCCCCATG | TCGGTCCAGGAGCAGGTGGG | 234 |
| Ctp1α | AGATCAATCGGACCCTAGACAC | CAGCGAGTAGCGCATAGTCA | 122 |
| Ucp1 | GGGCCCTTGTAAACAACAAA | GTCGGTCCTTCCTTGGTGTA | 196 |
| AdipoQ | AGAAGCCGCTTATGTGTATC | TGATACTGGTCGTAGGTGAA | 255 |
| Lep | CCTGTGGCTTTGGTCCTATC | TCATTGGCTATCTGCAGCAC | 273 |
| Ero1α | TTCTGGGCGAGGAAAAAGTA | TGACCCCATTTCTTTTCCAG | 171 |
| Erp44 | TGTGCCTTCCTTTCTGCTTT | CGGACAAGAGGGACACATTT | 173 |
| Actb | TCATGAAGTGTGACGTTGACA | CCTAGAAGCATTTGCGGTG | 285 |
## Western blotting
Whole‐cell lysates from mouse liver, indicated adipose tissue, or MPH were prepared for Western blotting as previously described (Tian et al., 2019). Antibodies for western blotting were listed in Table 2. Membranes were visualized using Pierce ECL Western Blotting Substrate (Thermo Scientific). Image densitometries were analyzed using ImageJ 1.53 software.
**TABLE 2**
| Unnamed: 0 | Dilution | Company | Catalog # |
| --- | --- | --- | --- |
| pERK | 1:2000 | Cell Signaling Technology | 9106S |
| Total ERK | 1:1000 | Santa Cruz | SC‐94 |
| pAKT | 1:1000 | Cell Signaling Technology | SC‐293125 |
| Total AKT | 1:1000 | Santa Cruz | 9272S |
| FGF21 | 1:1000 | Abcam | ab171941 |
| GAPDH | 1:1000 | Cell Signaling Technology | 2118 |
## Statistical analysis
Results are expressed as mean ± SD. Differences between multiple groups were analyzed by one‐way ANOVA followed by Bonferroni post hoc tests or unpaired student's t‐test. A p‐value less than 0.05 is considered as significantly different.
## Seven‐day semaglutide treatment reduces body weight and improves glucose tolerance in HFD‐challenged mice
As shown (Figure 1a), we aimed to test metabolic beneficial effects of short‐term (but high dose) semaglutide treatment in obese mice, generated by HFD challenge. Male C57BL/6J mice were fed with LFD or HFD for 13 weeks. HFD‐challenged mice were then randomly divided into two sub‐groups, receiving either daily semaglutide i.p. ( 600 μg/kg body weight) or PBS (as control) injection for 7 days. The treatment generated profound body weight lowering effect (Figure 1b and Supporting Figure S1A,B). Food intake was significantly decreased in mice received seven‐day semaglutide treatment (Supporting Figure S1C). Figure 1c shows that the treatment also improved glucose disposal, assessed by IPGTT. HFD‐induced elevation on fasting glucose level as well as hyperleptinemia were also reversed by 7‐day semaglutide treatment (Figure 1d,e), although HFD feeding or semaglutide treatment generated no appreciable effect on plasma adiponectin level in our current experimental settings (Figure 1f). Plasma FGF21 levels were comparable between LFD‐fed and HFD‐challenged mice, while its level was elevated in mice received 7‐day semaglutide treatment (Figure 1g). Fat weights (eWAT, iWAT, and BAT) and fat weight to body weight ratios were significantly elevated after HFD challenge (Figure 1h–m). Seven‐day semaglutide treatment reduced eWAT and iWAT weight moderately but generated no appreciable effect on reducing BAT level or BAT weight to body weight ratio (Figure 1h–m). We conclude that both glucose disposal and body weight lowering effects can be achieved by 7‐day semaglutide treatment.
**FIGURE 1:** *Short‐term semaglutide treatment improves glucose tolerance and reduces body weight in HFD‐challenged mice. (a) Diagram shows the animal experimental design. (b) Body weight changes during last 7 days in the three indicated groups. (c) Blood glucose level and area under the curve (AUC) during IPGTT. (d) Fasting (overnight) blood glucose levels at the end of the experiment for indicated groups. (e–g) Fasting plasma leptin (e), adiponectin (f), and FGF21 (g) levels. (h–m) Fat pad weights including epididymal (h, eWAT) and inguinal (i, iWAT) white adipose tissue and brown adipose tissue (j, BAT). (k) eWAT weight to body weight ratio. (l) iWAT weight to body weight ratio. (m) BAT weight to body weight ratio. Sema, semaglutide. Data are shown as the mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.*
## Seven‐day semaglutide treatment restores HFD‐induced attenuation on ERK phosphorylation to hFGF21 treatment in hepatocytes
We then isolated primary hepatocytes from mice fed with either LFD, HFD, or HFD with 7‐day semaglutide treatment and tested their response to hFGF21 treatment. Figure 2a–c show results of Western blotting in the determination of effect of hFGF21 treatment on ERK phosphorylation in MPH isolated from LFD‐fed mice. One‐hour hFGF21 (either 1 or 10 nM) treatment generated no appreciable effect on AKT Ser473 (Supporting Figure S2A) or ERK p44 (Thr202) phosphorylation (Figure 2b), while AKT Ser473 phosphorylation can be effectively stimulated by 100 nM insulin treatment (Supporting Figure S2A). One nM FGF21 treatment, however, moderately stimulated ERK p42 (Tyr204) phosphorylation (Figure 2c).
**FIGURE 2:** *Seven‐day semaglutide treatment restores HFD‐induced attenuation on ERK phosphorylation to hFGF21 treatment in hepatocytes. (a) Western blotting show expression levels of indicated protein in MPH isolated from LFD‐fed mice after 1 h hFGF21 treatment with indicated dose. (b, c) Densitometric analyses for pERK (p44, Thr202) and pERK (p42, Tyr204) with indicated treatment. (d) Western blotting show expression levels of indicated protein in MPH isolated from HFD‐fed mice after 1 h hFGF21 treatment with indicated dose. (e, f) Densitometric analyses for pERK (p44, Thr202) and pERK (p42, Tyr204) with indicated treatment. (g) Western blotting show expression levels of indicated protein in MPH isolated from semaglutide‐treated mice after 1 h hFGF21 treatment with indicated dose. (h, i) Densitometric analyses for pERK (p44, Thr202) and pERK (p42, Tyr204) with indicated treatment. Data are shown as the mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001.*
The same experiments were then applied to MPH isolated from mice fed with HFD for 14 weeks (Figure 2d–f) and for HFD‐challenged mice with 7‐day semaglutide treatment (Figure 2g–i). The stimulatory effect of hFGF21 treatment on ERK p42 Tyr204 phosphorylation was absent in MPH of HFD‐challenged mice (Figure 2e,f); while semaglutide treatment restored HFD‐induced impairment on ERK phosphorylation. Specifically, hFGF21 (1 and 10 nM) treatment stimulated both ERK p44 (Thr202) and p42 (Tyr204) phosphorylation (Figure 2h,i).
## Seven‐day semaglutide treatment restores the stimulatory effects of hFGF21 on its downstream target gene expressions in MPH
We then assessed FGF21 downstream target gene expressions including cFos and Egr1 in MPH of LFD, HFD and HFD with seven‐day semaglutide treatment. Isolated MPH was treated with 1 nM or 10 nM of hFGF21 for 4 h for gene expression analysis. As shown, 10 nM (but not 1 nM) hFGF21 treatment stimulated expression of cFos, while such stimulatory effect was lost in MPH of HFD‐fed mice. Seven‐day semaglutide treatment reversed HFD‐induced attenuation on cFos expression (Figure 3a). Egr1 is another defined downstream target gene for FGF21. Its expression was elevated by both 1 and 10 nM of hFGF21 treatment in MPH of LFD‐fed mice. In MPH of HFD‐fed mice, no appreciable effects were observed on Egr1 stimulation either by 1 nM or 10 nM hFGF21 treatment, while such impairment was partially restored by 7‐day semaglutide treatment. Specifically, 10 nM of hFGF21 treatment significantly elevated Egr1 mRNA level (Figure 3b). Together, the above observations collectively suggest that short‐term semaglutide treatment restores HFD‐induced FGF21 signaling impairment in mouse hepatocytes.
**FIGURE 3:** *Seven‐day semaglutide treatment restores the stimulatory effects of hFGF21 on its downstream target gene expressions in MPH. (a) qRT‐PCR show effect of indicated dose of hFGF21 treatment (4 h) on expression of cFos in MPH isolated from LFD‐fed, HFD‐fed, and semaglutide‐treated mice. (b) qRT‐PCR show effect of indicated dose of hFGF21 treatment (4 h) on expression of Egr1 in MPH isolated from LFD‐fed, HFD‐fed, and semaglutide‐treated mice. Data are shown as the mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001.*
## Seven‐day semaglutide treatment stimulates hepatic FGF21 expression and expression of Fgfr1 and Klb
The liver tissues from the three groups of mice were then isolated for assessing FGF21 expression at both protein and mRNA levels. In the current study, HFD feeding did not significantly increase hepatic FGF21 levels, while daily semaglutide treatment for 7 days generated a significant stimulatory effect on FGF21 level, assessed by Western blotting (Figure 4a). Figure 4b shows that semaglutide treatment increased liver Fgf21 mRNA level. Figure 4c,d show that HFD challenge‐induced attenuation on expression of Fgfr1 and Klb was reversed by 7‐day semaglutide treatment. T.
**FIGURE 4:** *Seven‐day semaglutide treatment improves FGF21 sensitivity and attenuates the effect of HFD feeding on hepatic gene expression. (a) Western blotting show expression levels of FGF21 in the liver of three indicated groups. (b–d) qRT‐PCR shows the comparison of expression levels on Fgf21 (b) and genes that encode its receptor, Fgfr1 (c) and co‐receptor Klb (d) in the liver of three indicated groups. (e) qRT‐PCR shows the comparison of expression levels of lipogenic and fatty acid oxidation genes in the liver of three indicated groups. Data are shown as the mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.*
## Seven‐day semaglutide treatment attenuates the effect of HFD feeding on hepatic gene expression
We have demonstrated previously that daily liraglutide treatment for 3 weeks can ameliorate HFD‐induced alterations on expression of genes that are involved in lipid homeostasis and fatty acid β oxidation in the liver (Liu et al., 2021). Here, we aimed to test whether short‐term semaglutide treatment can also process the similar effects. Expression of hepatic genes that are involved in lipogenesis and fatty acid β oxidation was assessed in the three groups of mice. Figure 4e shows that HFD feeding significantly reduced expression of genes that encode hepatic pyruvate dehydrogenase kinase 4 (Pdk4), and enoyl‐CoA hydratase and 3‐hydroxyacyl CoA dehydrogenase (Ehhadh). Furthermore, there was a trend of decrease on expression of the gene that encodes peroxisomal acyl‐CoA oxidase 1 (Acox1) after HFD challenge. Seven‐day semaglutide treatment increased expression levels of Acox1 and Ehhadh, but not Pdk4. Semaglutide treatment also significantly increased the expression of Ppargc1a, which encodes the FGF21 downstream target peroxisome proliferator‐activated receptor γ coactivator 1 α, a key player in facilitating fatty acid β oxidation. HFD feeding also increased expression of the two genes that encode transcription factors of lipogenesis, namely sterol regulatory element‐binding transcription factor 1 (Srebf1) and carbohydrate response element‐binding protein (Chrebp). Seven‐day semaglutide treatment repressed their expression, although the repression on expression *Chrebp is* relatively moderate. HFD feeding generated no appreciable effect on expression of Fasn, which encodes fatty acid synthase. Seven‐day semaglutide treatment stimulated Fasn expression level (Figure 4e).
## Seven‐day semaglutide treatment attenuates HFD‐induced alterations on a battery of adipose‐specific genes
Finally, we assessed the effect of short‐term semaglutide treatment on epidydimal white adipose tissue (eWAT). In eWAT, semaglutide treatment also showed improvement in HFD‐induced FGF21 resistance by partially restoring Klb expression level. However, such elevation did not reach statistical significance. Fgfr1 levels remained unaffected by HFD feeding or semaglutide treatment (Figure 5a,b). HFD feeding increased Fgf21 expression level in eWAT while one‐week semaglutide treatment did not cause a further increase (Figure 5c). Figure 5d shows that HFD challenge significantly repressed Acox1, Ehhadh, and Ppargc1a, but not Pdk4 expression levels. Seven‐day semaglutide treatment partially restored HFD‐induced alterations on these three genes. Furthermore, adipose tissue‐specific genes including those that encode leptin (Lep), adiponectin (AdipoQ), and UCP1 (Ucp1), were significantly altered by HFD challenge. HFD challenge increased Lep level by nearly 26‐fold, and such elevation was partially attenuated by one‐week semaglutide treatment (reduced to ~6.5 folds). Such changes agreed with plasma leptin level changes presented in Figure 1e. AdipoQ level in eWAT, however, was significantly reduced after HFD challenge, and the reduction was partially restored by 7‐day semaglutide treatment. Such changes disagreed with unchanged plasma adiponectin hormone levels after HFD challenge or semaglutide treatment (Figure 1f). Due to the inconsistency between plasma adiponectin hormone levels and AdipoQ levels in eWAT, we then assessed two chaperone genes known as Ero1a and Erp44 in eWAT. These two genes encode for endoplasmic reticulum oxidoreductase 1 alpha and endoplasmic reticulum protein 44, respectively; involved in mediating oligomerization of the active high molecule weight form of adiponectin. Both Ero1α and Erp44 levels were significantly repressed by the HFD challenge. Following one‐week semaglutide treatment, their expression levels were comparable with that in LFD‐fed mice (Figure 5d).
**FIGURE 5:** *Seven‐day semaglutide treatment recovers HFD‐induced alteration in a battery of adipose‐specific genes. (a–c) qRT‐PCR shows the comparison of expression levels on Fgfr1 (a), Klb (b) and Fgf21 (c) in the eWAT of three indicated groups. (d) qRT‐PCR shows the comparison of expression levels of a battery of adipose tissue‐specific genes in the eWAT of three indicated groups. (e) The diagram shows the observed effects of short term semaglutide treatment on mice fed with HFD. In MPH, seven‐day semaglutide treatment restores the response to hFGF21 treatment. In the liver and eWAT, the treatment improves FGF21 sensitivity and restores HFD‐induced attenuation on genes that involved in maintaining lipid homeostasis and other adipose tissue‐specific genes. Data are shown as the mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.*
## DISCUSSION
Shortly after GLP‐1 was recognized as an incretin, extra‐pancreatic functions of this gut hormone including that in the liver and adipose tissues have been broadly studied and recognized (Jin & Weng, 2016). Functions of GLP‐1 and GLP‐1RAs in the liver as well as in adipose tissues have provided plausible explanations for their effectiveness in treating T2D subjects with severe insulin resistance as well as their profound effect on lipid homeostasis (Hein et al., 2013; Jin & Weng, 2016; Taher et al., 2014). For their hepatic functions, we have paid close attention to the hepatic hormone FGF21, as this hormone is actively involved in both glucose and lipid homeostasis (Badakhshi & Jin, 2021; Liang et al., 2014). For exploring our mechanistic understanding on hepatic functions of GLP‐1 and GLP‐1RAs, we and others have revealed that in various rodent models, GLP‐1RAs including liraglutide and exenatide can stimulate hepatic FGF21 expression (Lee et al., 2014; Liu et al., 2019, 2021; Liu & Gao, 2019; Nonogaki et al., 2014; Yang et al., 2012). Here we show that short‐term semaglutide treatment can also stimulate hepatic FGF21 expression in HFD‐challenged C57BL/6J mice. More importantly, we expanded our investigation into FGF21 sensitivity in both the liver and eWAT, involving the restoration of HFD challenge‐induced repression on Fgfr1 or Klb. We have also expanded our investigation on FGF21 signaling sensitization in response to GLP‐1RA treatment utilizing in vitro hFGF21 treatment on MPH.
In the diabetic KKAy mouse model, liraglutide treatment was shown to suppress both obesity and hyperglycemia, associated with increased hepatic FGF21 production (Nonogaki et al., 2014). In HFD‐diet challenged ApoE −/− mouse model, liraglutide treatment was also shown to stimulate hepatic FGF21 production, associated with improved insulin sensitivity (Yang et al., 2012). Another investigation demonstrated that daily exenatide treatment for 10 weeks increased expression of hepatic FGF21 and genes that encode its receptor and co‐receptor in HFD‐challenged mice (Lee et al., 2014). Furthermore, this study showed that in the human Hepa1‐6 cell line, in vitro exenatide treatment can directly increase FGF21 expression and mechanistically, the regulation is mediated by activating silent mating type information regulation 2 homolog (SIRT1) (Lee et al., 2014). We, however, cannot reproduce such in vitro effect with direct liraglutide treatment in MPH (Liu et al., 2021). Exenatide stimulated FGF21 production was also observed in obese db/db mice as well as in Pax6 (m/+) mice (Liu et al., 2019). In a recent study, we were unable to observe the in vitro stimulatory effect of liraglutide treatment on FGF21 production in MPH, which agrees with the lack of GLP‐1R detection in mouse liver, utilizing RNA‐seq and other tools (Liu et al., 2021). The lack of GLP‐1R in mouse liver was also reported by other investigations (Baggio et al., 2018; Panjwani et al., 2013). Furthermore, we demonstrated that liraglutide cannot stimulate hepatic FGF21 in GLP‐1R KO mice and that in liver specific FGF21 KO mice, liraglutide treatment showed virtually no metabolic beneficial effects, especially on lipid homeostasis (Liu et al., 2021). Thus, liraglutide utilizes GLP‐1R expressed elsewhere to stimulate hepatic FGF21 expression, and such stimulation is patho‐physiologically important. To identify such extra‐hepatic organ would require the generation of tissue specific GLP‐1R KO mouse models during adulthood.
FGF21 exerts pleiotropic metabolic beneficial effects including the increase of insulin sensitivity, the facilitation of energy expenditure, as well as the decrease in body weight and glucose uptake by adipocytes (Badakhshi & Jin, 2021). The term “FGF21 resistance” was initially coined to describe the phenomena in obese mice, showing decreased expression of FGF21 receptor complex in eWAT, which was associated with increased plasma FGF21 level, blunted ERK phosphorylation and attenuated reduction in plasma glucose level in response to exogenous FGF21 administration (Badakhshi & Jin, 2021; Fisher et al., 2010). The paradoxical relationship between plasma FGF21 level and obesity was also interpreted as the development of FGF21 resistance in obese subjects (Fisher et al., 2010). We have reported that HFD feeding can induce hepatic FGF21 resistance and such effect can be attenuated by concomitant dietary intervention with the polyphenol curcumin (Zeng et al., 2017). In that study, we have established the method for assessing hFGF21 treatment on MPH on ERK phosphorylation as well as FGF21 downstream target gene expression. Briefly, MPH from mice fed with HFD for 12 weeks developed FGF21 resistant, which can be attenuated by 12‐week concomitant curcumin intervention (Zeng et al., 2017). In our most recent studies, we expanded the investigation on hepatic FGF21 expression in response to GLP‐1RA treatment, including liraglutide and the long‐term effective GLP‐1RA semaglutide (Liu et al., 2021, 2022). Surprisingly, 4‐week liraglutide treatment but not semaglutide treatment showed the stimulatory effect on hepatic FGF21 expression.
In our view, potential explanations for the lack of stimulation on hepatic FGF21 by semaglutide treatment are as follows. Firstly, we suggested that GLP‐1RAs activate hepatic FGF21 expression via an extra‐hepatic organ that does express GLP‐1R (Liu et al., 2021). The two drugs may target such target organ with different efficacy. For example, if the target organ is the brain, the two drugs might penetrate blood brain barrier differently. Secondly, GLP‐1RAs may stimulate both hepatic FGF21 expression and FGF21 sensitivity, and the long‐term effective semaglutide may exert such functions more effectively, leading to the triggering of a negative feedback on hepatic FGF21 expression. In other words, we may have missed the “activation window” after semaglutide treatment for 4 weeks. We assessed the second possibility in current study by testing the effect of short‐term semaglutide treatment. Partially for this reason, we have increased the dosage of semaglutide to 600 μg/kg body weight. As shown, 7‐day semaglutide treatment significantly reduced body weight and repressed food intake in mice with HFD challenge. In addition, 7‐day semaglutide treatment increased serum FGF21 level, hepatic FGF21 hormone level as well as hepatic Fgf21 mRNA level and that MPH isolated from HFD‐fed mice received 7‐day semaglutide treatment showed restored sensitivity to in vitro hFGF21 treatment on ERK phosphorylation and FGF21 target gene expression, and that in those mice both liver and eWAT showed partially restored expression of Klb. Like 4‐week liraglutide treatment (Liu et al., 2021), 7‐day semaglutide treatment also restored expression of Fgfr1 in the liver, which was attenuated by HFD challenge. In eWAT, either HFD challenge or 7‐day semaglutide treatment generated no appreciable effect on Fgfr1 expression level, which agrees with an early study conducted by Yang and colleagues (Yang et al., 2012). GLP‐1R activation has been shown to improve glycemic control and promote satiety, leading to reduced caloric intake and body weight (Baggio & Drucker, 2014; Drucker, 2022). Due to high dosage utilized in current study, seven‐day semaglutide treatment may induced a “fasting‐like” state in mice with HFD‐challenged. Since FGF21 is a fasting hormone, the increased level can be a results of reduced food intake (Fazeli et al., 2015; Zhang et al., 2012). Hence, whether semaglutide treatment‐induced stimulation on FGF21 level is secondary to its anorexigenic effect require further investigation.
We have reported the stimulatory effect of 3‐week liraglutide treatment in high fat and high fructose diet challenged mice on expression of Ppargc1a, Acox1, Pdk4, and Ehhadh, and the repressive effect of its treatment on Screbf1 in the liver (Liu et al., 2021). Those regulatory effects can be reproduced in HFD‐challenged mice with 7‐day semaglutide treatment. Among them the product of Ppargc1a directly mediates function of FGF21 in lipid homeostasis, while others are involved in lipogenesis and fatty acid β oxidation. In eWAT, we show here that AdipoQ level was repressed by HFD challenge, and the repression was attenuated by 7‐day semaglutide treatment. In mouse plasma, however, adiponectin hormone level was not significantly affected by 14‐week HFD feeding or 7‐day semaglutide treatment. The difference could be due to post‐translational modifications of the adiponectin hormone. We hence assessed the effect of HFD challenge and 7‐day semaglutide treatment on genes known as Ero1α and Erp44, which encode for the two chaperones that are engaged in adiponectin oligomerization. We show here that HFD feeding reduced expression of these two chaperone genes, whereas semaglutide treatment was able to restore their expression levels. Low plasma adiponectin level is linked to insulin resistance and increased T2D incidence. It has been reported that FGF21 stimulates adiponectin secretion in adipocytes (Lin et al., 2013). Thus, in HFD‐challenged mice, semaglutide mediated stimulation on AdipoQ expression and restoration of Ero1α and Erp44 expression may still be physiologically important. Further investigations are needed to assess plasma total adiponectin level, adiponectin oligomerization, and active adiponectin level, in obese mice treated with GLP‐1RAs during different time intervals.
To clarify the effect of semaglutide on hepatic FGF21 level, we designed the short‐term treatment of 7‐day, with a high dosage (600 μg/kg body weight). Such high dosage treatment did not generate obvious abnormalities on mouse health, including the development of hypoglycemia. Clinically, high dose weekly semaglutide treatment has shown to exert better outcome in body weight lowering and glucose disposal (Bradley et al., 2022; Frías et al., 2021). We have learned for decades that early intensive insulin therapy in patients with newly diagnosed T2D can bring favorable outcomes (Weng et al., 2008), while in T2D subjects with severe insulin resistance, high dose insulin treatment can be well‐tolerated and effective on improving glucose disposal (Kampmann et al., 2011). It is worth testing whether early intensive GLP‐1RA therapy can bring a better therapeutic effect in early diagnosed T2D patients.
Numerous studies have reported the metabolic regulatory role of Sirt1 in lipid homeostasis (Hou et al., 2008; Majeed et al., 2021; Simmons et al., 2015). Specifically, Sirt1 was determined to play a beneficial role in protecting HFD‐induced or alcohol consumption‐induced hepatic steatosis, yet the mechanism underlying its metabolic functions is not fully understood (Pfluger et al., 2008). Weng et al. has showed that Sirt1 mediates the effects of GLP‐1RA exenatide on attenuating hepatic steatosis (Xu et al., 2014). DPP4i, vildagliptin has been revealed to induce FGF21 via Sirt1 signaling (Furukawa et al., 2021). Thus, it is worthwhile to examine whether Sirt1 plays a role in improving FGF21 sensitivity after GLP‐1RA treatments. Here, we choose male mice as our animal model since female mice are known to be resistant against HFD feeding. In a recent study, we have determined that hepatic FGF21 expression is regulated by estrogen‐Wnt signaling cascade (Badakhshi et al., 2021). Hence, a technical breakthrough is required in the field to better understand the involvement of female hormone in current study.
Figure 5e summarizes our results in current study. In obese mice, 7‐day high dose semaglutide treatment increased hepatic FGF21 level, associated with attenuated repression on expression of Fgfr1 and Klb in the liver. In addition, alterations on a battery of genes that are implicated in mediating functions of FGF21 in the liver induced by HFD challenge were attenuated by 7‐day semaglutide treatment. In MPH, HFD‐induced impairments on FGF21 signaling were also restored by 7‐day semaglutide treatment. In eWAT, 7‐day semaglutide treatment partially restored the repressive effect of HFD on Klb expression and effectively restored the repression of HFD on Acox1, Ehhadh, and Ppargc1α, downstream effectors of FGF21. We hence confirmed that the GLP‐1RA semaglutide can up‐regulate hepatic FGF21 production and restore FGF21 sensitivity that is impaired by HFD challenge. Mechanisms underlying effect of short‐term semaglutide treatment on eWAT hormone gene expression and the restoration of expression of the two chaperone genes impaired by HFD challenge are also worth to be further examined.
## AUTHOR CONTRIBUTIONS
JNF, WS, and TJ conceived and designed research. JNF and WS performed experiments. JNF analyzed data. JNF, WS, and TJ interpreted results of experiments. JNF and WS prepared figures and tables. JNF drafted the manuscript. TJ edited and revised the manuscript.
## FUNDING INFORMATION
This study is supported by the Canadian Institutes of Health Research (PJT159735 to T.J.).
## CONFLICT OF INTEREST STATEMENT
The authors declare no competing interests.
## DISCLAIMERS
JNF is a Ph.D. student supported by the Banting & Best Diabetes Centre (BBDC)‐Novo Nordisk Studentship, and Canada Graduate Scholarships—Master's program (CGS M).
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---
title: Phenotype-specific estimation of metabolic fluxes using gene expression data
authors:
- Nicolás González-Arrué
- Isidora Inostroza
- Raúl Conejeros
- Marcelo Rivas-Astroza
journal: iScience
year: 2023
pmcid: PMC10006673
doi: 10.1016/j.isci.2023.106201
license: CC BY 4.0
---
# Phenotype-specific estimation of metabolic fluxes using gene expression data
## Summary
A cell’s genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction’s kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon’s entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.
## Graphical abstract
## Highlights
•A novel computational model estimates phenotype-specific fluxomes at the genome-scale•Results accurately estimate the carbon core metabolism in yeast and bacteria•Using RNA-seq data, the Warburg effect is predicted in various cancer types
## Abstract
Cellular physiology; Complex system biology; Omics; Transcriptomics
## Introduction
Cells can adapt their metabolism to context-specific conditions by controlling their enzymes production.1,2,3 This phenomenon has been observed in bacteria and yeasts where tampering with their normal genetic patterns is an effective way to redistribute metabolic fluxes and improve fermentation yields.4,5,6,7 Likewise, genetic disorders such as cancer and diabetes are often paired with aberrant distributions of metabolic fluxes.8,9,10,11 Anticipating how the genetic expression influences the metabolic state can drive further improvements in the fermentation industry and lead to novel therapies for genetic maladies. These challenges have ushered the development of various mathematical models that infer a cell’s metabolic flux distribution conditioned on its observed gene-expression pattern.12,13,14,15,16,17,18,19 However, translational and post-transcriptional mechanisms, such as enzyme activities and allosteric modulation, also regulate reactions’ kinetics, resulting in the fluxome not being perfectly correlated with the expression levels of their enzyme-related genes.20,21,22 To cover for these unobserved mechanisms, current mathematical models can only rely on ad hoc assumptions,23 resulting in inconsistent predictions.24,25 Such ambiguity poses the question: How should the unobserved regulatory interactions between transcriptome and reaction kinetics be systematically accounted for when inferring the fluxome?
Genome-scale fluxomes cannot be computed based on kinetic expressions as these are not fully known for most reactions.26,27,28,29 Alternatively, the fluxome space can be constrained by mass and energy conservation principles,30,31 leading to the development of constraint-based models (CBM). In CBMs a steady-state condition is assumed. Consequently, producing and consuming fluxes for each metabolite equate. Applying this assumption to all metabolites in a cell’s metabolic network results in a set of linear constraints defining a solution space of feasible fluxomes.28,32,33 Measurements of metabolically active molecules, such as RNA, proteins, and metabolites, can be used to inform further reductions of this space.34,35,36,37,38,39,40,41 *In this* context, transcriptomic patterns are one of the most accessible measurements, as reliable and affordable technologies like micro-arrays and RNA-seq have made gene expression measurements available at genome-wide scales.42,43 Current CBMs using transcriptomic information measure the correlation between fluxome and transcriptome by setting bounds on fluxes bounds, defining a context-specific objective function, or both.44 Some methods divide genes between highly and lowly expressed and then select a flux configuration that maximizes the consistency with this classification.13,16,45 However, these methods required an user-defined threshold to distinguish between both sets of genes. Methods maximizing an a priori objective function typically use biomass growth rate, which has proven adequate for unicellular organisms like bacteria and yeast.46,47,48,49 However, it may not be appropriate for somatic cells known to maintain stable biomass –e.g., neurons. Alternative objective functions such as maximization of ATP can be used, but these are hard to validate for somatic cells.50,51 Lastly, most methods do not produce a unique solution but only reduce the space of feasible flux configurations from where an arbitrary point is selected to estimate the metabolic state. Other methods can infer a single flux configuration by being formulated as strictly convex optimization problems.19,52 However, many strictly convex functions can maximize the correlation between fluxome and transcriptome but still produce divergent fluxomes.19 *As a* result, currently there is not a method that consistently produces better fluxome estimations that any other alternative.24 Here, we used the principle of maximum entropy to develop a mathematical model named Pheflux, which estimates the fluxome conditioned on an organism’s metabolic network and transcriptome. The principle of maximum entropy has been applied to infer genetic interaction networks,53 the distribution of growth rates of unicellular organisms,54,55,56 flux elementary modes,57 and fluxomes in the absence of transcriptomic data.52,58 To integrate transcriptomic data, we formulated Pheflux as an optimization problem maximizing Shannon’s entropy59 of fluxes per messenger RNA (mRNA). Pheflux formulation stands on two statistical inference arguments stemming from the principle of maximum entropy. First, from an information theory perspective, inferences made in this way correspond to the ones that admit the most ignorance besides prior information.60,61 By being the least biased, the selected fluxome is less susceptible to over-fitting.62 Second, from a statistical mechanics perspective, these inferences are the ones that can happen in the greatest number of ways.63 Without further information, it is reasonable to assume that all feasible fluxomes can occur. As a result, the selected fluxome is the most likely to be observed. Of interest, we show that *Pheflux is* equivalent to minimizing the forward Kullback-Leibler divergence between fluxome and transcriptome, providing a framework to argue that this function is the best to measure a statistical distance between both vectors. In addition, we found that Pheflux predictions in bacteria and yeast outperform alternative methods and that it recapitulates the Warburg effect in cancer human cells.
## Pheflux estimates are less sensitive to thermodynamically infeasible cycles
To gain intuition about how Pheflux differs from current methods, we compared its predictions to the Simplified Pearson Correlation with *Transcriptomic data* (SPOT) model.19 Like Pheflux, SPOT does not require an objective function with biological meaning and has been highlighted as one of the best methods to predict fluxomes conditioned on transcriptomic data.19,64 We used a toy network model consisting of two metabolites and four reactions. This network includes a thermodynamically infeasible cycle (TIC)65 (Figure 1A). We focus on TICs, as although there are several methods to remove them from fluxome estimations58,66,67,68,69 these are still ubiquitous among genome-scale metabolic models66 and known to introduce artifacts in the form of spurious high-level flux cycles.52 TICs are particularly important when considering that the probability distribution function of genes’ expression follows an exponential decay, i.e., many enzyme-coding genes have low expression levels, and a few are highly expressed.70 *As a* result, if highly expressed genes partake in TICs, this is likely to result in nonsensical fluxome estimations, which is particularly relevant as SPOT, as well as Pheflux, are not warranted to produce TICs free fluxome estimations. Figure 1Toy network with a thermodynamically infeasible cycleThe network consists of two metabolites and four reactions, where v represents fluxes and g genes’ expression (A) When all reactions have equal gene expression values, Pheflux (dark gray) and SPOT (light gray) produce similar fluxome estimations (B) When g3 magnitude doubles every other reaction, SPOT cycles higher flux between metabolites than Pheflux (C) When g3 is one order of magnitude higher than any other reaction, SPOT only predicts flux cycling between metabolites, with no flux exchange. Conversely, even in this extreme case, Pheflux predicts flux exchange (D).
As illustrated by the three study cases in Figures 1B–1D. Pheflux fluxome estimations are less sensitive to TICs than SPOT. In cases 1 (Figure 1B) and 2 (Figure 1C), the genetic expression has little influence in the fluxome inference of both models, probably because of the little difference that exists between the expression level of each enzyme across the network. However, in case 3 (Figure 1D) where reaction 3 has twice the gene expression level of any other reaction, SPOT estimated a biologically infeasible fluxome in which only reactions 2 and 3 carried flux. Pheflux also estimated flux cycling between these two reactions but, unlike SPOT, still predicted exchange fluxes through the intake (v1) and production (v4) reactions. These results suggest that Pheflux turns out to be less susceptible to TICs when highly expressed genes are detected. This behavior stems from its foundation, the principle of maximum entropy, which has already been reported to generate more homogeneous fluxomes, avoiding extreme outlier fluxes caused by TICs.52
## Central carbon metabolism was predicted with high accuracy
To assess the goodness of fit of Pheflux estimations, we used as benchmark reported flux values of the core carbon metabolism.71,72,73,74,75 We compiled from the literature a dataset encompassing five microorganisms —prokaryotes and eukaryotes— cultured under 21 different conditions (see Table S1). For each condition, this dataset includes a context-specific 13C derived fluxome of the carbon core metabolism and a transcriptome (generated by either RNA-seq or microarray technologies). We used each transcriptome to condition Pheflux and generated 21 phenotype-specific fluxomes. In addition, we estimated fluxomes using SPOT, and FBA followed by ℓ2 minimization (FBA+min ℓ2) as previous reports show that it produces good fluxome predictions despite not considering phenotype-specific data.19 We evaluated the goodness of fit of the phenotype-specific fluxomes produced by Pheflux, SPOT, and FBA+min ℓ2 by comparing them to their corresponding phenotype-specific 13C derived fluxomes, which is a common validation procedure for CBMs.24,55,64 We measured the goodness of fit between estimated and experimental fluxes using the Pearson correlation coefficient. Results are presented in Figure 2.Figure 2Comparison of Pheflux, SPOT, and FBA+min ℓ2 estimations to experimental fluxomes of bacteria and yeastsThe dataset includes glycolysis and TCA cycle reactions for cultures using single carbon sources and the mixture glycerol-glucose for Y. lipolytica, and succinate-glutamate and malate-glucose for B. subtilis.
*In* general, Pheflux yielded an average Pearson correlation value of 0.843 (Figure 2), outperforming SPOT, which resulted in an average value of 0.764. However, Pheflux did not outperformed FBA+min ℓ2(r¯=0.852), which dispenses with transcriptomic information and instead relies on maximizing biomass production. This result is coherent with previous studies reporting that FBA+min ℓ2 outperforms SPOT and other transcription-based CBMs.24,64 However, using the same transcriptomic information of our *Escherichia coli* study case, Bhadra-Lobo et al. [ 2020]64 reported that SPOT performs better than parsimonious FBA when the uptake rate of the carbon source is missing or speculative. We obtain similar results with Pheflux when compared to FBA+min ℓ2. Figure S1 shows that Pheflux outperforms FBA+min ℓ2 when 8 carbon sources (acetate, fructose, galactose, glucose, glycerol, gluconate, pyruvate, and succinate) are left free to be consume from the environment (Figure S1A), and likewise when all possible carbon sources are left open to be consumed from the medium (Figure S1B). A possible explanation for FBA+min ℓ2 superior performance when the uptake rate is known may stem from the carbon core metabolism being conserved across environmental conditions.76 However, the biomass reaction on which FBA+min ℓ2 relies is fine-tuned for a single source of carbon and may lead FBA+min ℓ2 astray when various carbon sources are consumed.50,77 In line with this explanation, when more than one carbon source is uptaken, Pheflux outperforms FBA+min ℓ2. In these cases, Pheflux performance is the best in two out of three cases, and close to the best in the remaining one. The higher variability in the performance of SPOT on these cases may be because of its higher sensitivity to TICs (see Figures 1B and 1C). It has been shown that TICs result in overestimation of fluxes, decreasing the predictive performance of CBMs.52 On the other hand, these outcomes suggest that FBA+min ℓ2 higher performance is particular to the carbon core metabolism. To explore this question, a more informative test would compare estimated and predicted fluxomes at a genome-wide scale. We present such a comparison in the next section.
## Genome-scale fluxome predictions
Current experimental methods do not allow measurements of the fluxome at a genome-scale in a reliable and reproducible manner.78 *For this* reason, we produced genome-wide fluxomes via computational simulations. We used the optGpSampler algorithm79 to uniformly sample the fluxome space of E. coli iJO1366 metabolic network,71 generating 11 sets of 1000 samples each. For each sample set, a fraction of the reactions was randomly selected. Gene expression values proportional to their fluxes were assigned to this fraction, and random gene expression values to all others. We varied the selected fraction, λ, between 0 and 1 –in increments of 0.1– to represent uncorrelated and perfectly correlated fluxomes-transcriptomes pairs, respectively. We used the fluxome simulations as a benchmark against which the Pearson correlations of Pheflux and FBA+min ℓ2 predictions were measured. To test if predictive performance is affected by the selection of the reference reaction set, we computed correlations at genome and carbon core scales.
At the core carbon scale, FBA+min ℓ2 produced correlations higher than 0.9 in all the scenarios (Figure 3A), outperforming Pheflux in all but the case where $100\%$ of the fluxome correlates with the genetic expression (Figure 3B). FBA+min ℓ2 high performance is coherent with the results of the previous section as well as with previous publications.24,64Figure 3E. coli core fluxes predictions under different scenarios. Carbon core fluxes computed by FBA+min l2 (A) and Pheflux (B). Genome-scale fluxes computed by FBA+min l2 (C) and Pheflux (D).
However, FBA+min ℓ2 advantage in performance did not extend beyond the carbon core test set. At a genome-scale, results show that FBA+min ℓ2 mean correlation coefficients plump below 0.1 regardless the λ level (Figure 3C). Pheflux performance was always higher than FBA+min ℓ2 even at λ=0 where gene expression is uninformative of the fluxome state (Figure 3D). It can be inferred that incorporating transcriptomic information via Pheflux improves estimations of the fluxome.
## Pheflux predicted Warburg in cancer cells
To determine whether Pheflux can estimate phenotype-specific fluxome distributions, we evaluated its ability to replicate known metabolic differences between normal and cancer cells at stages I, II, III, and IV.8 A hallmark of cancer metabolic reprogramming9,80 is the Warburg effect, also called aerobic glycolysis. The Warburg effect is characterized by an increased glucose uptake rate and subsequent conversion to lactate, regardless of oxygen availability.81 We used transcriptomes of Breast (899 tumoral and 95 normal tissue transcriptomes), Kidney (893 tumoral and 128 and normal tissue transcriptomes) and Bronchus–Lung (1036 tumoral and 108 normal tissue transcriptomes) tissues obtained from The Cancer Genome Atlas (TCGA), and we compared their yields of glucose in lactate (vlac/vglc).
As Figure 4 shows, Pheflux fluxome estimations in cancer tissues exhibit, on average, a higher yield of glucose in lactate than in normal tissues (p−value<1×10−15 for Breast and Kidney cancers, and p−value<1×10−4 for Bronchus-Lung cancer; Mann-Whitney U test). This distinctive feature can also be observed at cancer developmental stages I, II, and III for all three tissue types (Figure 5; p−value<0.05; Mann-Whitney U test, cancer stage IV was not considered because of lack of sample size), except for stage III of Bronchus-Lung tissues, where there was a significant overlap between the fluxomes of normal and tumoral tissues. These results are coherent with previous reports showing that, on average, tumoral tissues produce higher yields of glucose in lactate,82,83,84 suggesting that Pheflux can reproduce phenotype-specific fluxomes of cancer cells. Figure 4Fluxome estimations for normal and tumor cells for breast, kidney and bronchus-lung tissues. Pheflux estimated higher yields of glucose on lactate (vlac/vglc) on cancer compared to normal tissues. Figure 5Stage-specific fluxome estimations for normal and tumor cellsResults for kidney (A), breast (B) and bronchus-lung (C) tissues. For all cancer types, in all but stage IV (where sample sizes were not big enough to conduct statistical tests), Pheflux estimated higher yields of glucose on lactate (vlac/vglc) on cancer compared to normal tissues.
*The* gene expression patterns of various metabolic pathways have been reported to be affected by cancer,85 but it is not clear how these differences impact the distribution of metabolic fluxes. We used Pheflux inferences to find differential use of metabolic pathways between normal and tumoral cells. For each pathway, we used as index the average flux magnitude among its reactions (using the reaction pathway-membership reported in Recon3D86), and normalized by the sum of all network fluxes magnitudes. This index can be interpreted as the enrichment of such pathway given a distribution of fluxes. Then, we compared the average enrichments of normal and tumoral TCGA samples for each cancer type (Figure 6A). Coherent with previous results,87 we found that in all cancer types, oxidative phosphorylation (OXP) is dominant among normal samples, whereas glycolysis/gluconeogenesis (GG) and NAD metabolism are dominant among tumoral samples. However, we found that the tricarboxylic acid cycle (TCA) is dominant among normal kidney samples, but enriched in tumoral breast and bronchus-lung samples. These results do not contradict the Warburg effect as in all cancer types the flux of pyruvate that is diverted toward production of lactate (reaction LDH) is still greater than the flux that diverts pyruvate toward the TCA (reaction PDHm; Figure 6B). We speculate that in bronchus-lung and breast cancers, treatments aimed to downregulate the TCA flux may be of therapeutic value. Figure 6Differential use of metabolic pathways between normal and cancer tissuesThe relative use of a metabolic pathway (enrichment) between tumoral and normal cells is presented for all three cancer types (A). In all cancer types, the ratio between the fluxes of pyruvate that goes into the Krebs cycle versus lactate –computed as the flux ration between reaction LDH and PDHm– is always greater in tumoral cells (B). The metabolic pathways are coded as: Glycolysis/gluconeogenesis: GG; Oxidative phosphorylation: OXP; Pyruvate metabolism: PM; Glutamate metabolism: GM; Alanine and aspartate metabolism: AAM; CoA catabolism: CC; CoA synthesis: CS; Arginine and proline metabolism: APM; Tryptophan metabolism: TM; Citric acid cycle”: TCA; Nucleotide metabolism: NM; NAD metabolism: NAD; Fatty acid synthesis: FAS; Fatty acid oxidation: FAO; Cholesterol metabolism: CM; and Transport, mitochondrial: Mt. p-values (Mann-Whitney U test) are coded as: <0.0001: ∗∗∗; <0.01: ∗∗; and <0.05: ∗.
## CPU times
Pheflux CPU times ranged between 30 s and 240 s, depending on the number of variables associated with the size of the metabolic network (Figure 7). For example, metabolic networks with around 3000 variables –Bacillus subtilis, Scheffersomyces stipitis, Saccharomyces cerevisiae, and E. coli— the computing times were, on average, 19 s, whereas the large human metabolic network −14000 variables– needed around 240 s to solve. It should be noted that Pheflux was solved without using any specialized algorithms, so that CPU times could be further reduced if a custom implementation is considered. Figure 7CPU times for different genome-scale metabolic networksSeveral transcriptomes per species were processed using Pheflux. The data points density for B. subtilis, E. coli, and H. sapiens is color-coded in the blue (low density) to red (high density) range, whereas the non-overlapping data points for S. stipitis, S. cerevisiae and Y. lipolytica are presented in black.
## Discussion
To infer the fluxome conditioned on a cell’s metabolic network and transcriptome, we developed Pheflux, a novel CBM. Pheflux estimates the fluxome by maximizing Shannon’s entropy of fluxes per enzyme. Such an approach can not completely eliminate the uncertainty of the missing information of reactions’ kinetics, but should, on average, outperform alternative CBMs conditioned on the same prior data because a fluxome inferred according to the principle of maximum entropy corresponds to the one that can happen in the greatest number of ways and with the least amount of unwarranted assumptions. We found support for this hypothesis in the superior performance of Pheflux compared to alternative methods for estimating the fluxomes of various bacteria and yeasts. We further studied Pheflux capacity to infer phenotype-specific fluxomes using thousands of transcriptomes obtained from the TCGA. We found that Pheflux correctly reported higher yields of glucose on lactate on tumor cells compared to their normal counterparts, being this coherent with the Warburg effect, a hallmark of cancer metabolism.
The principle of maximum entropy has been previously used in CBMs to estimate a cell’s fluxome52,55,57 but, to the best of our knowledge, *Pheflux is* the first to condition its inferences on transcriptomics. Transcription-based CBMs such as integrative metabolic analysis tool (iMAT),16 SPOT, and E-flux219 dispense with the principle of maximum entropy and instead rely on maximizing a mutual relationship between fluxome and transcriptome. Conversely, Pheflux does not require surmising a correlation function as its formulation is equivalent to minimizing a statistical distance –the forward Kullback-Leibler divergence– between fluxome and transcriptome. As such, Pheflux predictions can be interpreted as the fluxome that minimizes the expected excess surprise with regard to the transcriptome. Pheflux does not require or prevent using an a priori objective function, which can be useful in cases where such a function is appropriate and easily measurable, for instance, the biomass growth rate for bacteria and yeast. In these cases, measurements of such function can be used as extra constraints of the fluxome space to further improve Pheflux predictions.
The quality of Pheflux inferences is affected by the grade of the data upon which it is conditioned and by the validity of its underlying assumptions. As a CBM, Pheflux requires a fluxome space that faithfully matches the metabolic capacities of the organism under study. For this, it is critical that the genome-scale metabolic network encompasses the reactions, metabolites, and cellular compartments that actually pertain to the organism under study. Once a bona fide fluxome space is established, experimentally observed fluxes –e.g., exchange rates of substrates and products– can be used as extra constraints to reduce the fluxome space. Pheflux’s underlying assumptions may also limit the accuracy of its inferences, as the inclusion of transcriptomic information does not yield extra constraints to reduce further the solution space but weights the selection of the most probable fluxome. Pheflux assumes that gene expression is a good proxy of enzyme abundance, but it has been shown that the abundance of enzymes and mRNAs are not perfectly correlated.21,22,88 *In this* case, Pheflux estimations may be improved if provided directly with protein concentrations. In addition, Pheflux assumes that all copies of an enzyme carry the same flux. We expect this to be a good approximation for enzymes that move freely within the cell but not for enzymes that are organelle-specific, membrane-attached or otherwise unevenly distributed within the cell. In these cases, more detailed information about enzyme distribution must be provided experimentally. Another limitation of *Pheflux is* that its predictions are not free of TICs. However, Pheflux can be easily upgraded to prevent this. Fleming et al. [ 2000]58 have shown that the maximization of a functional equivalent to the objective function of Pheflux archives thermodynamically consistent fluxome estimations if all reactions are divided into forward and reverse fluxes. This will increase CPU times by adding extra variables but it is guaranteed to produce TICs free estimations.
CPU times showed that Pheflux could be efficiently applied to large eukaryotic metabolic networks in a computationally efficient manner, even when Pheflux objective function is convex but non-linear. As a result, we expect Pheflux to have a wide range of applications in studies that rely on large metabolic networks, such as RECON3D. These include the study of diabetes,89 animal cell cultures,90 and metabolic changes in embryo development.91 Pheflux will be especially useful to model cell types from multi-cellular organisms, where a biological function may prove hard to come by.
## Limitations of study
In the absence of in vivo genome-wide protein concentration data, we used gene expression data as an approximation of enzyme concentrations. However, post-transcriptional regulatory processes may result in mRNA levels not always proportional to their corresponding protein concentrations. As a result, this approximation may limit the accuracy of Pheflux estimations.
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERDeposited dataS. cerevisiae GEMMo et al.72Mo et al. ,72 iMM904S. cerevisiae RNA-seq transcriptomicsNookaew et al.99Chemostat and batch, using glucose as car-bon source. S. cerevisiae 13C fluxomicsPapini et al.73Chemostat and batch, using glucose as car-bon sourceS. stipitis GEMLiu et al.105iTL885S. stipitis RNA-seq transcriptomicsPapini et al.73Chemostat and batch, using glucose as car-bon sourceS. stipitis 13C fluxomicsPapini et al.73Chemostat and batch, using glucose as car-bon sourceY. lipolytica GEMKerkhoven et al.74iYaliY. lipolytica RNA-seq tran-scriptomicsSabra et al.100Glycerol and glucose as carbon sourceY. lipolytica 13C fluxomicsSabra et al.100Glycerol and glucose as carbon sourceE. coli GEMOrth et al.71iJO1366E. coli microarray transcrip-tomicGerosa et al.101Eight different carbon sources. E. coli 13C fluxomicsGerosa et al.101Eight different carbon sourcesB. subtilis GEMOh et al.75iYO844B. subtilis microarray tran-scriptomicsNicolas et al.103Eight different carbon sources. B. subtilis 13C fluxomicsChubukov et al.102Eight different carbon sources. H. sapiens GEMBrunk et al.86Recon3DKidney primary tumor and solid tissue normal FPKMshttps://portal.gdc.cancer.gov/GDC API fields: cases.primary_site: kidney, files.analysis.workflow_type: HTSeq - FPKMBreast primary tumor and solid tissue normal FPKMshttps://portal.gdc.cancer.gov/GDC API fields: cases.primary_site: breast, files.analysis.workflow_type: HTSeq - FPKMBronchus-Lung primary tu-mor and solid tissue normal FPKMshttps://portal.gdc.cancer.gov/GDC API fields: cases.primary_site: bronchus and lung, files.analysis.workflow_type: HTSeq - FPKMSoftware and algorithmsPheflux (v 1.0.1)This paperhttps://doi.org/10.5281/zenodo.7383247Python (v 3.8)Van Rossum et al.106https://www.python.org/
## Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Marcelo Rivas-Astroza ([email protected]).
## Materials availability
This study did not generate new unique data or reagents.
## Fluxome space
Given a metabolic network of N reactions and M metabolites, the rate of change of the metabolites’ concentrations over time, c˙∈RM, is given by:(Equation 1)Sv=c˙where v∈R+N is the vector of reactions’ fluxes –the fluxome– and S∈RM×RN the stoichiometric matrix. All fluxes are positive, with reversible reactions been split into forward (vif) and reverse (vir) reactions, such that the net flux is vi=vif−vir. Assuming a steady-state condition, Equation 1 reduces to Sv=0. Thermodynamic potentials defining reactions directions or experimentally measured reaction fluxes are incorporated in the form of lower (LB∈R+N) and upper bounds (UB∈R+N). All these constraints define the polytope of the fluxome space:(Equation 2)P={v∈R+N|Sv=0,LB≤v≤UB}
## Selection of the most likely and least biased fluxome
For any reaction i in a metabolic network, its flux, vi mmol/gDW/g, is proportional the number of enzyme copies, qi. This proportionality can be converted into equality by considering the maximum turnover rate of each enzyme, ki mmol/gDW/h, and a term in the range [0,1], ηi, to account for condition-specific factors affecting the flux kinetics (for instance, inhibition by product) as follows92:(Equation 3)vi=(kiηi)qi At present it is not feasible to measure most (kiηi) values under in vivo conditions,92 but instead they must be statistically inferred. Here, we propose to use the principle of maximum entropy to estimate such values. From Equation 3, kiηi can be expressed as:(Equation 4)(ηiki)=viqi Equation 4 implies that (kiηi) can be interpreted as the flux per each copy of the enzyme catalyzing reaction i. Assuming that gene expression, gi, is a good proxy for qi, we replace qi for gi in Equation 4, and then define the probability distribution that is function of v and condition on g, Pg(v), as the relative frequency of each vi/gi:(Equation 5)Pg(vi)=vi/giVwhere V=∑$i = 1$N∑$j = 1$givi/gi, and j index each enzyme copy catalyzing reaction i. Assuming that each enzyme copy carries the same flux, the terms vi/gi can be factor out of the inner sum, rendering V=∑$i = 1$Ngivi/gi=∑$i = 1$Nvi. From a statistical mechanics point of view, the Pg(v) that maximizes the Boltzmann’s entropy results in the distribution of vi/gi that can happen in the greatest number of ways.63 From an information theory point of view, the Pg(v) that maximized Shannon entropy results in the distribution of vi/gi that requires the least amount of prior information.59 Boltzmann and Shannon’s entropies have the same functional form, H, which, when applied to all enzyme copies of each reaction, results in:(Equation 6)Hg(v)=−∑$i = 1$N∑$j = 1$giPg(vi)logPg(vi)(Equation 7)=−∑$i = 1$NgiPg(vi)logPg(vi)(Equation 8)=−∑$i = 1$Ngivi/giVlogvi/giV(Equation 9)=−∑$i = 1$NviVlogvi/giV *In this* formulation, Hg(v) is a function of the fluxome, v, conditioned on the experimentally observed transcriptome, g. Thus, we defined Pheflux as the selection of v according to the following optimization problem:(Equation 10)maxvHg(v)subjectto:(Equation 11)v∈P
## Equivalence with the minimization of the forward kullback-libeler divergence
Equation 10 is equivalent to minimize the forward Kullback-Leibler93 between v and g. Factoring the V terms of Equation 9 results in:(Equation 12)Hg(v)=−1V∑$i = 1$Nvilogvigi+logV A V is a constant, the following equality holds:(Equation 13)max{Hg(v)}=max{−1V∑$i = 1$Nvilogvigi+logV}=min{∑$i = 1$Nvilogvigi} The rightmost term of Equation 13 is equivalent to minimize the forward Kullback-Leibler divergence93 between the probability distribution of the fluxome P(vi)=vi/V and the probability distribution of gene expression per reaction Q(gi)=gi/G, where G is equal to ∑igi. Thus, the solution of Equation 10 is also the solution of(Equation 14)min:vDKL(P‖Q)=∑$i = 1$NP(vi)logP(vi)Q(gi)s.a:Sv=0LB≤v≤UB
## Bioinformatic analyses
The RNA-seq libraries from S. cerevisiae, S. stipitis, and Y. lipolytica (see Table S1) were mapped using STAR 2.5.0a94 with default settings. We used as reference genomes the assemblies S. cerevisiae sacCer3, S. stipitis CBS 6054, and Y. lipolytica CLIB122. Gene expression per gene was computed as fragments per thousands of exonic bases per millions of reading mapped. The micro-arrays from E. coli and B. subtilis were quantile normalized using the limma R package.95
## Computational implementation
As we considered all fluxes non-negative, their lower and upper bounds were assigned 0 and 1000, respectively. Pheflux, SPOT, FBA+min ℓ2, and Flux sampling were implemented using COBRApy 0.22.196 library in Python 3.8. Pheflux non-linear optimization was done using IPOPT 3.12.397 optimizer through the CasADi 3.5.598 interface. SPOT and FBA+min ℓ2 optimization were performed using CPLEX 20.1 optimizer and Flux sampling using the optGpSampler79 implemented in COBRApy.
To perform Pheflux optimization via IPOPT we used the same considerations as Rivas-Astroza & Conejeros [2020],52 i.e. we added a small number, ϵ, to each flux to avoid an undefined value of the term log(vi/(giV)) (Equation 9) when vi or gi are zero. For all computations we used ϵ=10−8. Also, in order to speed up CPU times, we constrained the fluxome space to the subset where the sum of all fluxes (V) are equal to a positive constant. For all Pheflux computations we set $V = 1000$, large enough, to avoid forcing any reaction to reach its bounds.
For SPOT and Pheflux, we assigned gene expression values to each reaction according to their gene-protein-reaction associations.19 We used the median value of this set as the gene expression value of any reaction without a reported gene-protein-reaction association.
## Data sources
We use previously published information73,99,100,101,102,103 of 5 microorganisms –yeasts and bacteria– grown under 21 culture conditions (see Table S1), which have transcriptomic and fluxomic information that correspond to RNA-seq/microarrays libraries and experimental fluxes using 13C labeled, respectively. For each microorganism we use the genome-scale metabolic models detailed in Table S1. For all bacteria and yeasts, we consider uptake rates for the genome-scale metabolic network according to the culture media conditions reported by the original authors.
For kidney, breast, and bronchus-lung normal and cancer human tissues, we use Recon3D metabolic network86 and transcriptomic data from TCGA (https://www.cancer.gov/tcga). We considered the set of uptake reactions as reported by Shen et al. [ 2019]104 for human tissues.
## Quantification and statistical analysis
In this study, differences between the statistics of tumoral and normal cancer cells were assess using the Mann-Whitney U test. Statistical details can be found in the figure legends, results and method details. All statistical analyses were performed using Python 3.8.
## Additional resources
Pheflux computational implementation and the code used to generate the results used in all figures can be accessed from the following GitHub repository: https://github.com/mrivas/pheflux.
## Supplemental information
Document S1. Figure S1 and Table S1
## Data and code availability
•This paper analyzes existing, publicly available data. These source references for the datasets are listed in the key resources table.•All original code has been deposited at Zenodo and is publicly available as of the date of publication. DOIs are listed in the key resources table.•Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
## Author contributions
N.G., R.C., and M.R. conceived the mathematical methods and experiments, N.G., I.I., and M.R. conducted the experiments and wrote the algorithms, N.G., R.C., and M.R. analyzed the results. All authors reviewed the manuscript.
## Declaration of interests
The authors declare no competing interest.
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|
---
title: Relationship of apolipoprotein(a) isoform size with clearance and production
of lipoprotein(a) in a diverse cohort
authors:
- Anastasiya Matveyenko
- Nelsa Matienzo
- Henry Ginsberg
- Renu Nandakumar
- Heather Seid
- Rajasekhar Ramakrishnan
- Steve Holleran
- Tiffany Thomas
- Gissette Reyes-Soffer
journal: Journal of Lipid Research
year: 2023
pmcid: PMC10006688
doi: 10.1016/j.jlr.2023.100336
license: CC BY 4.0
---
# Relationship of apolipoprotein(a) isoform size with clearance and production of lipoprotein(a) in a diverse cohort
## Body
Lipoprotein(a) [Lp(a)] has two main protein components: each particle has one apolipoprotein B100 (apoB100) molecule covalently bound to one apo(a) molecule [1, 2]. High levels of Lp(a) are causal for atherosclerotic cardiovascular disease (ASCVD) [3], as confirmed by large epidemiological studies [4], genome-wide association studies [5, 6], and Mendelian randomization studies [7, 8].
A distinct feature of this apoB100-containing lipoprotein is the variability of apo(a) size, with masses that range from 300 to 800 kDa [9, 10], due to the number of Kringle IV type 2 (KIV-2) repeats ranging from 1 to > 40 [11, 12]. Most people express two different apo(a) isoforms and these are synthesized in the liver.
Previous studies have shown a consistent and strong inverse relationship between plasma Lp(a) concentration and isoform size [13, 14]; high Lp(a) levels are associated with low numbers of KIV-2 repeats and small isoforms, whereas low Lp(a) levels are associated with high numbers of KIV-2 repeats and large isoforms. The KIV-2 repeat size polymorphism explains approximately $30\%$–$70\%$ of the variance in Lp(a) levels [15]. What regulates this relationship is not clear. The levels of Lp(a) in plasma are determined by the rate of entry of these particles into the circulation (production rate: PR) and the efficiency of their removal (fractional clearance rate: FCR). Several studies have examined the association of Lp(a) concentration with FCR and PR, with data suggesting that either or both play a role in the regulation of Lp(a) levels in the circulation [16, 17, 18, 19, 20, 21]. Some of these studies also interrogated the associations of circulating levels of individual apo(a) isoform sizes with FCR and PR. In two of the studies, isoform size was tightly associated with PR: smaller isoforms with fewer KIV-2 repeats have higher PRs [18, 20]. However, two other studies showed that both FCR and PR were affected by isoform size [19, 21]. Most of these studies were conducted in predominantly Caucasian populations and have not taken self-reported race and ethnicity (SRRE) differences into account. It is well established that Lp(a) concentrations differs by SRRE [22, 23]. To gain additional insights into this issue, we combined data from our previously completed studies of the effects of pharmacologic interventions on the kinetics of apo(a), considering the SRRE of the subjects in those studies [24, 25, 26]. Our results, using a weighted isoform size (wIS) for each subject, support previous studies indicating the isoform size is more closely related to PR than to FCR and that ancestry, as assessed by SRRE, impacts the association between isoform size and PR.
## Abstract
Lipoprotein(a) [Lp(a)] has two main proteins, apoB100 and apo(a). High levels of Lp(a) confer an increased risk for atherosclerotic cardiovascular disease. Most people have two circulating isoforms of apo(a) differing in their molecular mass, determined by the number of Kringle IV Type 2 repeats. Previous studies report a strong inverse relationship between Lp(a) levels and apo(a) isoform sizes. The roles of Lp(a) production and fractional clearance and how ancestry affects this relationship remain incompletely defined. We therefore examined the relationships of apo(a) size with Lp(a) levels and both apo(a) fractional clearance rates (FCR) and production rates (PR) in 32 individuals not on lipid-lowering treatment. We determined plasma Lp(a) levels and apo(a) isoform sizes and used the relative expression of the two isoforms to calculate a “weighted isoform size” (wIS). Stable isotope studies were performed, using D3-leucine, to determine the apo(a) FCR and PR. As expected, plasma Lp(a) concentrations were inversely correlated with wIS (R2 = 0.27; $$P \leq 0.002$$). The wIS had a modest positive correlation with apo(a) FCR (R2 = 0.10, $$P \leq 0.08$$) and a negative correlation with apo(a) PR (R2 = 0.11; $$P \leq 0.06$$). The relationship between wIS and PR became significant when we controlled for self-reported race and ethnicity (SRRE) (R2 = 0.24, $$P \leq 0.03$$); controlling for SRRE did not affect the relationship between wIS and FCR. Apo(a) wIS plays a role in both FCR and PR; however, adjusting for SRRE strengthens the correlation between wIS and PR, suggesting an effect of ancestry.
## Study population
The study subjects had participated in one of three separate stable isotope studies examining the effects on lipoprotein metabolism of [1] an apoB100 antisense, [2] an inhibitor of cholesteryl ester transfer protein, or [3] a monoclonal antibody against PCSK9 [24, 25, 26]. The studies were approved by the Columbia University Irving Medical Center Institutional Review Board. All subjects provided informed consent before enrolling in the studies, which included consent for the use of their study data and samples for future research. Due to mass spectrometry assay sensitivity limitations, we only included subjects with Lp(a) concentrations above 10 nmol/L. The present analysis uses the baseline (preintervention phase) studies of 32 healthy individuals of varying SRRE. Subjects were neither on lipid-lowering agents, nor were they taking over-the-counter supplements. None of the subjects had clinical ASCVD and were considered in good health as assessed by medical history and physical exam. The studies reported in this manuscript abide by the Declaration of Helsinki.
## Study design
Complete details of the stable isotope studies on the metabolism of apoB100 and apo(a) have been previously published [24, 25, 26]. Briefly, isocaloric, low-fat liquid meals ($57\%$ carbohydrate, $18\%$ fat, and $25\%$ protein) were started 8 hours (h) before stable isotope administration (1:00 AM on Day 1) and provided to subjects every 2 h for the next 32 h to maintain steady state metabolic conditions during the kinetic studies. Subjects received a bolus injection of 5,5,5-D3-leucine dissolved in 0.15 M NaCl (10 μmol/kg body weight) immediately followed by a constant infusion of D3-leucine dissolved in 0.15 M NaCl (10 μmol/kg body weight/hour) for 15 h. EDTA blood samples were collected at 18 predefined times over 24 h and plasma separated and stored at −80°C. Aliquots of these banked samples were utilized for this study; the samples had not been previously thawed or refrozen.
## Biochemical and immunological assays
Plasma lipids [total cholesterol (C), triglycerides (TGs), and high density lipoprotein (HDL)-C] were measured on an Integra400plus (Roche) from samples obtained at baseline. Plasma low density lipoprotein (LDL)-C levels were calculated using the *Friedewald formula* (no subject had a TG level >400 mg/dl). Plasma apoB100 levels were measured by a ELISA kit # 3715-1HP-2, from Mabtech, Inc, Cincinnati, OH.
## Apo(a) stable isotope enrichment determination
Apo(a) enrichment with D3-leucine was measured as described by Zhou et al. [ 27]. In brief, 200 μl of the LDL fraction or equal volumes of LDL (100 μl) and HDL (100 μl) fractions isolated from plasma by ultracentrifugation were desalted. Isolated lipoprotein fractions were then treated with dithiothreitol to open disulfide bonds, alkylated with iodoacetamide, and digested using trypsin. A multiple reaction monitoring method was used to monitor the following precursor-product ion transitions of a peptide specific to apo(a): (LFLEPTQADIALLK): 786.7 > 1069.7 (M0) and 788.2 > 1069.7 (M3). Two microliteres of the digested samples were analyzed using a nanoAcquity ultra-performance LC system coupled with an ionKey source integrated to a Xevo TQ-S triple quadrupole tandem mass spectrometer (Waters, Milford, Massachusetts). The separation was achieved using an iKey Peptide BEH C18 separation device (130 Ǻ, 1.7 μm, 150 μm × 100 mm) maintained at 60°C. The gradient was $90\%$ A ($0.1\%$ formic acid in water)/$10\%$ B ($0.1\%$ formic acid in acetonitrile) ramped linearly to $10\%$ A at 6 min, held for 3 min, and then reequilibrated to initial conditions (total run time: 12 min; flow rate: 3 μl/min). The multiple reaction transitions were monitored with a collision energy of 24 eV.
## Lp(a) concentration and apo(a) isoform size
Lp(a) plasma concentration was measured using the isoform-independent sandwich ELISA developed by the Northwest Lipid Metabolism and Diabetes Research Laboratory [28]. Apo(a) isoform size measurements were performed by the same laboratory. We started with 250 μl of plasma and each sample was diluted in saline to have 100 ng of protein in 40 μl, which was combined with an equal volume of reducing buffer and boiled for 10 min. The sample was then loaded onto an agarose gel and run overnight at 123V and 4°C, transferred to a nitrocellulose membrane, immunoblotted, and imaged using the ChemiDoc MP Imaging System to determine the isoforms (separated by size) present in the samples by comparison to in-house standards (combined material containing six apo(a) isoforms: 38, 32, 24, 19, 15, and 12 KIV-2 repeats) [29]. The relative expression of each isoform was determined using the Image Lab software, which calculated relative proportions of the two isoforms based on the intensity profile of each lane. The method has an intrasample variability under $15\%$.
## wIS calculation
Most individuals express two apo(a) isoforms in plasma, and these are inversely correlated with Lp(a) plasma levels, with smaller isoforms generally dominating [13]. To ascertain the contribution of isoforms to plasma Lp(a) concentration, we estimated a wIS. Each expressed isoform can potentially have a different FCR (equivalently, as used by some investigators, fractional synthetic rate), say, k1 and k2 for the two isoforms. As apo(a) is a slowly turning over protein and we use a primed constant infusion protocol in our studies, the apo(a) enrichment, when expressed as a fraction of the precursor plateau, goes up nearly linear during the 15 h infusion period and the rising slope, as a fraction of the plateau enrichment, equals the FCR or fractional synthetic rate. If E1, the enrichment of isoform 1, goes up with slope k1, and E2 goes up with slope k2, it can be seen that the overall enrichment E, which equals m1E1 + m2E2, goes up with slope m1k1 + m2k2, where m1 and m2 are the relative masses (i.e., mass fractions, m1 + m2 = 1) of the two isoforms, with the total mass denoted by M.
If there is a linear relationship (with intercept “a” and slope “b”) between isoform-specific FCR, termed k, and the corresponding isoform size, termed S, it means k = a + bS. Applying it to the two isoforms 1 and 2 above, the relationships become k1 = a + bS1 and k2 = a + bS2. It follows, then, that the combined apo(a) FCR, which is m1k1+m2k2, equals m1(a + b S1) + m2(a + bS2), which simplifies to a + b(m1S1 + m2S2). We define m1S1 + m2S2 as the wIS. Substituting, we see that the combined apo(a) FCR, termed kc, follows the relationship kc = a + b wIS. Thus, when we estimate a single apo(a) FCR, it bears the same linear relationship with wIS as the isoform-specific FCR would bear with the corresponding isoform size. Further, if we look at the total PR, which is the sum of the two isoform-specific PR1 and PR2, where PR1 = m1Mk1 and PR2 = m2Mk2, it follows that PR = m1Mk1 + m2Mk2 = M(m1k1 + m2k2) = Mkc. That is, the total PR equals the total mass multiplied by the FCR we estimate from the total enrichment data. When we calculate a single apo(a) PR, it bears the same relationship with wIS as the isoform-specific PR would bear with the corresponding isoform size.
Example: Say the two isoform masses are M1 and M2, so total Lp(a) mass is M = M1 + M2. If the two isoform sizes are 20 and 30, with relative expression of $70\%$ and $30\%$, respectively, the wIS is 0.7∗20+0.3∗30 = 23. If now, the two isoforms are cleared with rate constants k1 = 0.4 and k2 = 0.2, then, by the formulas above, PR1 = 0.7∗M∗0.4 = 0.28 M; PR2 = 0.3∗M∗0.2 = 0.06 M; kc = 0.7∗0.4 + 0.3∗0.2 = 0.34. We see that total PR = PR1 + PR2 = 0.34 M = kcM.
## Apo(a) modeling
The apo(a) enrichment data were modeled as previously described [24, 25, 26]. Apo(a) FCR was calculated by fitting the leucine enrichment data in an apo(a)-specific peptide using a single-pool model, with the precursor enrichment set as the VLDL apoB100 D3-leucine enrichment plateau in the same study. The plateau is typically reached during the first 15-h sampling period and estimated using our model for VLDL apoB100 metabolism [30, 31]. The apo(a) PR in nmol/kg/day was calculated as the product of apo(a) FCR (in pools/day) and the apo(a) concentration (nmol/L) multiplied by the plasma volume (estimated as 0.045 L/kg).
## Statistical analysis
All data were analyzed using standard R software functions [summary, lm, estimable, ggplot, etc.] invoked by our cufunctions package [32]. Variables found to be normally distributed are summarized by mean and SD, while Lp(a) levels, along with TGs, are summarized by median and interquartile range. Pearson correlation coefficients are reported. The relationship of Lp(a) levels with wIS in the three SRRE groups was studied by analysis of covariates (ANCOVA).
## RESULTS
The subject demographic data as well as plasma lipid and apoB100 levels are shown in Table 1. We analyzed data from 32 subjects with a mean age of 46.8 years. Seventeen subjects were female, and by SRRE, there were 17 Black, 9 Hispanic, and 6 White subjects. The mean BMI was 28.9 ± 4.3 kg/m2. Lipid and apoB100 levels were within normal ranges. Table 1Population demographics, lipid, and ApoB100 levelsCharacteristicStudy SampleAge, y46.8 ± 12.4Range for age26–68BMI, kg/m228.9 ± 4.3Race (n)FemaleMaleWhite24Black98Hispanic63Total cholesterol, mg/dL173 ± 38.8Triglycerides, mg/dL109 (52.5, 143)LDL-C, mg/dL103 ± 28.9HDL-C, mg/dL51.8 ± 17.2Plasma ApoB100, mg/dL81.2 ± 22.1Legend: ± represents Mean and Standard Deviation; [] represents Median and Interquartile Range.
## Lp(a) levels and Apo(a) kinetics
The study population had median Lp(a) levels of 54.6 nmol/L (interquartile range 36.8–119.0) (Table 2). Plasma Lp(a) levels did not differ between males and females (data not shown). Participants had a mean wIS of 22.8 ± 4. As expected from published data, Black subjects had a higher median Lp(a) concentration. In our cohort, 9 of the 32 individuals ($28\%$) had only one detectable isoform. Mean ± SD FCR and PR of apo(a) were 0.18 ± 0.08 pools/day and 0.57 ± 0.40 nmol/kg/day, respectively (Table 2). Individual data for the full cohort are provided in supplemental Table S1.Table 2Lp(a) Plasma levels, weighted isoform size, and kinetic parameters of Apo(a)CharacteristicStudy SampleLp(a) (nmol/L)54.6 (36.8, 119.0) Black61.2 (43.7, 127.6) Hispanic42.0 (21.5, 116.4) White49.8 (27.1, 62.3)Weighted isoform size22.8 ± 4.0Apo(a) FCR (pools/day)0.18 ± 0.1Apo(a) PR (nmol/kg/day)0.57 ± 0.4PR, production rate; FCR, fractional clearance rateLegend: ± represents Mean and Standard Deviation; [] represents Median and Interquartile Range.
As observed in larger population data sets, our subjects had an inverse relationship between Lp(a) levels and wIS (R2 = 0.27, $$P \leq 0.002$$) (Fig. 1A). Lp(a) levels are impacted by SRRE, hence we examined the relationship between Lp(a) levels and wIS for each SRRE group, Fig. 1B. Although adjustment for SRRE strengthened the overall correlation (R2 = 0.35), SRRE group differences were not statistically significant [Black-Hispanic ($$P \leq 0.25$$); Black-White ($$P \leq 0.11$$); Hispanic-White ($$P \leq 0.57$$)]. The lack of significance may be due to the small number of subjects in each SRRE group. The relationship between individual apo(a) isoforms and the Lp(a) levels associated with each isoform in the combined cohort of all subjects (Black, Hispanic, and White) was also statistically significant ($P \leq 0.0001$) (supplemental Fig. S1). Isoform size is a determinant of Lp(a) concentration, and it is known that SRRE plays a role in determining Lp(a) levels at any isoform size, thus we included SRRE in all our data analyses examining the relationships of wIS with the kinetics of apo(a).Fig. 1(A) Negative association of Lp(a) levels with wIS. B: Negative association of Lp(a) levels with wIS, controlling for SRRE using ANCOVA (Analysis of Covariance). Lp(a), lipoprotein(a); wIS, weighted isoform size; SRRE, self-reported race/ethnicity; B, Black; H, Hispanic; W, White.
The relationship between Lp(a) levels and apo(a) FCR (R2 = 0.07, $$P \leq 0.16$$) was not statistically significant (supplemental Fig. S2). When we examined the relationships of FCR with wIS, we found a positive trend ($$P \leq 0.08$$) (Fig. 2A). Controlling for SRRE did not affect the relationship of wIS with FCR (Fig. 2B).Fig. 2Relationship between apo(a) FCR with wIS before (A) and after controlling for SREE (B) using ANCOVA (Analysis of Covariance). Relationship of apo(a) PR with wIS before (C) and after controlling for SREE (D) using ANCOVA (Analysis of Covariance). FCR, fractional catabolic rate; PR, production rate; wIS, weighted isoform size; SRRE, self-reported race/ethnicity; B, Black; H, Hispanic; W, White.
Additionally, wIS showed a negative trend with apo(a) PR ($$P \leq 0.06$$) (Fig. 2C) and this relationship became statistically significant when controlling for SRRE ($$P \leq 0.03$$) (Fig. 2D).
The results above comprised all 32 subjects, including 9 individuals who expressed only a single isoform (supplemental Table S1). In this subgroup with single isoforms, we found that wIS and FCR were positively correlated (R2 = 0.61, $$P \leq 0.01$$) but no correlations were found between the single isoforms and PR (R2 = 0.20, $$P \leq 0.23$$) (supplemental Fig. S3A, B).
## DISCUSSION
High plasma Lp(a) levels are associated with an increased risk for ASCVD [3, 23]. The pathways regulating Lp(a) levels are not well understood, and this has been recently reviewed [33, 34, 35]. Similar to other cohorts [13, 14, 36, 37], the current study finds an inverse association between plasma Lp(a) levels and apo(a) allele size, with smaller isoforms associated with higher Lp(a) levels (supplemental Fig. S1). Some studies have also found smaller apo(a) isoforms to be associated with coronary artery disease [7, 38, 39, 40, 41, 42], although only two of these studies demonstrated that association to be independent of Lp(a) concentration.
## Relationship of plasma Lp(a) concentrations with PR and FCR
Previous reports support a role of production and/or clearance regulating Lp(a) plasma levels; these have been reviewed [33, 34, 35]. Studies by Krempler et al. and Rader et al. found that PR, but not FCR, correlated with Lp(a) levels [17, 43]. Our study agrees with these findings, as we found no significant relationship between Lp(a) levels and FCR (supplemental Fig. S2). Similarly, in a large study of the effects of a PCSK9 inhibitor on apoB metabolism in individuals without concomitant statin therapy, a treatment-associated decrease in the plasma pool size of Lp(a)-apo(a) was linked with a decrease in the PR of Lp(a)-apo(a), with no effect on FCR in the subjects not on statins. However, in the group taking statins, treatment with the PCSK9 inhibitor resulted in an increase in the FCR of Lp(a)-apo(a) with no treatment-effect on the PR of Lp(a)-apo(a) [44]. These studies and other reports [21] support a role of both FCR and PR in the regulation of plasma Lp(a) levels.
## Relationship of apo(a) isoforms with PR and FCR
As previously stated, an individual’s plasma Lp(a) level, with few exceptions, is highly regulated by the number of KIV-2 repeats present in their apo(a) isoforms, which are determined by the LPA gene. Early studies in cultured liver cells, using both steady state labeling and pulse chase analyses, showed that the endoplasmic reticulum residence time of secreted apo(a) isoforms is determined by their size, and that this accounted for the inverse relationship between isoform size and level of secretion. The authors concluded that apo(a) posttranslational stability is a major determinant of the levels of plasma Lp(a) in baboons [45]. Additional cell work provided support for the important role of the number of KIV-2 repeats in the rate of assembly and secretion of apo(a) [46, 47, 48]. Human studies using externally labeled Lp(a) demonstrated the importance of PR in determining plasma levels of Lp(a) in subjects with varying Lp(a) levels and either similar apo(a) isoforms [17] or varying apo(a) sizes [18]. The latter study showed an inverse correlation between apo(a) size and PR of apo(a). In both of these studies, the FCRs of Lp(a) were not related to the concentration of plasma Lp(a). On the other hand, Jenner et al. reported that isoform size, determined by gel electrophoretic separation, affected both the PR and FCR of apo(a) in studies using endogenous labeling of Lp(a) with stable isotopes [21]. Subjects with smaller isoforms had higher PRs of apo(a), similar to the findings of Rader et al., but they also had lower apo(a) FCRs, the latter similar to our current findings. These previous studies did not determine and take into account participant SRRE.
Advances in mass spectrometry and methods to isolate Lp(a) have enhanced our ability to interrogate the mechanisms that regulate Lp(a) [33]. In this current study, we used wIS (see Methods), which captures the contribution of each isoform to the Lp(a) level in the circulation. The use of isoform expression to calculate isoform specific Lp(a) plasma levels has been applied in earlier studies [38]. Calculation of the wIS suffers from some limitations listed below, yet it allowed us to assess the effects of a weighted mean of two expressed isoforms on FCR and PR of apo(a). We found that wIS had only modest correlations with both PR and FCR. Our results are consistent with previous reports that found strong relationships between allele size and PR but also identified trends with FCR [17, 18, 19, 21, 49, 50]. As seen in Fig. 2A, one individual in our cohort had a very large wIS and excluding this individual from the analysis improved the relationship between wIS and FCR ($P \leq 0.02$). Relevant to our current findings, a sub-analysis by Chan et al. [ 19] of the baseline results obtained from a study of the effects of evolocumab on the kinetic of Lp(a) metabolism, found that levels of Lp(a) were negatively associated with apo(a) size and FCR and positively associated with PR. Moreover, in subjects with small isoforms (≤22 KIV-2), they found strong correlations between apo(a) concentration and increased apo(a) PR but not with FCR. In subjects with large isoforms (>22 KIV-2), on the other hand, Lp(a) levels were correlated with both kinetic parameters [19]. The authors found similar associations in the subjects treated with either statin alone, evolocumab alone, or the combination of the two treatments. They demonstrated that Lp(a) lowering with a PCSK9 inhibitor, evolocumab, lowered plasma Lp(a) levels by decreasing apo(a) PR and increasing apo(a) FCR. We found similar results when administering the PCSK9 inhibitor, alirocumab [25]. Importantly, different nontargeted Lp(a)-lowering treatments decrease Lp(a) by different effects on FCR and PR. Niacin lowered plasma Lp(a) levels in association with decrease in both PR and FCR [51]. Mipomersen, an apoB antisense oligonucleotide (ASO), reduced Lp(a) by increasing FCR, although PR was reduced as well in some individuals [24]. Anacetrapib, a CETP inhibitor, decreased Lp(a) by decreasing PR [26]. The results from those studies support a complex regulatory mechanism of Lp(a) levels. This may be due to the additional proteins and lipids found on and within Lp(a) particles [52]. Lastly, the exact location where the covalent linkage of apo(a) to apoB100 assembly occurs (intrahepatic or at the surface of the liver), as well as the site and molecular mechanism of Lp(a) clearance from plasma are not completely defined [33, 34, 35]. Recent studies using cell models with a single isoform (17 KIV-2 repeats), found that, in addition to a covalent disulfide bond between apo(a) and apoB100, there are also noncovalent interactions between these two proteins [53]. The latter observations, if true in vivo, could affect measurements of FCR. Additionally, free apo(a) fragments have been found in plasma and urine but their concentrations are very low and their physiological role, if any, were poorly understood [54, 55].
Due to clear racial differences in the relationship of isoform size and plasma Lp(a) levels [56, 57], it is important to control for these when analyzing such data. Since our study population was composed of a diverse cohort, we controlled for any effects of SRRE on the analyzed study outcomes. When we adjusted for SRRE, the relationship between wIS and PR was statistically significant.
Lp(a) lowering with nontargeted and targeted treatments decreases both isoforms. Of interest, the relative expression of apo(a) isoforms does not change after Lp(a) levels are lowered using ASO apo(a) treatment [58]. The latter result suggests that apo(a) ASO treatment does not preferentially affect one isoform size over the other. Similarly, in data from our lab, we have not observed treatment effects on wIS after various therapies that lower apoB100 and apo(a) (supplemental Table S2). A recent study using PCSK9 inhibitors showed a positive correlation between apo(a) size and reductions in Lp(a) levels for both small and large isoforms of apo(a) [59]. Recent studies using a targeted siRNA therapy showed significant Lp(a) lowering but no isoform size data have been presented [60, 61].
Lastly, there are studies examining the roles of single nucleotide polymorphisms (SNP) present in the LPA gene within the KIV2 region that are linked to high and low Lp(a) levels. The allele frequencies of these SNPs have been found to differ across SRRE groups [22, 62, 63]. These differences in SNP presentation could explain why Lp(a) levels differ for similar isoform sizes in different SRRE groups. The effect of these SNP on the clearance and production of Lp(a) has not been studied.
Study Limitations: Our results indicate that apo(a) isoforms have a significant yet modest contribution to the mechanisms regulating apo(a) FCR and PR. Although we did include subjects with different SRRE in this study, our study population was small, with 17 of our 32 subjects identifying as Black, leaving very few subjects in the other groups. Mechanistic studies are costly and labor extensive, hence it will be difficult to perform studies in large populations with adequate sample size for different SRRE groups. Our results, however, highlight the need to recruit diverse cohorts when designing these studies. Berglund et al. examined the role of isoforms in larger diverse cohorts [56] showing associations similar to those found in our cohort, with Blacks having higher Lp(a) level for the same apo(a) isoform size than Whites, even though the difference did not reach statistical significance in our study due to the limited subject number. However, the study by Berglund et al. did not examine metabolic pathways [56].
There were methodologic limitations: In the current study, we did not isolate individual apo(a) isoforms and calculate their unique FCR and PR; instead, we used the relative expression data from gel electrophoresis to estimate their contributions to the wIS. We examined the kinetics of apo(a) isolated from LDL or LDL+HDL fractions. However, the apo(a) PR and FCR from LDL-only or from LDL plus HDL fractions were not statistically different (supplemental Fig. S4) and the data were, therefore, combined for all analyses. Apo(a) measurements were performed on plasma samples by a validated ELISA [28] and not on the mass spectrometry used to obtain enrichments. Various methods have been proposed to measure apo(a) via mass spectrometry [64], however we did not have these methods available at the time of the study.
## Data availability
All the data generated during and/or analyzed during the current study are available from the corresponding author and are included in this published article and its supplementary information file.
## Supplemental data
This article contains supplemental data [25, 65, 66].
Supplemental data
## Conflict of interest
The authors declare that they have no conflicts of interest with the contents of this article
## Author contributions
A. M., R. R., S. H., and G. R.-S. data analysis; A. M., N. M., R. N., H. S., T. T., and G. R.-S. investigation; A. M, G. R.-S. writing-original draft preparation; N. M., A. M. visualization; H. G., R. R., and T. T. reviewing and editing; R. N. validation; G. R.-S. conceptualization; G. R.-S. methodology; G. R.-S. supervision; G. R.-S. funding; G. R.-S. resources. Reyes-Soffer, R01 HL139759 and UL1TR001873.
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|
---
title: 'Exercise training reduces circulating cytokines in male patients with coronary
artery disease and type 2 diabetes: A pilot study'
authors:
- Léa Garneau
- Tasuku Terada
- Matheus Mistura
- Erin E. Mulvihill
- Jennifer L. Reed
- Céline Aguer
journal: Physiological Reports
year: 2023
pmcid: PMC10006733
doi: 10.14814/phy2.15634
license: CC BY 4.0
---
# Exercise training reduces circulating cytokines in male patients with coronary artery disease and type 2 diabetes: A pilot study
## Abstract
Low‐grade inflammation is central to coronary artery disease (CAD) and type 2 diabetes (T2D) and is reduced by exercise training. The objective of this study was to compare the anti‐inflammatory potential of moderate‐to‐vigorous intensity continuous training (MICT) and high‐intensity interval training (HIIT) in patients with CAD with or without T2D. The design and setting of this study is based on a secondary analysis of registered randomized clinical trial NCT02765568. Male patients with CAD were randomly assigned to either MICT or HIIT, with subgroups divided according to T2D status (non‐T2D‐HIIT $$n = 14$$ and non‐T2D‐MICT $$n = 13$$; T2D‐HIIT $$n = 6$$ and T2D‐MICT $$n = 5$$). The intervention was a 12‐week cardiovascular rehabilitation program consisting of either MICT or HIIT (twice weekly sessions) and circulating cytokines measured pre‐ and post‐training as inflammatory markers. The co‐occurrence of CAD and T2D was associated with increased plasma IL‐8 ($$p \leq 0.0331$$). There was an interaction between T2D and the effect of the training interventions on plasma FGF21 ($$p \leq 0.0368$$) and IL‐6 ($$p \leq 0.0385$$), which were further reduced in the T2D groups. An interaction between T2D, training modalities, and the effect of time ($$p \leq 0.0415$$) was detected for SPARC, with HIIT increasing circulating concentrations in the control group, while lowering them in the T2D group, and the inverse occurring with MICT. The interventions also reduced plasma FGF21 ($$p \leq 0.0030$$), IL‐6 ($$p \leq 0.0101$$), IL‐8 ($$p \leq 0.0087$$), IL‐10 ($p \leq 0.0001$), and IL‐18 ($$p \leq 0.0009$$) irrespective of training modality or T2D status. HIIT and MICT resulted in similar reductions in circulating cytokines known to be increased in the context of low‐grade inflammation in CAD patients, an effect more pronounced in patients with T2D for FGF21 and IL‐6.
We compared the efficacy of high intensity interval training (HIIT) and moderate intensity continuous training (MICT) in improving low‐grade inflammation in patients with coronary artery disease (CAD) with or without type 2 diabetes (T2D). We observe and report that HIIT and MICT were similarly effective in decreasing the levels of the circulating inflammatory markers in both patients with CAD and patients with CAD and T2D. Furthermore, the training interventions showed a greater effect in the reduction of fibroblast‐growth factor 21 (FGF21) and interleukin‐6 (IL‐6) in patients with CAD and T2D than in patients with CAD alone.
## INTRODUCTION
Coronary artery disease (CAD) is the most common form of heart disease. Following coronary artery revascularization, patients with CAD are referred to cardiovascular rehabilitation programs to improve patients' physical and mental health (Luepker et al., 1996; Mampuya, 2012; Warburton et al., 2007). Conventional cardiovascular rehabilitation programs include moderate‐to‐vigorous intensity continuous training (MICT) to assist patients in attaining weekly exercise recommendations (i.e., 150 min of moderate‐to‐vigorous‐intensity exercise per week in combination with light physical activity; Ross et al., 2020), with at least two sessions per week incorporating exercises to strengthen muscles and bones (resistance training; Tremblay et al., 2011) and improving their cardiovascular health. Type 2 diabetes (T2D) is a non‐communicable chronic disease often associated with a sedentary lifestyle (prolonged sitting time) and/or lack of physical activity, and excess body weight (Booth et al., 2012; Hu et al., 2003; Knowler et al., 2002). T2D is also strongly associated with the development and progression of CAD (Stern, 1995). In a study evaluating the long‐term outcomes of coronary artery bypass graft surgery (CABG) in patients with CAD, the presence of T2D resulted in increased morbidity and mortality over the first 10 years following the surgery, and this increase was exacerbated in patients taking anti‐hyperglycemic medications and insulin (Kogan et al., 2018).
Individuals who suffer from T2D often experience difficulties in engaging in regular exercise due to physical discomforts associated with their disease (e.g., fear of injury, pain related to movement, hypoglycemia, and tiredness), as well as psychological factors affecting their participation in exercise (e.g., depression, shame, lack of motivation, laziness, and fear of the perception of others) (Korkiakangas et al., 2009). Regardless of existing pathologies, lack of time is frequently cited as a barrier to regular exercise participation and meeting current World Health Organization activity guidelines (i.e., at least 150 min/week of moderate to vigorous aerobic exercise) (Advika et al., 2017). Many researchers have, thus, explored high‐intensity interval training (HIIT) as an alternative to traditional MICT in achieving similar or superior physical and mental health improvements in less time. HIIT has shown promise for the management of T2D as it was shown to induce similar or greater improvements in glucose homeostasis when compared to MICT, even with $45\%$ reduced training volume (Winding et al., 2018).
Atherosclerosis is driven by the accumulation of cholesterol within the arterial wall and chronic non‐resolving inflammation (Libby et al., 2002), which is also an important feature of metabolic syndrome, insulin resistance, and T2D (Hotamisligil, 2006). The state of chronic inflammation induced by imbalances between the regulation of pro‐inflammatory cytokines and chemokines (e.g., interleukin [IL]‐1b, tumor necrosis factor [TNF]‐a, IL‐6 and IL‐8) and anti‐inflammatory cytokines (e.g., IL‐10 and IL‐13) results in impaired glucose and lipid metabolism, tissue dysfunction, and contributes to residual inflammatory risk in cardiovascular death (Hotamisligil, 2017). This underscores the importance of reducing chronic inflammation in patients with CAD with or without T2D. In a recent meta‐analysis examining the effect of exercise interventions (HIIT ~$27\%$ of all studies; MICT ~$76\%$; MICT or HIIT in combination with resistance training ~$22\%$) on circulating inflammatory markers in patients with CAD, neither significant reductions in TNF‐a, IL‐6 or IL‐8 concentrations, nor increases in IL‐10 were found; however, a reduction in C‐reactive protein (CRP) suggested diminished acute phase inflammation without changes in chronic inflammatory markers (Thompson et al., 2020). Direct comparisons of the anti‐inflammatory potential of HIIT and MICT are scarce in the literature and both short‐ (2 weeks) (Barry et al., 2018; Robinson et al., 2015) and longer‐term (10–12 weeks) interventions (Bartlett et al., 2017; Mallard et al., 2017) resulted in no changes in circulating inflammatory markers in various populations (healthy individuals, patients with obesity and/or T2D). The exception is one 8‐week protocol of HIIT which led to increased serum CRP and IL‐6, while no significant change was measured with the MICT protocol in the same cohort of overweight or obese participants (Vella et al., 2017). Because most comparisons between exercise modalities are drawn through meta‐analyses with few direct comparisons of HIIT and MICT interventions in clinical populations, the effects of such training on reducing chronic inflammation remain unknown.
The current study assessed the impact of 12 weeks of HIIT‐ or MICT‐based cardiovascular rehabilitation on plasma cytokine concentrations in patients with CAD with or without T2D. We examined 11 target cytokines (IL‐1b, TNF‐a, CRP, secreted protein acidic rich in cysteine [SPARC], fibroblast growth factor 21 [FGF21], IL‐6, IL‐8, IL‐10, IL‐13, IL‐15, and IL‐18) known to be altered in the presence of obesity and/or T2D, and to be regulated by acute and/or chronic exercise (Garneau & Aguer, 2019). It was hypothesized that HIIT and MICT modalities would induce a more significant reduction in circulating cytokines concentrations in the patients with CAD and T2D, as these patients are affected with more pronounced chronic inflammation. As HIIT has been shown to further improve glucose homeostasis than MICT in patients with T2D, we also sought to determine if this training modality would be more effective in reducing inflammatory markers in these patients.
## Study population
The samples were obtained from the previously published cardiac rehabilitation exercise modalities study (CRX‐Modalities; clinical trial NCT02765568), a randomized clinical trial conducted at the University of Ottawa Heart Institute (UOHI) designed to compare the efficacy of alternative cardiac rehabilitation modalities on short‐ and long‐term physical and mental health outcomes in patients with CAD (Reed et al., 2021). This protocol was approved by the Ottawa Health Sciences Network Research Ethics Board (protocol #: 20160127‐01H). The patients were randomized following the baseline phase in a 1:1:1 ratio between groups in a sex (male vs. female) and age (<60 vs. ≥60 years) stratified manner using a computer‐generated sequence as previously described (Reed et al., 2021). For the current study, only the participants assigned to MICT and HIIT exercise modalities from the original study were included. Participants were assigned to subgroups with or without the comorbidity of T2D (participants without T2D: HIIT‐non‐T2D and MICT‐non‐T2D; patients with T2D: HIIT‐T2D and MICT‐T2D).
The inclusion and exclusion criteria were previously described (Reed et al., 2021), with the exception that only male participants were included in the current study to avoid the confounding effects of unbalanced biological distribution since there were no female participants with T2D. Included participants were patients with CAD aged 40–74 years old who previously underwent a percutaneous coronary intervention or CABG in the previous 4–18 weeks. These patients were also referred to the UOHI cardiovascular rehabilitation program, able to walk autonomously, and willing to attend the on‐site twice weekly cardiovascular rehabilitation program for 12 weeks. Exclusion criteria included: current participation in structured exercise training (>2 days/week), inflammatory disease or active infection, persistent or permanent atrial fibrillation, unstable angina or established diagnosis of chronic obstructive pulmonary disease, severe mitral or aortic stenosis, or hypertrophic obstructive cardiomyopathy, unable to read French or English, or unwilling or unable to return for follow‐up visits at week 12.
## Demographic, anthropometric, functional, and metabolic characteristics
Medical information including medications was obtained from clinical databases. At baseline (pre‐) and follow‐up (post‐; within 1 week of completing the 12‐week intervention), the participants' height (baseline measurement only), body mass, waist circumference, body composition (bioelectrical impedance analysis; UM‐041, Tanita, Roxton Industries Inc., Kitchener, ON), resting heart rate (RHR) and blood pressure were measured, and a fasting blood sample was collected to obtain plasma and subsequently measure glucose concentration and glycated hemoglobin levels (HbA1c). A 6‐min walk test (6MWT) was also performed at baseline and follow‐up (Reed et al., 2021). The HIIT participants also underwent a peak graded exercise test on a treadmill to establish peak HR with an electrocardiogram (Way et al., 2020), as is standard practice at the UOHI for higher‐intensity exercise in this patient population.
## Exercise interventions
As previously described (Reed et al., 2021), study participants were randomized to MICT or HIIT modalities. Both training protocols were 12 weeks in duration with twice weekly exercise classes performed on‐site at the UOHI. Strength training programs were provided to the participants regardless of group assignment, and they were encouraged to perform one weekly session of strength training exercises on their own (e.g., shoulder press and raise, bent over row, elbow flexion and extension, chest press, squat, lunge, push up, and core exercises). Participants were also instructed to perform 200–400 weekly minutes of moderate‐to‐vigorous aerobic exercise outside their cardiovascular rehabilitation program.
## High‐intensity interval training
Classes were 45 min in duration, beginning with 10 min of warm‐up at $60\%$–$70\%$ peak HR, then four training blocks consisting of 4‐min high‐intensity work periods at $85\%$–$95\%$ peak HR interspersed with 3‐min low intensity work periods of at $60\%$–$70\%$ peak HR for a total of 28 min and concluding with 5–10 min of cool‐down at $60\%$–$70\%$ peak HR consisting of strength and stretching exercises. The participants choose to perform the HIIT sessions either on aerobic exercise equipment (treadmill, cycle ergometer, elliptical, etc.) or aerobic dance/movement sequences. HRs were monitored directly on the exercise equipment or with a Polar HR monitor (Polar RS800CX, Polar Electro Oy, Kempele, Finland).
## Moderate‐to‐vigorous intensity continuous training
Classes were 1 h in duration, beginning with 10–15 min of walking or low‐intensity use of the exercise equipment as warm‐up, then 10–15 min of continuous aerobic exercise (walking or jogging, cycling, elliptical or rowing) for the first 3 weeks, progressing to 30 min of continuous exercise for the remaining weeks at a HR 20–40 bpm above resting values, and concluding with 15 min of cool‐down consisting of strength and stretching exercises. HRs were monitored using a Polar HR monitor, the participant's own device (e.g., Apple watch) or manual palpation.
## Cytokine quantification
The target cytokines were measured in plasma samples collected from the participants at baseline and follow‐up (Week 12) using three single‐plex assays (i.e., CRP U‐plex; SPARC and FGF21 R‐plex) and two multiplex assays (i.e., IL‐1b and TNF‐a U‐plex; IL‐6, IL‐8, IL‐10, IL‐13, IL‐15 and IL‐18 U‐plex) from Meso Scale Discovery (Rockville, MD, USA) following the manufacturer's instructions and as previously published (Garneau et al., 2020). The antibodies in all the assays were validated for target specificity with the exception of SPARC. Information about tested specificity can be found on the datasheet of the U‐plex antibody products (https://www.mesoscale.com/). The intra‐assay coefficient of variation for the standards were: $4.37\%$ for IL‐1b, $4.60\%$ for TNF‐a, $2.28\%$ for CRP, $6.45\%$ for SPARC, $2.85\%$ for FGF21, $4.30\%$ for IL‐6, $3.10\%$ for IL‐8, $4.95\%$ for IL‐10, $6.60\%$ for IL‐13, $5.65\%$ for IL‐15, and $2.95\%$ for IL‐18. The lower and upper limits of detection of each antibody were as follows: 0.227 and 4530 pg/mL for IL‐1b, 0.358 and 2900 pg/mL for TNF‐a, 0.427 and 5780 pg/mL for CRP, 0.443 and 1000 ng/mL for SPARC, 0.58 and 20,000 pg/mL for FGF21, 0.1 and 2060 pg/mL for IL‐6, 0.06 and 2010 for IL‐8, 0.08 and 3610 pg/mL for IL‐10, 2.26 and 2440 for IL‐13, 0.6 and 3000 pg/mL for IL‐15, and 0.28 and 39,100 pg/mL for IL‐18.
## Statistical analyses
The age of the participants in the four different groups was compared using two‐way ANOVA, with the factors being type of training and T2D status, and Šidák's multiple comparison was used as a post‐hoc test. Data relating to the characteristics of the participants (BMI, waist circumference, body fat %, systolic and diastolic blood pressure, RHR, fasted blood glucose, glycated hemoglobin (HbA1c) as well as 6MWT results) were first assessed for normality and lognormality in the subgroups using the D'Agostino‐*Pearson omnibus* K2 test. When normality was not achieved, but the data followed a lognormal distribution, they were transformed to their logarithms before further analyses. The data were analyzed by three‐way ANOVA with repeated measures or mixed effects analysis (when time‐points were missing) with the different factors being training type, T2D status and time‐point of intervention (baseline and follow‐up). Šidák's multiple comparisons was used as a post‐hoc test.
Plasma cytokine levels were first analyzed using the ‘identify outliers’ function of Prism 9 with the ROUT method ($Q = 1$%) by subgroup. Any identified outlier was then omitted during subsequent analyses. The remaining data were then assessed for normality and lognormality using the D'Agostino‐*Pearson omnibus* K2 test as previously described, and the necessary transformations were performed. The cytokine concentrations or their logarithm were then analyzed using three‐way ANOVA with repeated measures or mixed effects analysis when data points for pre‐ or post‐training intervention were missing. Šidák's multiple comparisons was used as a post hoc test. When applicable, the transformed variables were used for statistical analyses; however, non‐adjusted values are reported in the results for descriptive purposes. All statistical analyses were performed with the Prism 9 software from GraphPad (San Diego, CA, USA). A p‐value <0.05 was considered significant for all tests.
## Impact of the training interventions on participants' characteristics and functional capacity
The number of participants in each group were as follows: $$n = 14$$ for non‐T2D‐HIIT, $$n = 13$$ for non‐T2D‐MICT, $$n = 5$$ for T2D‐HIIT, and $$n = 6$$ for T2D‐MICT. On average, participants were classified as obese (average BMI: 30.4 kg/m2) and were normotensive (average systolic/diastolic blood pressure: $\frac{123}{79}$ mmHg) due to medical management. The list of medications taken by the participants is reported in Appendix A.
The anthropometric and metabolic characteristics of the study participants pre‐ and post‐training interventions are presented in Table 1. No significant differences were found in any group at baseline or in response to the training interventions for age, BMI, waist circumference, body fat %, resting systolic or diastolic blood pressure. There was a significant interaction between the effect of time and training modality on changes in systolic blood pressure ($$p \leq 0.0455$$), such that values increased in the HIIT groups, but decreased in the MICT groups following the training interventions. We found no effect of time or training modality on fasting blood glucose concentrations, although these were significantly increased in the groups of patients with than without T2D ($$p \leq 0.0015$$). As expected, the HbA1c values of both groups with T2D pre‐ and post‐training interventions were higher than those of participants without T2D ($p \leq 0.0001$), with no significant effect of the training interventions. Following the interventions, regardless of training modality or T2D status, participants' RHR was reduced compared to baseline measurements ($$p \leq 0.0129$$) and the distance achieved during the 6MWT increased ($p \leq 0.0001$).
**TABLE 1**
| Unnamed: 0 | Non‐T2D groups | Non‐T2D groups.1 | Non‐T2D groups.2 | Non‐T2D groups.3 | T2D groups | T2D groups.1 | T2D groups.2 | T2D groups.3 | p‐values | p‐values.1 | p‐values.2 | p‐value interactions | p‐value interactions.1 | p‐value interactions.2 | p‐value interactions.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | HIIT (n = 14) | HIIT (n = 14) | MICT (n = 13) | MICT (n = 13) | HIIT (n = 5) | HIIT (n = 5) | MICT (n = 6) | MICT (n = 6) | Effect of time | Effect of training modality | Effect of T2D | Time and training modality | Time and T2D | Training modality and T2D | Time, training modality and T2D |
| | Pre‐ | Post‐ | Pre‐ | Post‐ | Pre‐ | Post‐ | Pre‐ | Post‐ | Effect of time | Effect of training modality | Effect of T2D | Time and training modality | Time and T2D | Training modality and T2D | Time, training modality and T2D |
| Age (years) | 60.2 ± 7.8 | 60.2 ± 7.8 | 62 ± 7.2 | 62 ± 7.2 | 61.8 ± 3.6 | 61.8 ± 3.6 | 56.8 ± 7.5 | 56.8 ± 7.5 | — | 0.4917 | 0.5410 | — | — | 0.1987 | — |
| BMI (kg/m2) | 30.5 ± 5.9 | 30.2 ± 6.4 | 29.9 ± 3.4 | 29.7 ± 3.4 | 30.9 ± 2.9 | 31.4 ± 2.7 | 32.2 ± 10.2 | 30.6 ± 9.8 | 0.2400 | 0.6903 | 0.6856 | 0.1113 | 0.2394 | 0.8078 | 0.1903 |
| Waist circumference (cm) | 104.1 ± 11.9 | 104.8 ± 13.7 | 104.3 ± 8.3 | 111.1 ± 23.0 | 107.7 ± 7.6 | 109.2 ± 7.5 | 107.9 ± 22.5 | 105.4 ± 20.9 | 0.6926 | 0.7188 | 0.5141 | 0.0516 | 0.7489 | 0.7136 | 0.2461 |
| Body fat (%) | 28.6 ± 6.7 | 27.7 ± 5.5 | 28.4 ± 5.5 | 28.8 ± 4.7 | 30.6 ± 4.7 | 29.8 ± 4.1 | 28.0 ± 5.9 | 25.2 ± 4.2 | 0.1101 | 0.3776 | 0.9267 | 0.6315 | 0.3106 | 0.3234 | 0.3348 |
| Systolic blood pressure (mmHg) | 120.3 ± 13.5 | 123.5 ± 14.6 | 127.2 ± 14.2 | 125.1 ± 12.1 | 116.2 ± 9.8 | 125.2 ± 20.5 | 121.5 ± 15.3 | 115.3 ± 16.3 | 0.7484 | 0.9965 | 0.3800 | 0.0455 | 0.7545 | 0.5190 | 0.3781 |
| Diastolic blood pressure (mmHg) | 79.9 ± 9.0 | 83.8 ± 10.3 | 81.3 ± 11.1 | 81.9 ± 7.5 | 76.6 ± 6.0 | 80.2 ± 8.7 | 76.5 ± 9.4 | 71.8 ± 10.5 | 0.6853 | 0.3847 | 0.0904 | 0.1374 | 0.8070 | 0.4427 | 0.6484 |
| Resting heart rate (beats/min) | 58.9 ± 8.3 | 58.1 ± 7.1 | 62.8 ± 13.6 | 57.7 ± 10.0 | 64.6 ± 8.3 | 58.6 ± 8.6 | 65.1 ± 10.0 | 58.5 ± 9.7 | 0.0129 | 0.8863 | 0.5471 | 0.5946 | 0.2847 | 0.7671 | 0.4291 |
| Fasting blood glucose (mmol/L) | 5.23 ± 0.46 | 5.41 ± 0.51 | 5.55 ± 0.44 | 5.20 ± 0.60 | 5.86 ± 1.39 | 5.90 ± 1.39 | 6.37 ± 2.93 | 7.03 ± 0.47 | 0.4146 | 0.1421 | 0.0015 | 0.9887 | 0.2036 | 0.2055 | 0.1580 |
| HbA1c (%) | 5.75 ± 0.25 | 5.87 ± 0.31 | 5.53 ± 0.45 | 5.72 ± 0.34 | 6.72 ± 1.54 | 6.90 ± 1.00 | 6.85 ± 1.29 | 6.57 ± 1.09 | 0.9363 | 0.6466 | <0.0001 | 0.7247 | 0.2255 | 0.6669 | 0.4785 |
| 6‐min walking test (m) | 577.8 ± 52.2 | 620.1 ± 58.4 | 596.5 ± 64.4 | 639.1 ± 73.3 | 533.7 ± 84.3 | 571.5 ± 73.4 | 549.4 ± 58.4 | 617.3 ± 98.4 | <0.0001 | 0.2818 | 0.0842 | 0.2334 | 0.1098 | 0.1434 | 0.2246 |
## Regulation of plasma cytokine levels before and following the training interventions
All cytokines measured were detected in the totality of the samples (HIIT non‐T2D $$n = 14$$; MICT non‐T2D $$n = 13$$; HIIT T2D $$n = 5$$; MICT T2D $$n = 6$$) except for IL‐13, which was undetected in roughly half of the participants' samples irrespective of group. The process of identification of outliers resulted in the exclusion of certain data points for IL‐1b, TNF‐a, CRP, FGF21, IL‐6, IL‐8, IL‐10, IL‐15, and IL‐18, and all the outliers were found in the non‐T2D groups.
We found no effect of time (before and after training interventions consisting of HIIT or MICT) nor of the comorbidity with CAD and T2D on plasma IL‐1b, TNF‐a, and CRP concentrations (Figure 1a–c). Although no effect of time or type of exercise was detected for SPARC, there was a significant interaction between the presence of T2D in the participants and the plasma SPARC levels as a function of training modality ($$p \leq 0.0124$$), as well as between T2D, the training modalities and the effect of time ($$p \leq 0.0415$$) (Figure 2a). This is portrayed by varying plasma concentrations of SPARC at all time‐points between the groups. The variations in response to training were also different between groups, with an increase in plasma SPARC concentrations in the HIIT‐non‐T2D group and a reduction in the HIIT‐T2D group, whereas the contrary occurred in the two MICT groups. Furthermore, in response to the 12‐week supervised exercise protocols, regardless of the exercise modalities, reductions in resting plasma concentrations of FGF21 ($$p \leq 0.0030$$), IL‐6 ($$p \leq 0.0101$$), IL‐8 ($$p \leq 0.0087$$), IL‐10 ($p \leq 0.0001$), and IL‐18 ($$p \leq 0.0009$$) were observed (Figure 2b,e,h). The reductions in plasma FGF21 in the HIIT‐T2D group ($$p \leq 0.0499$$) post‐intervention reached significance in comparison with pre‐intervention concentrations (Figure 2b). An interaction between the co‐occurrence of T2D and CAD in the patients and the changes in plasma concentrations of FGF21 ($$p \leq 0.0368$$) and IL‐6 ($$p \leq 0.0385$$) in response to training was also detected independently of training modality. The reductions in these cytokines were more pronounced in the groups with T2D. No effects of either HIIT or MICT interventions were detected on resting IL‐13 and IL‐15 plasma concentrations (Figure 2f,g). At all time‐points, plasma IL‐8 concentrations were significantly elevated in the T2D groups in comparison with non‐T2D ($$p \leq 0.0331$$) (Figure 2d). No significant effect of T2D was found for any of the other cytokines.
**FIGURE 1:** *Circulating cytokine concentrations in participants with coronary artery disease (CAD) with or without type 2 diabetes (T2D) in response to a high intensity interval training (HIIT)‐ or moderate intensity continuous training (MICT)‐based rehabilitation program. Plasma cytokine concentrations at rest in men with CAD without (non‐T2D) or with the T2D comorbidity (T2D) before (pre) and after (post) a supervised 12‐week training intervention consisting of either HIIT or MICT. (a) IL‐1β, (b) TNF‐α, and (c) CRP. HIIT non‐T2D group (black circle): n = 23 for IL‐1β, n = 25 for TNF‐α, n = 22 for CRP, MICT non‐T2D group (white circle): n = 24 for IL‐1β and TNF‐α, n = 22 for CRP, HIIT T2D group (black triangle): n = 10 for all, MICT T2D group (white triangle): n = 12 for all. Data are shown individually and bars represent the average ± SEM.* **FIGURE 2:** *Circulating cytokine concentrations in participants with coronary artery disease (CAD) with or without type 2 diabetes (T2D) in response to a high intensity interval training (HIIT)‐ or moderate intensity continuous training (MICT)‐based rehabilitation program. Plasma cytokine concentrations at rest in men with CAD without (non‐T2D) or with the T2D comorbidity (T2D) before (pre) and after (post) a supervised 12‐week training intervention consisting of either HIIT or MICT. (a) SPARC, (b) FGF21, (c) IL‐6, (d) IL‐8, (e) IL‐10, (f) IL‐13, (g) IL‐15, and (h) IL‐18. HIIT non‐T2D group (black circle): n = 28 for SPARC, FGF21, IL‐6 and IL‐15, n = 26 for IL‐18, n = 24 for IL‐10, n = 22 for IL‐8, n = 14 for IL‐13. MICT non‐T2D group (white circle): n = 26 for SPARC and IL‐18, n = 25 for IL‐15, n = 24 for IL‐8 and IL‐10, n = 23 for FGF21 and IL‐6, n = 14 for IL‐13. HIIT T2D group (black triangle): n = 10 for SPARC, FGF21, IL‐6, IL‐8, IL‐10, IL‐15 and IL‐18, n = 8 for IL‐13. MICT T2D group (white triangle): n = 12 for SPARC, FGF21, IL‐6, IL‐10, IL‐15 and IL‐18, n = 11 for IL‐8, n = 5 for IL‐13. Effect of time refers to differences pre‐ and post‐intervention regardless of training type, type of training effect refers to variations between HIIT and MICT regardless of diabetes status, effect of T2D refers to differences between groups of patients with CAD only or with the comorbidity of T2D. *p < 0.05 post‐ in comparison with pre‐training intervention. Data are shown individually and bars represent the average ± SEM.*
## DISCUSSION
Chronic inflammation is a hallmark of diseases such as CAD and T2D, as elevated concentrations of specific cytokines and other signaling factors are found in the circulation in these patients and often correlate with the severity of their disease (Chen et al., 2007; Trøseid et al., 2009). Regular exercise has conversely been shown to reduce the risk of cardiovascular events through it's influence on improving metabolic parameters (e.g., HbA1c and blood lipid profile) in patients with CAD with or without T2D (Karjalainen et al., 2015). The objective of our study was to compare the anti‐inflammatory potential of traditional (i.e., MICT) and alternative (i.e., HIIT) exercise interventions on the regulation of circulating cytokine concentrations in patients with CAD with or without T2D. We specifically targeted cytokines as markers of chronic inflammation because most of these signaling peptides are increased in the circulation of patients with metabolic syndrome and/or T2D (Hotamisligil, 2006). Consistent with our original hypothesis, the plasma concentrations of certain cytokines (i.e., FGF21 and IL‐6) were further reduced following the training interventions in the groups of patients with T2D. Further, both training modalities yielded significant decreases in plasma cytokine concentrations (FGF21, IL‐6, IL‐8, IL‐10, and IL‐18), suggesting that the 12‐week exercise interventions were sufficient to reduce chronic inflammation in these patients.
## The effect of 12 weeks of exercise training on circulating cytokine concentrations
Surprisingly, we found no significant effect of any of the training interventions on plasma concentrations of IL‐1b, TNF‐a and CRP in the patients with CAD with or without T2D. The lack of changes in plasma TNF‐a concentrations following an exercise training intervention in patients with CAD is consistent with the finding of others following a 6‐month HIIT‐based intervention (Munk et al., 2011). However, a 12‐week MICT‐based intervention in CAD patients was found to result in a significant reduction in circulating CRP and IL‐1 concentrations, contrary to what we observed in our patient population (Goldhammer et al., 2005). These differences in findings could be due to several factors, including different assays to measure the cytokines and the presence of angina in their patient population. Regardless, a recent meta‐analysis by Hejazi et al. [ 2022] showed a consensus in the reductive effect of aerobic exercise training interventions on circulating TNF‐a and CRP concentrations in patients with CAD. Interestingly, their analysis of variations in circulating TNF‐a and CRP concentrations in response to aerobic training interventions showed that patients with a BMI ≥30 kg/m2 did not experience significant reductions in either cytokine, while those with BMI 25.0–29.9 kg/m2 did not show reduced circulating CRP concentrations following the interventions. On the contrary, a reduction in circulating CRP concentrations was achieved with exercise training in patients with CAD and a BMI <25 kg/m2. Consequently, it is possible that the absence of significant changes in circulating levels of these cytokines known to be influenced by exercise levels in patients with CAD could be explained by the demographics of our patient population, since 19 ($50\%$) participants at baseline had a BMI 25.0–29.9 kg/m2, 16 ($42\%$) had a BMI ≥30 kg/m2, and 3 ($8\%$) had a BMI 20.0–24.9 kg/m2. A reduction in circulating FGF21 concentrations (−253.8 pg/mL) was observed across all groups following the 12 weeks of exercise training. Kruse et al. [ 2017] found that 10 weeks of MICT in patients with T2D did not affect serum concentrations of FGF21. This discrepancy may be due to the medium analyzed (serum versus plasma; as the medium in which cytokines are measured can affect the outcome of interventions on the variation of their circulating concentrations; Lombardi et al., 2017) or the shorter duration of the aerobic exercise bouts in their study (20–35 min). Similarly, across all groups, plasma IL‐6 concentrations (−0.250 pg/mL) were reduced following the exercise intervention. This finding is consistent with those of Sabouri et al. [ 2021] in a cohort of patients with T2D that includes both sexes following a 12‐week (three sessions per week) HIIT intervention and in patients with CAD following a 12‐week MICT‐based intervention (Goldhammer et al., 2005). A reduction in plasma IL‐8 concentrations was also detected across all of our groups (−0.693 pg/mL). The reductions in both circulating IL‐6 and IL‐8 concentrations are similar to the results of Munk et al. [ 2011] who measured a reduction in both cytokines in the plasma of patients with CAD following 6 months of HIIT with a similar protocol performed either on a treadmill or cycle ergometer (3 times per week with 4 × 4 min bouts interspersed with 3 min of active rest and some strength and stretching exercises). Plasma IL‐15 concentrations were unchanged following the training interventions (−0.534 pg/mL) irrespective of T2D status ($$p \leq 0.0562$$). These findings contrast those of Perez‐Lopez et al. [ 2018] in healthy and obese individuals, for which habitual physical activity was associated with lower serum concentrations of IL‐15. On the contrary, we expected a reduction in plasma IL‐15 concentrations. It is possible that if our interventions were performed in a larger group of participants, specifically patients with T2D comorbidity, it may have allowed for the detection of significant reductions, as our p‐value was close to significance. Also, we demonstrated that 12 weeks of HIIT or MICT resulted in a reduction in plasma IL‐18 concentrations (−82.19 pg/mL) with no differences between groups regarding T2D status. A previous study revealed no effect of a 12‐month aerobic exercise intervention including HIIT‐based sessions was detected on serum IL‐18 concentrations in a cohort of patients with CAD and T2D composed mostly of men (Zaidi et al., 2019). They also observed no reduction in adipose tissue or leukocyte IL‐18 expression following the training intervention. We did not observe a significant reduction in total body fat percentage following our exercise training interventions. This suggests that the observed reduction in plasma IL‐18 concentrations in response to the 12‐week exercise interventions in the patients with CAD with or without T2D is likely not due to reduced secretion of this cytokine from adipose tissue in accordance with the findings of Zaidi et al. discussed above.
Although we observed a reduction of the anti‐inflammatory cytokine IL‐10 (−0.155 pg/mL) in the plasma of all groups following training, Munk et al. [ 2011] detected an increase in circulating IL‐10 after 6 months of HIIT and Goldhammer et al. [ 2005] showed that 12 weeks of MICT in patients with CAD also increased plasma concentrations of this cytokine. Notably, the plasma concentrations measured in these studies were approximately three to fivefold higher than those of our cohort. This could be explained by the duration of the training protocol in the case of the Munk et al. study (i.e., 6 months vs. 12 weeks), the different timing of the blood sample in relation to procedures and intervention, as well as the medical conditions of the patients (i.e., angina vs. CAD without angina), all potentially confounding the comparison between studies. Also, although the kit used to measure IL‐10 by Munk et al. was not described in their methods section, the one used by Goldhammer et al. was a high‐sensitivity quantitative enzyme sandwich immunoassay from a different manufacturer and based on different technology for detection (i.e., colorimetric vs. sulfo‐tag). Nonetheless, it would seem counterintuitive that an anti‐inflammatory cytokine or cytokines that have been shown to have a dual role in inflammatory pathways (i.e., IL‐6 and IL‐18) be diminished in the circulation of patients following a training intervention. An increase in anti‐inflammatory cytokines would be expected. The reduction we observed could be the counterbalance of lower concentrations of pro‐inflammatory cytokines following the training interventions in the patients with CAD.
## High‐intensity interval training and moderate‐to‐vigorous intensity continuous training have a similar effect on plasma cytokine concentrations
Few studies have compared the effects of HIIT‐ and MICT‐based exercise interventions on circulating cytokines in clinical populations. In a 12‐month randomized controlled trial of combined aerobic and strength training (three sessions per week) including only patients with T2D, greater reductions in plasma concentrations of IL‐6 were detected following MICT than HIIT in comparison with pre‐intervention values (Magalhaes et al., 2020). The interventions employed by Magalhaes et al. included three sessions of cycling per week matched for energy expenditure between modalities, which gradually increased in intensity up to $40\%$–$60\%$ heart rate reserve (HRR) in the MICT group, and up to $90\%$ HRR for 1‐min bouts interspersed with 1 min of active rest in the HIIT group for a duration matching the prescribed energy expenditure. In the current study, we showed that both training interventions yielded similar reductions in plasma IL‐6 over 12 weeks. The differences between our observations in IL‐6 concentrations in the plasma according to the training modalities could arise from the different types of HIIT protocols employed in both cases, as the intervals in our study were of longer duration and at a lower intensity than those of Magalhaes et al. [ 2020]. Of note, the measured values for circulating IL‐6 in the aforementioned study were approximately 10‐fold higher than ours and most of the data in the literature and their study groups were comprised of men and women in approximately even proportions, while only men were included in our study.
## The impact of T2D comorbidity in patients with CAD and their peripheral cytokine levels
We found no differences in plasma SPARC concentrations between patients with or without T2D, while Wang et al. [ 2015] observed a correlation between serum SPARC concentrations and the homeostatic model assessment of insulin resistance (HOMA‐IR) index in patients with CAD. This discrepancy between the two findings could be due to the medium in which SPARC was measured, as we quantified the cytokine in plasma rather than serum. In patients with CAD, serum FGF21 concentrations are elevated in comparison with healthy subjects and the effect is more pronounced with additional metabolic disorders, such as T2D (Shen et al., 2013). Our findings are not consistent with the literature, as we noted no effect of T2D in increasing plasma FGF21 concentrations compared to patients with CAD alone. On the other hand, there was an additive effect of T2D to the reduction in plasma FGF21 in response to training, which was more pronounced in the patients with T2D. The same interaction between T2D status and the effect of time was found for variations in circulating IL‐6 concentrations. This confirms our research hypothesis regarding these two cytokines as inflammatory markers that can be further reduced by training interventions in patients with both CAD and T2D, rather than CAD alone. In our cohort of patients with CAD, no effect of the T2D comorbidity was detected on plasma IL‐10 and IL‐18 concentrations. In men at risk or diagnosed with CAD, Trøseid et al. [ 2009] discovered that elevated fasting serum glucose or metabolic syndrome did not affect circulating IL‐10 concentrations. However, they measured higher serum IL‐6 and IL‐18 in participants with poor glucose homeostasis, which positively correlated with their risk of adverse cardiac events. We did not find increased plasma IL‐6 in the groups of patients with T2D, but the reduction in circulating concentrations of this cytokine was greater in patients with T2D in comparison with CAD alone, suggesting an effect of T2D on the response to training. Similarly, Chen et al. [ 2007] identified a correlation between circulating IL‐18 concentrations and CAD severity. In their study population, the occurrence of T2D positively correlated with elevated IL‐18 concentrations. Contrastingly, Zaidi et al. [ 2019] revealed no relationship between circulating IL‐18 concentrations and insulin resistance in patients with both CAD and T2D, but a positive correlation between adipose tissue IL‐18 expression and both fasting insulin concentrations and HOMA‐IR values. This finding suggests that elevated IL‐18 concentrations in their patient population might be related to its secretion by adipose tissue. Finally, plasma IL‐8 levels were significantly lower in patients with CAD alone in comparison with patients with T2D ($$p \leq 0.0331$$), while Trøseid et al. [ 2009] demonstrated no effect of metabolic syndrome on IL‐8 circulating concentrations. Of note, their study population consisted of older men (average age: 70 years old) of which approximately one third were current smokers, and cytokines were measured in serum. Any of these factors could explain this contradiction.
## Study limitations
Our study design and findings are novel as little information is available in the literature regarding the concentrations of these cytokines in the circulation of patients with CAD following a HIIT or MICT exercise intervention. There are several limitations that warrant mention. First, there is the potential confounding effect of medication between the groups. Indeed, one participant in the MICT non‐T2D group was taking an oral hypo‐glycemic agent at baseline and follow‐up. In healthy individuals, oral hypo‐glycemic agent such as Metformin may influence concentrations of circulating cytokines in a highly variable manner (Ustinova et al., 2019). One participant in the MICT T2D group also began taking insulin between the baseline and follow‐up visits. The initiation of insulin medication can lead to lipogenic effects in the first 6 months of treatment, leading to macrophage infiltration in sub‐cutaneous adipose tissue and an increase in certain circulating inflammatory factors (e.g., MCP‐1, TNF‐a, and IL‐1b) in patients receptive to these effects of insulin treatment (Jansen et al., 2013). Second, there were few ($$n = 5$$ and 6 respectively for HIIT and MICT) participants in the group of patients with the comorbidity of T2D. Because of the smaller sample size across all groups, but specifically for participants with T2D, appropriately powered statistical analyses were not performed. The inconclusive results as pertains to IL‐13 could, therefore, be explained by low statistical power. A more sensitive assay may have yielded more conclusive results for IL‐13 in our patient population. Third, our study did not include any control participants, defined as those not participating in any exercise‐based cardiovascular rehabilitation program. Future investigations should consider the inclusion of [1] patients with CAD yet without T2D and [2] patients with CAD and T2D who did not participate in exercise training interventions to control for the effect of time on potential variations in plasma cytokine concentrations. Without this important control group for reference, there is a possibility that the changes we observed in circulating cytokine concentrations is the result of the natural course of the disease(s). Moreover, our study only included men due to the underrepresentation of women in the subgroups created for this secondary analysis of the original randomized controlled trial (Reed et al., 2021).
Finally, we used plasma as the medium to quantify the cytokines; this component of blood samples prevents the determination of which tissue(s) act(s) as the source of decreased inflammation. Further research into the cytokines measured in this study in response to the two training modalities are warranted to identify their origin (e.g., skeletal muscle, adipose tissue, splanchnic bed, and immune cells) to more thoroughly understand the molecular mechanisms mediating the beneficial metabolic effects of exercise in patients with CAD with or without concurrent T2D.
## CONCLUSIONS
Although our conclusions are limited due to low statistical power in some groups and high inter‐individual variability, these data regarding plasma concentrations of cytokines in patients with CAD and T2D remain of high value for comparison and/or compilation in future studies/meta‐analyses. While we did not find an increased effect of the HIIT‐based exercise intervention program on reducing the cytokines in the circulation of the study participants compared to the MICT‐based intervention, both training types reduced inflammation in these patients. This effect was preserved in patients with both CAD and T2D, and even more pronounced for FGF21 and IL‐6, suggesting that T2D does not interfere with the positive effects of exercise on inflammatory markers. Our findings highlight the potential for both HIIT and MICT to help patients with CAD and co‐morbidities such as T2D to reduce their risk factor of adverse cardiac events by regulating the levels of certain inflammation‐related markers such as the cytokines discussed (i.e., FGF21, IL‐6, IL‐8, IL‐10, and IL‐18).
## AUTHOR CONTRIBUTIONS
Jennifer L. Reed and Céline Aguer conceived and designed research; Jennifer L. Reed and Céline Aguer obtained funding; Léa Garneau and Tasuku Terada performed experiments; Léa Garneau, Tasuku Terada, and Matheus Mistura analyzed data; Léa Garneau, Tasuku Terada, Erin E. Mulvihill, Jennifer L. Reed, and Céline Aguer interpreted results of experiments; Léa Garneau prepared figures; Léa Garneau drafted the manuscript; Léa Garneau, Tasuku Terada, Matheus Mistura, Erin E. Mulvihill, Jennifer L. Reed, and Céline Aguer edited and revised manuscript; Léa Garneau, Tasuku Terada, Matheus Mistura, Erin E. Mulvihill, Jennifer L. Reed, and Céline Aguer approved the final version of the manuscript.
## FUNDING INFORMATION
This clinical trial was funded by the Innovations Fund of the Alternate Funding Plan for the Academic Health Sciences Centres of the Ministry of Ontario (UOH‐16‐003) and the Heart and Stroke Foundation of Canada to J.L.R. Our sub‐study of this trial was supported by the Société Francophone du Diabète to C.A. L.G. was funded by a Ph.D. scholarship from Fonds de Recherche du Québec – Santé.
## CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## ETHICS STATEMENT
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ottawa Health Science Network Research Ethics Board (protocol #: 20160127‐01H).
## Unknown
Non‐T2D groups n (%)T2D groups n (%)HIIT ($$n = 14$$)MICT ($$n = 13$$)HIIT ($$n = 5$$)MICT ($$n = 6$$)Pre‐Post‐Pre‐Post‐Pre‐Post‐Pre‐Post‐ACE inhibitor7 [50]7 [50]5 [38]5 [38]2 [40]1 [20]4 [67]4 [67]Acetylsalicylic acid13 [93]13 [93]10 [77]10 [77]3 [60]3 [60]5 [83]5 [83]Calcium‐antagonist2 [14]2 [14]1 [8]1 [8]2 [40]2 [40]0 [0]0 [0]Statins12 [86]12 [86]11 [85]11 [85]5 [100]5 [100]6 [100]6 [100]Diuretics1 [7]1 [7]1 [8]1 [8]0 [0]1 [20]0 [0]0 [0]Clopidogrel0 [0]1 [7]1 [8]1 [8]0 [0]0 [0]2 [33]2 [33]Nicotine replacement0 [0]0 [0]1 [8]0 [0]0 [0]0 [0]0 [0]0 [0]Anti‐platelet11 [79]10 [71]8 [62]8 [62]3 [60]3 [60]3 [50]3 [50]β‐blocker12 [86]11 [79]8 [62]9 [69]5 [100]5 [100]5 [83]5 [83]Nitrate6 [43]6 [43]8 [62]8 [62]2 [40]2 [40]3 [50]3 [50]Angiotensin receptor blocker2 [14]2 [14]1 [8]1 [8]1 [20]1 [20]1 [17]1 [17]Antidepressant1 [7]1 [7]2 [15]2 [15]0 [0]0 [0]1 [17]1 [17]Insulin0 [0]0 [0]0 [0]0 [0]2 [40]3 [60]1 [17]2 [33]Oral hypoglycemiant0 [0]0 [0]1 [8]1 [8]3 [60]3 [60]2 [33]2 [33]Fibrate0 [0]0 [0]0 [0]1 [8]0 [0]0 [0]0 [0]0 [0]Coumadin0 [0]0 [0]0 [0]0 [0]0 [0]1 [20]0 [0]0 [0] Note: High intensity interval training (HIIT), moderate intensity continuous training (MICT), non‐type 2 diabetes (T2D) groups refers to patients with CAD without T2D and T2D groups refer to patients with CAD and this comorbidity, at baseline (pre‐) and follow‐up (post‐) of a 12‐week training intervention.
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|
---
title: Reduction of NADPH oxidase 4 in adipocytes contributes to the anti-obesity
effect of dihydroartemisinin
authors:
- Hu Hua
- Mengqiu Wu
- Tong Wu
- Yong Ji
- Lv Jin
- Yang Du
- Yue Zhang
- Songming Huang
- Aihua Zhang
- Guixia Ding
- Qianqi Liu
- Zhanjun Jia
journal: Heliyon
year: 2023
pmcid: PMC10006843
doi: 10.1016/j.heliyon.2023.e14028
license: CC BY 4.0
---
# Reduction of NADPH oxidase 4 in adipocytes contributes to the anti-obesity effect of dihydroartemisinin
## Abstract
Artemisinin derivatives have been found to have anti-obesity effects recently, but the mechanism is still controversial. Herein, long-term DHA treatment in obese mice significantly reduced the body weight and improved glucose metabolism. However, short-term DHA treatment did not affect glucose metabolism in obese mice, suggesting that the improved glucose metabolism in mice with DHA treatment could be secondary to body weight reduction. Consistent with previous reports, we observed that DHA inhibited the differentiation of adipocytes. Mechanistically, DHA significantly reduced the expression of NADPH oxidase 4 (NOX4) in white adipose tissue (WAT) of mice and differentiated adipocytes, and using NOX4 siRNA or the NOX4 inhibitor GKT137831 significantly attenuated adipocyte differentiation. Over-expression of NOX4 partially reversed the inhibition effect of DHA on adipogenic differentiation of preadipocytes. In addition, targeted proteomics analysis showed that DHA improved the abnormality of metabolic pathways. In conclusion, DHA significantly reduced fat mass and improved glucose metabolism in obese mice, possibly by inhibiting NOX4 expression to suppress adipocyte differentiation and lipid accumulation in adipocytes.
## Highlights
•DHA improved glucose metabolism in obese mice possibly by ameliorating obesity.•DHA attenuated lipid accumulation in white adipose tissues and differentiated adipocytes.•DHA inhibited adipocyte differentiation possibly by inhibiting NOX4 expression.•DHA improved the abnormality of metabolic pathways.
## Introduction
Obesity is a rising global epidemic with a high risk for metabolic syndromes that include cardiovascular disease, type 2 diabetes mellitus, dyslipidemia, and even malignant tumors [[1], [2], [3]]. The current therapeutic approaches to obesity management principally include lifestyle modifications such as reducing food intake and increasing exercise, inhibiting intestinal lipid absorption, and undergoing weight-loss surgery [[4], [5], [6], [7]]. However, obese patients often face a rapid rebound in body weight after maintaining short-term weight loss using the above methods [8,9]. Therefore, using compounds derived from traditional Chinese medicine constitutes another option that can be applied to treat obesity and metabolic diseases due to their long-term effectiveness and safety [10].
During the development of obesity, excess calories accumulate as fats in adipocytes and lead to the growth and expansion of WAT [11], and adipocytes are generated through adipogenesis from specific precursor cells. Although the total number of adipocytes in lean and obese adults does not differ, approximately $10\%$ of fat cells are renewed annually in adults [12]. Therefore, adipocyte differentiation is a vital process for both adipocytes and the development of obesity [13]. The process of adipogenesis requires an orchestrated multistep process controlled by the activation of key transcription factors that include peroxisome proliferator-activated receptor γ (Pparγ) and CCAAT/enhancer binding protein α and β (C/Ebpα, C/Ebpβ) [14], and the expression of fatty acid synthase (Fasn) is activated in the late phase of differentiation to promote adipogenesis [15].
Reactive oxygen species (ROS) are pervasive signaling molecules in biologic systems, and ROS generation and scavenging are tightly regulated to maintain homeostasis [16]. ROS in adipose tissue increases during the development of obesity, and there are accumulating evidences that implicate a tight regulation of adipogenesis by ROS [[17], [18], [19]]. Among the various enzymes responsible for ROS generation are mitochondrial electron transport chain complexes I and III, nitric oxide synthases (NOSs), CYP450 reductase, xanthine oxidase, and NADPH oxidases (NOXs); with NOXs comprising the only enzymes whose primary function is to generate superoxide/ROS [20]. Of the NOX family members, NOX4 is primarily expressed in adipocytes, is the major source of ROS production during adipocyte differentiation [21]. Moreover, the expression level of NOX4 represents a switch between proliferation and differentiation in preadipocytes [22].
Artemisinin is a nature product initially extracted from plant *Artemisia annua* and applied as an antimalarial drug. Interestingly, recent studies have shown that artemisinin and its derivatives exert potential anti-obesity effects [23,24] with the underlying mechanism(s) of action needing further exploration. Dihydroartemisinin (DHA) is an active metabolite of artemisinin widely used to treat malaria, and our in vivo experiments suggested that long-term DHA treatment improved high-fat diet (HFD)-induced obesity and that the improved glucose metabolism in the obese mice were secondary to body weight reduction. Further research showed that DHA inhibited the differentiation of adipocytes both in vivo and in vitro. In terms of mechanism, we demonstrated that DHA significantly reduced the expression of NOX4 in WAT and differentiated adipocytes, and the application of NOX4 siRNA or the NOX4 inhibitor GKT137831 significantly inhibited adipocyte differentiation. We additionally implemented ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS)-based DHA targetom analysis using DHA-treated preadipocytes during differentiation. Our results showed that a total of 85 proteins involved in metabolic pathways were conformationally changed after DHA treatment, providing additional evidence that DHA affected adipocyte metabolism.
## Animal experiments
Six-week-old male C57BL/6J mice were purchased from Nanjing Medical University, maintained under a 12-h light/12-h dark cycle at 22 ± 2 °C and a relative humidity of 55 ± $10\%$, and provided food and water ad libitum. For the diet-induced obese (DIO) mouse model, mice were fed with a high-fat diet (HFD) (D12492, Research Diets, USA), and control mice were fed with a normal diet (ND) (Beijing Keao Xieli Feed Co., Ltd., China). For the long-term DHA-treatment experiment, mice were randomly distributed into the following four groups: a group fed with the ND, a group fed with the ND supplemented with DHA, a group fed with the HFD, and a group fed with the HFD supplemented with DHA. For the short-term DHA treatment experiment, mice were fed with HFD or HFD supplemented with the same dose of DHA as for the long-term treatment. For these experiments, we mixed DHA (D7439, Sigma) with an ND or HFD at a dose of 25 mg/kg/d. The body weights (BWs) of the mice were documented, and food intake of each mouse was recorded once per day for three consecutive days. At the end of the experimental period, the animals were sacrificed and adipose tissue and blood were collected for subsequent analyses. All animal experiments were performed according to the protocols approved by the Institutional Animal Care and Use Committee of Nanjing Medical University (IACUC14030112-1).
## Glucose tolerance test (GTT) and insulin tolerance test (ITT) analyses
GTT and ITT experiments were conducted to evaluate the glucose metabolic rate of the correspondingly treated mice. For the GTT, mice were fasted overnight (from 5 p.m. to 9 a.m.), and fasting blood glucose was assessed (0 min). Then, 2 g/kg glucose was injected intraperitoneally (i.p.), and tail blood glucose was measured using a handheld glucometer (Ascensia Breeze, Bayer Company, Germany) at 15, 30, 60, 90, 120, and 150 min after glucose injection. For the ITT, mice were fasted for 4 h with free access to drinking water and the basal blood glucose levels were recorded (0 min), after which the mice received an i.p. injection of 0.75 units/kg insulin (NovoRapid, Novo Nordisk); and the glucose concentrations were determined at 15, 30, 60, and 90 min after insulin injection.
## Quantitative real-time PCR (qRT-PCR)
The total RNA of tissues or cultured cells was extracted with TRIzol Reagent (Takara, Takara Biotechnology, Dalian, China). A total of 1 μg of RNA from each sample was subsequently reverse-transcribed to cDNA in a 20-μL reaction system using a PrimeScript RT reagent kit (Takara, Tokyo, Japan) in accordance with the manufacturer's protocol. The cDNA was then diluted three times and 2 μL of cDNA was used as a template for PCR adopting a two-step method. qRT-PCR amplification was conducted with a SYBR Green Master Mix (Vazyme, Nanjing, China) on an ABI 7500 Real-time PCR Detection System (Foster City, CA, USA) or HONGSHI Real-time PCR Detection System (Shanghai, China). The cycling conditions were 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The mRNA levels were normalized to GAPDH and calculated using the comparative cycle threshold (ΔΔCt) method. The sequences of the primers are shown in Table 1.Table 1Sequences of the primers for qRT-PCR.Table 1GenePrimer Sequencehuman C/Ebpα_F5'-TGGACAAGAACAGCAACGAG-3'human C/Ebpα_R5'-CCATGGCCTTGACCAAGGAG-3'human Glut4_F5'-GGCCTCCGCAGGTTCTG-3'human Glut4_R5'-TTCGGAGCCTATCTGTTGGAA-3'human Pparγ2_F5'-AAATATCAGTGTGAATTACAGCAAACC-3'human Pparγ2_R5'-GGAATCGCTTTCTGGGTCAA-3'human Fabp4_F5'-GGTGGTGGAATGCGTCATG-3'human Fabp4_R5'-CAACGTCCCTTGGCTTATGC-3'human Nox4_F5'-AGCAGAACATTCCATATTACCTGTG-3'human Nox4_R5'-GATCCTCATCTCGGTATCTTGCT-3'human Gapdh_F5'-CGCTCTCTGCTCCTCCTGTT-3'human Gapdh_R5'-CATGGGTGGAATCATATTGG-3'mouse Cd68_F5'-CATCCCCACCTGTCTCTCTC-3'mouse Cd68_R5'-TTGCATTTCCACAGCAGAAG-3'mouse Glucokinase_F5'-GCTGGTACGACTTGTGCTG-3'mouse Glucokinase_R5'-TGGACACGCTTTCACAGG-3'mouse Glut2_F5'-TCAGAAGACAAGATCACCGGA-3'mouse Glut2_R5'-GCTGGTGTGACTGTAAGTGGG-3'mouse Cd36_F5'-ATGGGCTGTGATCGGAACTG-3'mouse Cd36_R5'-GTCTTCCCAATAAGCATGTCTCC-3'mouse Srebp-1c_F5'-ATCGCAAACAAGCTGACCTG-3'mouse Srebp-1c_R5'-AGATCCAGGTTTGAGGTGGG-3'mouse Acc1_F5'-ATGGGCGGAATGGTCTCTTTC-3'mouse Acc1_R5'-TGGGGACCTTGTCTTCATCAT-3'mouse Cpt-1α_F5'-TTGCCCTACAGCTCTGGCATTTCC-3'mouse Cpt-1α_R5'-GCACCCAGATGATTGGGATACTGT-3'mouse Glut4_F5'-TTGGAGAGAGAGCGTCCAAT-3'mouse Glut4_R5'-CTCAAAGAAGGCCACAAAGC-3'mouse C/Ebpα_F5'-AGGTGCTGGAGTTGACCAGT-3'mouse C/Ebpα_R5'-CAGCCTAGAGATCCAGCGAC-3'mouse Pref-1_F5'-TTCGGCCACAGCACCTATG-3'mouse Pref-1_R5'-GGGGCAGTTACACACTTGTCA-3'mouse Hsl_F5'-AGACACCAGCCAACGGATAC-3'mouse Hsl_R5'-ATCACCCTCGAAGAAGAGCA-3'mouse Pparα_F5'-TCAGGGTACCACTACGGAGTTCA-3'mouse Pparα_R5'-CCGAATAGTTCGCCGAAAGA-3'mouse Nox4_F5'-TGTCTGCATGGTGGTGGTATT-3'mouse Nox4_R5'-ACCTGAAACATGCAACAGCAG-3'mouse Gapdh_F5'-GTCTTCACTACCATGGAGAAGG-3'mouse Gapdh_R5'-TCATGGATGACCTTGGCCAG-3'
## Western blotting (WB)
We lysed the cultured cells and homogenized tissues in RIPA buffer (Cat# P0045, Beyotime, China) containing a protease-inhibitor cocktail (Cat# 04693132001, Roche, Canada). After centrifugation at 14,000 rpm for 15 min at 4 °C, the protein concentrations were determined using a BCA protein assay kit (Cat# P0012, Beyotime, China). The cell and tissue lysates were then electrophoretically separated on $10\%$ polyacrylamide gels and transferred onto PVDF membranes. The membranes were subsequently blocked with $5\%$ nonfat dry milk dissolved in Tris-buffered saline/$0.1\%$ Tween 20 for 1 h at room temperature. After blocking, membranes were probed with the following diluted primary antibodies: anti-FASN (1:1000; Cell Signaling Technology, USA), anti-NOX4 (1:1000; Abcam, USA), anti-FABP4 (1:1000; Proteintech, China), anti-PPARγ (1:1000; Bioworld, USA), anti-β-actin (1:5000; Bioworld, USA) and anti-GAPDH (1:2000; Abcam, USA). After the membranes were incubated with the primary antibodies at 4 °C overnight, the membranes were incubated with goat anti-rabbit (1:1000, Beyotime, China) HRP-conjugated secondary antibodies and signals were detected using Image Lab software (Bio-Rad, USA).
## Isolation and differentiation of primary white fat precursor cells
The primary white fat precursor cells were isolated from inguinal white adipose tissue (iWAT) of 4-week-old C57BL/6J male mice. The isolation method is consistent with that of mouse primary brown fat precursor cells our research group adopted [5]. For the differentiation of white fat precursor cells, the confluent cells were firstly induced with induction medium Ⅰ (DMEM/F12 containing $10\%$ FBS, 0.5 mM isobutylmethylxanthine (IBMX) (Sigma), 1 μM dexamethasone (DEX) (Sigma), 860 nM insulin (Sigma), and 1 μM rosiglitazone (Sigma)) for 4 days (replaced every 2 days). After 4 days, induction medium Ⅰ was replaced with induction medium Ⅱ (DMEM/F12 supplemented with $10\%$ FBS and 860 nM insulin (Sigma)) for 2 days and the precursor cells were differentiated into mature white fat cells. GKT137831 (S7171, Selleck) was added to the induction medium throughout the induction period. To further explore the effect of NOX4 on primary white fat precursor cell differentiation, small interfering RNA (siRNA) targeting mouse NOX4 gene sequence (CTCTTCATAGTTTGAGTAA) or mouse NOX4 overexpression plasmid was transfected into cells using Lipofectamine 2000 (Invitrogen, San Diego, CA) according to the manufacturer's instructions.
## Human visceral preadipocytes (HPA-v) culture and differentiation
HPA-v (ScienCell Research Laboratories, USA) were cultured and induced to differentiate for the study of adipogenesis in vitro [25]. In detail, we maintained HPA-v cells in preadipocyte medium (PAM; ScienCell Research Laboratories) supplemented with $5\%$ fetal bovine serum (FBS), $1\%$ preadipocyte growth supplement (PAGS), and $1\%$ penicillin/streptomycin solution at 37 °C in $5\%$ CO2 until the cells achieved confluency. A differentiation medium (serum-free PAM supplemented with 5 μg/mL insulin, 1 μM DEX, 0.5 mM IBMX, and 1 μM rosiglitazone) was then employed to induce the differentiation of the cells for the first four days. The medium was then replaced with serum-free DMEM containing 5 μg/mL insulin and changed every three days until lipid droplets accumulation was observed. DHA (D7439, Sigma) or GKT137831 (S7171, Selleck) was added to the induction medium throughout the differentiation process. To explore the effect of the NOX4 gene on HPA-v differentiation, we mixed three siRNAs that targeted human NOX4 gene (GGACCCAATTCACTATCCA, CCAGGAGATTGTTGGATAA, and GCCGAACACTCTTGGCTTA) and transfected the siRNAs into HPA-v cells using Lipofectamine 2000 (Invitrogen, San Diego, CA) according to the manufacturer's instructions.
## MTT and CCK8 assay
We employed the MTT (3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide) and CCK8 (Cell Counting Kit 8) assay to evaluate the toxicity of DHA on HPA-v. For MTT assay, HPA-v was incubated with MTT (25 μg/mL) at 37 °C in $5\%$ CO2 for 6 h an MTT detergent solution was added for 12 h at 37 °C. The optical density of the solution was then measured at 570 nm to evaluate cellular viability. Time course evaluation of DHA on HPA-v cell viability was examined 1, 4, 7, 10 days separately after adipocyte differentiation using a CCK8 (ApexBio, Houston, TX, USA). After incubation for 45 min at 37 °C, the absorbance was detected at 450 nm using a microplate reader Multiscan FC (Thermo Scientific, Waltham, MA, USA).
## Oil red O staining
Oil red O staining was performed after the HPA-v cells differentiated into mature adipocytes. The cells were washed with phosphate-buffered saline (PBS) and stained with filtered oil red O (Sigma-Aldrich) solution ($0.5\%$ oil red O-isopropyl alcohol: H2O, 3:2, v/v) for 15 min at 37 °C. Following three washes with distilled water, the images of the cells were captured with an inverted microscope (Zeiss, Germany). To semi-quantify oil red O in positively stained cells, the stained cells were first washed with $60\%$ isopropanol to remove the nonspecific stain, and the oil red O in lipid droplets was extracted with $100\%$ isopropanol [26]. The absorbance value was determined using a microplate reader (Tecan GmbH, Grodig, Austria) at a wavelength of 510 nm.
## Measurement of intracellular triacylglycerol (TG) content
The intracellular TG concentration was conducted using a TG assay kit (Applygen, Beijing, China) according to the manufacturer's instructions. Briefly, the cells were lysed with the lysis buffer provided in the TG assay kit. An appropriate amount of lysate was collected and heated at 70 °C for 10 min and then centrifuged at 2000 rpm for 5 min at room temperature. We then aspirated 10 μL of supernatant for the determination of TG concentrations using the prepared working solution provided in the kit. The remaining lysates were directly centrifuged at 14,000 rpm at 4 °C, and the supernatant was collected to determine the protein concentration using a BCA Protein Assay kit (Cat# P0012, Beyotime, China). The intracellular TG level was ultimately normalized with the protein concentration.
## Determination of serum TG, total cholesterol (TC) and non-esterified fatty acids (NEFAs) concentrations
The serum TG and TC concentrations were conducted using TG and TC assay kits (Applygen, Beijing, China) according to the manufacturer's instructions. The detection steps of TG and TC were basically the same as the detection of intracellular TG. The serum NEFAs were conducted using assay kit (Cat# A042-2-1, Jiancheng, China) according to the manufacturer's instructions. Briefly, 4 μL sample, standard or double distilled water were incubated with 200 μL reagent 1 provided by the kit for 5 min at 37 °C, and the absorbance values were read at 546 nm and recorded. Then 50 μL reagent 2 was added to each well and incubated for another 5 min at 37 °C, the absorbance values were read at 546 nm and recorded. Finally, the concentration of NEFAs were calculated according to the formula provided in the manual.
## Determination of serum insulin levels
After fasted for 6 h, whole blood was collected from the posterior vena cava of mice soon after euthanasia. Serum was then obtained after centrifuging the whole blood at 2000 rpm for 10 min at room temperature. Serum insulin levels were measured using an enzyme-linked immunosorbent assay (ELISA) kit (E-EL-M1382c, Elabscience, China) according to the manufacturer's instructions.
## Histologic examination
Fresh adipose tissues were first soaked in $90\%$ ethanol for 24 h and then transferred to $4\%$ polyformaldehyde for fixation for another 24 h. The fixed tissues were dehydrated, embedded in paraffin, sectioned at 4-μm thickness, and then stained with hematoxylin-eosin (H&E). Tissue sections were examined, and photographs were taken under an Olympus BX51 (Olympus, Japan). The adipocyte diameter from the adipose tissue sections were analyzed by ImageJ software (v1.52a, National Institutes of Health, Bethesda, MD, USA).
## UPLC−MS/MS-based targeted proteomics analysis of DHA in cultured HPA-v cells
HPA-v cells were differentiated and treated with DHA (20 μM) as described in section 2.6, and then the cells were washed with cold PBS three times, and scraped and centrifuged at 1000 rpm for 5 min. The cell pellets were resuspended in mammalian protein extraction reagent (Thermo Fisher Scientific) containing protease inhibitors and phosphatase inhibitors and lysed on ice for 30 min. After centrifugation at 18,000×g for 10 min, the cell lysates (150 μL, 3 μg/μL) were labeled with Paraformaldehyde-d 2 (CD2O), precipitated, digested and desalted by the same protocol as described previously [27]. A nanoACQUITY UPLC system coupled to a SYNAPT G2-Si mass spectrometer (Waters, Milford, MA, USA) was used for label-free quantification. The detailed UPLC−MS/MS conditions were the same as those described previously [27]. We searched the acquired data against the UniProt/SwissProt database (Homo sapiens, version 2018) using PEAKS Studio 8.5 (Bioinformatics Solutions Inc., Waterloo, Ontario, Canada). The search parameters were a precursor mass tolerance of 20 ppm and a fragment mass tolerance of 0.1 Da, and protein identifications with a false discovery rate (FDR) of less than $1\%$ with at least one unique peptide were considered acceptable. Kyoto Encyclopedia of Genes and Genomes (KEGG)-pathway analysis was conducted by importing the proteins that displayed significant differential peptide abundance (i.e., a ratio higher than 1.50 or lower than 0.67, with a p value < 0.05) into Cytoscape and Database for Annotation, Visualization and Integrated Discovery (DAVID, v6.8).
## Statistical analysis
All values are presented as the mean ± SEM and we executed statistical tests using GraphPad Prism 9. An unpaired Student's t-test was used for one-variable comparisons, and one- or two-way ANOVA was performed for two-variable comparisons. $P \leq 0.05$ was considered statistically significant.
## DHA improves obesity induced by a high-fat diet in mice
To confirm the potential therapeutic effect of DHA on obesity, we first used a high-fat diet to induce obesity for 10 weeks, and then employed DHA to treat the obese mice orally (25 mg/kg/day) for another 10 weeks. Our results showed that the DHA-treated obese mice became significantly thinner, whereas the DHA-treated ND-fed mice were not significantly different (Fig. 1A). DHA maintained or even reduced the BW of obese mice, with the weight loss of obese mice reaching $21.8\%$ (Fig. 1B). Consistent with the BW change, DHA-treated HFD mice possessed a much lower volume and weight of epididymal white adipose tissue (eWAT), iWAT, and perirenal white adipose tissue (pWAT) ($P \leq 0.05$) (Fig. 1D, H-J). H&E staining of eWAT also revealed that the adipocyte size was much smaller in DHA-treated obese mice than in control mice (Fig. 1E and F). Meanwhile, the expression of macrophage marker gene cluster of differentiation 68 (Cd68) was significantly decreased in eWAT of HFD-fed mice treated with DHA (Fig. 1G). Along with the decreased weight of adipose tissues, the weight of liver also decreased significantly while pancreas weight did not change significantly under the intervention of DHA (Fig. 1K and L). DHA significantly decreased serum NEFAs level, but had no significant effect on serum TG and TC levels (Fig. 1M, N and O). We observed no significant difference in the food intake of obese mice treated with or without DHA (Fig. 1C), indicating that the weight loss is not due to appetite decreases. These results indicated that DHA significantly improved obesity in DIO mice. Fig. 1DHA improves a high-fat diet induced obesity in mice. ( A) Representative photographs of ND- and HFD-fed mice treated with or without DHA. ( B) BW of the experimental animals ($$n = 6$$–8 per group). ( C) Food intake of the experimental animals ($$n = 6$$–8 per group). ( D) Macroscopic view of representative sections of eWAT, iWAT, and pWAT from the experimental animals. ( E–F) Representative H&E-stained images (200 ×) and adipocyte diameter of eWAT. ( G) mRNA level of *Cd68* gene in eWAT ($$n = 6$$–8 per group). ( H–L) Weight of (H) eWAT, (I) iWAT, (J) pWAT, (K) liver and (L) pancreas of the experimental animals ($$n = 6$$–8 per group). ( M–O) Serum TG, TC and NEFAs levels of the experimental animals ($$n = 6$$–8 per group). The values are mean ± SEM. * $P \leq 0.05$ and **$P \leq 0.01$; n.s., not significant ($p \leq 0.05$).Fig. 1
## The improvement of DHA on glucose metabolism depends on weight loss
We performed GTT and ITT to assess the effects of DHA on glucose homeostasis and insulin resistance. GTT results showed that long-term (10-week) treatment of DHA-treated obese mice with DHA induced a faster diminution in blood glucose concentration upon glucose injection compared to control animals (Fig. 2A). The ITT results also showed better insulin sensitivity in the long-term DHA-treated obese mice than in the control mice (Fig. 2B). It has been previously reported that artemisinin and its derivatives can induce the conversion of α cells to functional β-like cells, thereby increasing the secretion of insulin [28]. Therefore, in order to explore whether the improvement in glucose metabolism in obese mice was due to a change in islet cell function or to a decline in body weight, we treated the DIO mice showing an average BW of about 42 g for relatively short terms of three and six days. During treatment, the BW did not change significantly ($P \leq 0.05$) (Fig. 2C), and the GTT results showed that the efficiency of glucose metabolism was not significantly improved in obese mice treated with DHA for either three or six days ($P \leq 0.05$) (Fig. 2D and E). In addition, we did not detect a significant change of serum insulin levels in obese mice treated with DHA for six days ($P \leq 0.05$) (Fig. 2F). These results suggested that the effects of DHA on glucose metabolism were secondary to the reduction in mouse body weight. Fig. 2Improvement in glucose metabolism in mice depends upon weight loss. ( A) GTT results of the ND- and HFD-fed mice treated with or without DHA for 10 weeks ($$n = 6$$–8 per group). ( B) ITT results of the ND- and HFD-fed mice treated with or without DHA for 10 weeks ($$n = 6$$–8 per group). ( C) BW of DIO mice treated with or without DHA for three and six days ($$n = 8$$ per group). ( D–E) GTT results of DIO mice treated with or without DHA for (D) three days and (E) six days ($$n = 8$$ per group). ( F) Serum insulin levels of DIO mice treated with or without DHA for six days ($$n = 8$$ per group). The values are mean ± SEM. * $P \leq 0.05$; n.s., not significant ($p \leq 0.05$).Fig. 2
## DHA inhibits adipocyte differentiation and the expression of NOX4 in WAT of mice
The weight reduction of WAT and liver plays a major role in the weight reduction of mice fed with HFD, so we next analyzed transcription level of the metabolism related genes in the WAT and liver. We did not observe significant changes in mRNA levels of liver glucose metabolism-related genes such as Glucokinase and Glut2 and lipid metabolism-related genes such as cluster of differentiation 36 (Cd36), Sterol Regulatory element binding protein-1c (Srebp-1c), acetyl-CoA carboxylase1 (Acc1) and carnitine palmitoyltransferase 1a (Cpt-1α) (Fig. 3A). Then we detected the expression of genes related to adipocyte differentiation such as glucose transporter 4 (Glut4), C/Ebpα, Pparγ and preadipocyte factor-1 (Pref-1) in the eWAT of mice fed with HFD, and the results showed that DHA significantly down-regulated the expression of differentiation promoting genes Glut4 and Pparγ at mRNA or protein level, and C/Ebpα gene also showed a downward trend upon DHA treatment (Fig. 3B, C and D). Pref-1, an early negative regulator of adipogenic differentiation [29], was significantly upregulated after DHA treatment (Fig. 3B). The lipolysis related-genes such as hormone-sensitive lipase (Hsl), Cpt-1α and peroxisome proliferator-activated receptor α (Pparα) in the eWAT of HFD-fed mice were not significantly changed under the intervention of DHA (Fig. 3B). In addition, we found that the protein level of NOX4 in adipocytes of eWAT was significantly downregulated after DHA treatment ($P \leq 0.05$) (Fig. 3C and D). These results indicating that DHA inhibited the differentiation of white adipocytes in vivo. Fig. 3DHA inhibits the expression of adipose differentiation-related genes in WAT of mice fed with HFD. ( A) mRNA levels of glucose metabolism-related genes (Glucokinase and Glut2) and lipid metabolism-related genes (Cd36, Srebp-1c, Acc1 and Cpt-1α) in the liver of HFD-fed mice treated with or without DHA ($$n = 6$$–8 per group). ( B) mRNA levels of lipogenesis-related genes (Glut4, C/Ebpα and Pref-1) and lipolysis-related genes (Hsl, Cpt-1α and Pparα) in the eWAT of HFD-fed mice treated with or without DHA ($$n = 6$$–8 per group). ( C) Representative western blots of PPARγ and NOX4 in the eWAT of ND- and HFD-fed mice treated with or without DHA, and (D) corresponding quantified signal intensities ($$n = 3$$ per group). The values are mean ± SEM. * $P \leq 0.05$ and **$P \leq 0.01$; n.s., not significant ($p \leq 0.05$).Fig. 3
## DHA inhibits the differentiation of white adipose precursor cells in vitro
Next, we assessed whether DHA inhibited fat formation by exploiting a well-characterized model of inducing adipose precursor HPA-v cells into typical white adipocytes. As MTT assay showed that DHA at concentrations ranging from 10 to 40 μM posed no cellular toxicity in HPA-v cells (Fig. 4A), DHA less than 40 μM was employed in vitro. After the cultured HPA-v cells attained confluency, they were induced to differentiate with or without DHA treatment. During the whole differentiation process of HPA-v cells, 40 μM DHA treatment had no significant effect on cell viability (Fig. 4B). After the induction was completed, oil red O-stained images and corresponding semi-quantification results depicted a DHA-dependent attenuation of lipid-droplet accumulation in adipocytes ($P \leq 0.05$) (Fig. 4C and D). Analysis of the TG content in the different groups of adipocytes also confirmed that DHA significantly curtailed lipid accumulation during adipocyte differentiation (Fig. 4E).Fig. 4DHA suppresses the adipocyte differentiation in vitro. ( A) Cell viability of DHA-treated HPA-v cells determined by MTT. ( B) Cell viability of DHA-treated HPA-v cells during the whole differentiation process by CCK8. ( C) Representative oil red O-stained images (100 ×) of HPA-v cells treated with DHA during differentiation induction ($$n = 3$$ per group). ( D–E) (D) *Semiquantitative analysis* of oil red O levels and (E) TG levels in HPA-v cells treated with DHA during differentiation induction ($$n = 4$$ per group). ( F–H) mRNA levels of adipocyte differentiation-related genes (Pparγ2, Glut4 and C/Ebpα) in HPA-v cells treated with DHA during differentiation induction ($$n = 4$$ per group). ( I) Nox4 mRNA levels in HPA-v cells treated with DHA during differentiation induction ($$n = 4$$ per group). ( J) FASN and FABP4 protein levels in HPA-v cells treated with DHA during differentiation induction. ( K) Western blots of NOX4 in HPA-v cells treated with DHA during differentiation induction. The values are mean ± SEM. * $P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$; n.s., not significant ($p \leq 0.05$). ( For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)Fig. 4 We next examined the expression of typical adipocyte differentiation-related genes, and demonstrated that—consistent with cellular phenotype—the mRNA expression levels of the adipogenesis markers Pparγ2, Glut4 and C/Ebpα were significantly inhibited by DHA in a dose-dependent manner ($P \leq 0.05$) (Fig. 4F–H). Western blotting results showed that the expression of FASN, a key enzyme in adipogenesis, and FABP4, an important marker of adipogenesis, were both significantly inhibited by DHA (Fig. 4J). To further examine whether the change in NOX4 expression in adipose tissue was a direct effect of DHA, we assessed the effect of DHA on the expression of NOX4 in cultured HPA-v cells. Consistently, the qRT-PCR and WB data showed that DHA significantly downregulated the expression of NOX4 at both the mRNA and protein levels in HPA-v cells compared with the induction group without DHA treatment (Fig. 4I, K). Collectively, these data suggested that DHA inhibited the differentiation of adipocytes.
## Pharmacological inhibition of NOX4 inhibits the differentiation of adipose precursor cells in vitro
In order to investigate whether NOX4 plays a role in DHA's adipocyte differentiation inhibition effect, NOX4 inhibitor GKT137831 was used to observe its effect on adipocyte differentiation. The oil red O staining images and quantitative TG results showed that GKT137831 significantly inhibited lipid accumulation in both differentiation-induced mouse primary white fat precursor cells (Fig. 5A and B) and HPA-v cells (Fig. 5C and D). Similarly, the transcriptional level of the adipocyte differentiation-related genes Pparγ2, C/Ebpα, and Fabp4 in HPA-v cells were downregulated in a drug concentration-dependent manner (Fig. 5E–G).Fig. 5Pharmacological inhibition of NOX4 inhibits the differentiation of adipose precursor cells. ( A and B) (A) Representative oil red O-stained images (200 ×) and (B) TG levels of differentiated mouse primary white fat precursor cells treated with GKT137831 ($$n = 3$$ per group). ( C and D) (C) Representative oil red O-stained images (100 ×) and (D) TG levels of differentiated HPA-v cells treated with GKT137831 ($$n = 3$$ per group). ( E–G) mRNA levels of adipocyte differentiation-related genes (Pparγ2, C/Ebpα, and Fabp4) in HPA-v cells treated with GKT137831 ($$n = 3$$ per group). The values are mean ± SEM. * $P \leq 0.05$, **$P \leq 0.01$ and ***$P \leq 0.001.$ ( For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)Fig. 5
## NOX4 plays a role in DHA inhibiting adipocyte differentiation
We then investigated whether NOX4 plays a role in DHA inhibiting adipocyte differentiation. Firstly, we knocked down NOX4 with siRNAs in HPA-v cells, and qRT-PCR results showed that the NOX4 gene in HPA-v cells was knocked down nearly $50\%$ at 24 h after siRNA transfection ($P \leq 0.01$) (Fig. 6A). At the end of the induction, oil red O staining showed that the density of lipid droplets in the NOX4-knockdown group was significantly lower than that in the control group, and the change in intracellular TG levels was consistent with the results of oil red O staining ($P \leq 0.01$) (Fig. 6B and C). Consistent with cellular phenotype, the qRT-PCR results revealed that the adipocyte differentiation-related genes Pparγ2, C/Ebpα, Fabp4 and Glut4 were significantly downregulated upon NOX4 knocking down ($P \leq 0.01$) (Fig. 6D–G), indicating that the NOX4 gene was critical in promoting adipocyte differentiation. In addition, we also knocked down *Nox4* gene in mouse primary white fat precursor cells using siRNA (Fig. 6H). The oil red O staining and TG levels showed that knocking down Nox4 inhibited adipogenic differentiation of preadipocytes, but its inhibition was not further aggravated by 20 μM DHA treatment (Fig. 6I and J). We then over-expressed *Nox4* gene in primary white fat precursor cells (Fig. 6K), the oil red O staining and TG levels showed that the over-expression of Nox4 partially reversed the inhibition effect of DHA on preadipocytes differentiation (Fig. 6L and M). These results affirmed that NOX4 accounted for the differentiation of adipocytes, and that the inhibition of NOX4 by DHA was the key underlying mechanism by which DHA inhibited adipose differentiation. Fig. 6NOX4 plays a role in DHA's adipocyte differentiation inhibition effect. ( A) The knocking down efficiency of the Nox4 siRNA was assessed by qRT-PCR in HPA-v cells at 24 h after transfection ($$n = 3$$ per group). ( B–C) (B) Representative oil red O-stained images (200 ×) and (C) TG levels of differentiated HPA-v cells transfected with NOX4 siRNA ($$n = 3$$ per group). ( D–G) mRNA levels of adipocyte differentiation-related genes (Pparγ2, C/Ebpα, Fabp4 and Glut4) in HPA-v cells transfected with NOX4 siRNA ($$n = 3$$ per group). ( H) The transcription level of *Nox4* gene was assessed by qRT-PCR in primary white fat precursor cells 24 h after Nox4 siRNA transfection ($$n = 3$$ per group). ( I–J) (I) Representative oil red O-stained images (200 ×) and (J) TG levels of DHA-treated differentiated primary white fat precursor cells transfected with Nox4 siRNA ($$n = 3$$ per group). ( K) The NOX4 overexpression efficiency in primary white fat precursor cells was assessed by WB ($$n = 3$$ per group). ( L–M) (L) Representative oil red O-stained images (200 ×) and (M) TG levels of DHA-treated differentiated primary white fat precursor cells transfected with Nox4 overexpression plasmid. The values are mean ± SEM. * $P \leq 0.05$, **$P \leq 0.01$ and ***$P \leq 0.001$; n.s., not significant ($p \leq 0.05$). ( For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)Fig. 6
## UPLC–MS/MS-based targetomic analysis can be used to explore the effects of DHA on adipocytes
In order to assess the effect of DHA on adipocytes more comprehensively, we used a target-responsive accessibility-profiling approach that measures DHA-induced steric hindrance of proteins via UPLC–MS/MS-based global-profiling of accessibility changes in reactive lysines. This method was previously termed as targetome [27]. During the differentiation of HPA-v cells, we added 20 μM DHA to the culture medium, and the cells were harvested until completion of induction. The DHA targetome was thus profiled, and we discerned that DHA treatment altered the conformations of 85 proteins (Data S1). Of these, FASN (one of the key enzymes in the fatty acid synthetic pathway), was shown to be conformationally changed upon DHA treatment, with reduced abundance of two unique dimethylated K-containing peptides (Data S1). KEGG-pathway analysis showed that the modified proteins were significantly enriched in fatty acid metabolism; TCA cycle; carbon metabolism; valine, leucine, and isoleucine degradation; and protein processing in endoplasmic reticulum (Fig. 7A and B). These data indicated that DHA significantly affected the metabolism of adipocytes. Fig. 7KEGG-pathway analysis of conformationally modified proteins in DHA-treated, differentiating adipocytes identified by targeted proteomics. ( A) KEGG pathway enrichment analysis of 85 conformationally altered proteins was performed with the ClueGO app included in Cytoscape. Pathways with a p value < 0.01 (by two-sided hypergeometric test) and the inclusion of at least three genes are summarized. ( B) Gene *Ontology analysis* of KEGG pathways using Database for Annotation, Visualization and Integrated Discovery (DAVID, v6.8).Fig. 7
## Discussions
Obesity and its related disorders have developed into a global pandemic, and although the U.S. Food and Drug Administration (FDA) has approved five drugs for long-term weight management, their modest efficacy and unfavorable side effects limit their clinical application [30]. As a clinical antimalarial drug, artemisinin and its derivatives possess the advantages of moderate pricing, little toxicity, and side-effects. Recent studies have shown that artemisinin derivatives improved HFD-induced obesity by promoting the browning of white fat and enhancing brown adipose tissue function [23]. However, in a follow-up report these authors did not achieve a similar effect, and no typical browning-related genes (such as Ucp1, Pgc-1α, or Prdm16) were found among the differentially expressed genes identified by RNA-Seq [31]. A recent report showed that artemisinin derivatives DHA or artesunate (ATS) inhibited adipogenic differentiation of preadipocytes 3T3-L1 by up-regulating C/Ebp-Homologous Protein (CHOP) [31]. In addition, some studies have shown that artemisinin derivatives can inhibit the differentiation of preadipocytes through reducing expression of C/Ebp δ [32] or reducing the expression and/or phosphorylation levels of C/Ebp-α, PPAR-γ, FAS, perilipin A, and STAT-3 [33]. Therefore, the mechanism(s) underlying the anti-obesity effects of DHA remains controversial. In the present study we demonstrated that DHA improved HFD-induced obesity and enhanced glucose metabolism in obese mice. DHA treatment also reduced the white fat mass, possibly by inhibiting adipocyte differentiation. Our research confirms for the first time that the reduction in NOX4 in adipocytes served as a molecular mechanism mediating the anti-obesity effects of DHA.
White adipose tissue stores energy through adipogenesis in response to caloric excess, but excessive WAT poses a serious threat to metabolic health [34]. The number of adipocytes in lean and obese adults is in a dynamic and stable state, adipocyte renewal and lipid accumulation constitute important events in adipose tissue enlargement [12], and we acknowledge that adipocytes are generated through adipogenesis from specific precursor cells. In our study, DHA treatment reduced the volume of white fat and the expression of Pparγ in the eWAT in DIO mice, suggesting that DHA inhibited the differentiation of adipocytes; and this was confirmed by the adipogenic differentiation model of HPA-v cells in vitro. The inhibition of adipogenesis may thus comprise the principal cause of the anti-obesity effect of DHA. It is noteworthy that the decrease of liver weight and serum NEFAs in obese mice treated with DHA we observed in our study could be secondary to the change of fat tissue metabolism or through other mechanisms, which needs further study.
It is well known that aerobic cells produce a series of ROS in metabolic processes; these include O2−, H2O2, HO2 • and • OH [35]; and free radicals in the body regulate the balance of cellular life and death through changes in their concentrations. In addition to their functions in apoptosis and necrosis, the broader physiologic significance of low ROS concentrations reflects the activation of transcription factors and the promotion of cellular proliferation and differentiation [[36], [37], [38]]. ROS are thought to interact with several pathways that affect the transcriptional machinery required for mesenchymal stem cells differentiation, and tightly regulated levels of ROS are therefore critical for the terminal differentiation of mesenchymal stem cell [17].
Among various enzymes responsible for ROS generation, NOXs are the major and most widely studied sources of ROS [16,39]; and NOX4 is an important source of ROS production in adipocytes, playing a crucial role in adipogenesis [22,40]. Previous studies revealed that overexpression of NOX4 in human preadipocytes promoted the accumulation of fat droplets, while NOX4 knockdown inhibited adipocyte differentiation [17,22]. Both our in vivo and in vitro experiments showed that DHA was able to significantly downregulate NOX4 expression in the WAT of obese mice, and that the adipose precursor cells induced to differentiate after inhibition of NOX4 by siRNA or GKT137831 also reduced the differentiation of adipocytes. And overexpressed NOX4 in DHA-treated cells significantly weakened the inhibitory effect of DHA. In terms of a subserving mechanism of action, a previous report showed that H2O2 induced activation of cAMP response element-binding protein (CREB) transcription [17]. CREB has been implicated as an early regulator of the adipocyte differentiation program, and in 3T3-L1 preadipocytes, CREB was identified as a transcription factor that regulated the adipocyte marker C/Ebpβ during adipocyte differentiation [41,42]. In addition to CREB, other investigators reported that preadipocytes released the EGF-like protein Pref-1 to maintain the undifferentiated state of preadipocytes. Pref-1 activates the MEK/ERK-pathway to block the induction of the key transcription factor Pparγ2 for further differentiation [43,44]. Pref-1 expression is completely lost in the course of adipocyte differentiation, but the downregulation of Pref-1 was prevented by Nox4 knockdown using a Nox4 siRNA [22]. These studies revealed that the expression of NOX4 was closely related to abnormal cellular metabolic pathways, and our data also showed that inhibition of NOX4 downregulated the expression of adipocyte differentiation related genes such as Pparγ2 and C/Ebpα. It is hypothesized that the regulation of NOX4 expression by DHA may be part of the molecular mechanism by which DHA inhibits adipose differentiation.
In order to assess the effect of DHA on adipocytes more comprehensively, we implemented UPLC–MS/MS-based targetome analysis in DHA-treated HPA-v cells. The targetomics approach of labeling the protein with CD2O before digestion has been proven to directly detect alterations in potential target proteins by quantifying unique peptides with and without treatment [27,45]. We can therefore define a group of peptides with altered abundances and thus reveal potential DHA-protein interactions. In our experimental setting, a total of 104 peptides mapped to 85 proteins were found to be conformationally changed after DHA treatment. By KEGG analysis, we observed that these proteins were significantly enriched in endogenous metabolism pathways, such as fatty acid metabolism; TCA cycle; carbon metabolism; valine, leucine, and isoleucine degradation; and protein processing in endoplasmic reticulum pathways. Based upon published studies, there is an elevated flux within the TCA cycle and fatty acid oxidation during adipocyte differentiation and enlargement [46]. It has been reported that catabolism of branched-chain amino acids, such as leucine and isoleucine fueled adipocyte differentiation and lipogenesis [47]; and endoplasmic reticulum stress was also associated with adipocyte differentiation [48]. FASN, which plays a key role in reducing lipid accumulation in adipocytes, was additionally specifically demonstrated to be conformationally changed upon DHA treatment. Thus, the targetomics analysis provided additional evidence that DHA affected adipocyte metabolism.
There are limitations of our study. Firstly, we did not use white adipose-specific NOX4 overexpression mice to verify the key role of NOX4 in DHA inhibiting adipocyte differentiation. Secondly, we did not verify the effect of GKT137831 on body weight and lipid metabolism of WAT of DIO mice in vivo. These limitations deserve our in-depth research in the future.
## Conclusions
In summary, our study revealed that DHA exerts a therapeutic effect on obesity in an HFD-induced obese mice by inhibiting adipocyte differentiation, and that its mechanism of action may lie in its inhibitory effect on the ROS-producing enzyme NOX4 and abnormal cell-metabolism pathways. The study of these functions and underlying molecular mechanisms provides a greater theoretical basis for DHA in the treatment of obesity, enabling the potential for DHA to be applied as a therapy for obese patients.
## Consent for publication
All authors approved the final version of the manuscript for publication.
## Availability of data and materials
Raw data from our UPLC–MS/MS-based targetomics analysis have been deposited into the iProX system with the project number IPX0003263000, and can be viewed at https://www.iprox.cn/page/PSV023.html;?url=1626145160861VLpW by entering the accession number (password) “RahH”. The other raw data supporting the conclusions of this article will be made available by the authors, upon reasonable request and without undue reservation.
## Ethics approval
This animal study was reviewed and approved by the Institutional Animal Care and Use Committee of Nanjing Medical University.
## Author contribution statement
Hu Hua, Mengqiu Wu: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.
Tong Wu: Analyzed and interpreted the data; Wrote the paper.
Yong Ji, Lv Jin, Yang Du, Yue Zhang, Songming Huang, Aihua Zhang: Contributed reagents, materials, analysis tools or data.
Guixia Ding, Qianqi Liu: Conceived and designed the experiments.
Zhanjun Jia: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.
## Funding statement
The study was supported by grants from the $\frac{10.13039}{501100001809}$National Natural Science Foundation of China [82090020, 82090022, 82000642], the $\frac{10.13039}{501100004608}$Natural Science Foundation of Jiangsu Province (BK20191123), and the Science and Technology Development Foundation of $\frac{10.13039}{501100007289}$Nanjing Medical University (NMUB2020038).
## Data availability statement
Data associated with this study has been deposited at *Raw data* from our UPLC–MS/MS-based targetomics analysis have been deposited into the iProX system with the project number IPX0003263000, and can be viewed at https://www.iprox.cn/page/PSV023.html;?url=1626145160861VLpW by entering the accession number (password) “RahH”.
## Declaration of interest’s statement
The authors declare no competing interests.
## Supplementary data
The following are the *Supplementary data* to this article:Multimedia component 1Multimedia component 1Multimedia component 2Multimedia component 2
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|
---
title: Optical Glucose Sensors Based on Chitosan-Capped ZnS-Doped Mn Nanomaterials
authors:
- Son Hai Nguyen
- Phan Kim Thi Vu
- Hung Manh Nguyen
- Mai Thi Tran
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10006924
doi: 10.3390/s23052841
license: CC BY 4.0
---
# Optical Glucose Sensors Based on Chitosan-Capped ZnS-Doped Mn Nanomaterials
## Abstract
The primary goal of glucose sensing at the point of care is to identify glucose concentrations within the diabetes range. However, lower glucose levels also pose a severe health risk. In this paper, we propose quick, simple, and reliable glucose sensors based on the absorption and photoluminescence spectra of chitosan-capped ZnS-doped Mn nanomaterials in the range of 0.125 to 0.636 mM glucose corresponding to 2.3 mg/dL to 11.4 mg/dL. The detection limit was 0.125 mM (or 2.3 mg/dL), much lower than the hypoglycemia level of 70 mg/dL (or 3.9 mM). Chitosan-capped ZnS-doped Mn nanomaterials retain their optical properties while improving sensor stability. This study reports for the first time how the sensors’ efficacy was affected by chitosan content from 0.75 to 1.5 wt$.\%$. The results showed that 1 %wt chitosan-capped ZnS-doped *Mn is* the most-sensitive, -selective, and -stable material. We also put the biosensor through its paces with glucose in phosphate-buffered saline. In the same range of 0.125 to 0.636 mM, the sensors-based chitosan-coated ZnS-doped Mn had a better sensitivity than the working water environment.
## 1. Introduction
Although glucose is an energy source, having too much or too little in the blood might have adverse health effects. While excessive blood glucose levels result in diabetic disease, extremely low blood sugar can have serious health consequences, including seizures, stroke, and brain damage [1]. As a result, the healthcare system must monitor glucose levels and detect anomalies [2]. The industry-standard method for estimating glucose concentration in saliva using optical measuring tools is liquid chromatography–mass spectrometry (LC–MS) [3]. However, the process is time-consuming and requires expensive chemicals, specialized equipment, and trained personnel. Recently, optical biosensors based on nanomaterials have been developed to take the place of traditional glucose measurement methods [4]. Optical biosensors based on absorption and fluorescence detection appeal to researchers due to their low production costs, low specimen consumption requirements, low energy consumption, excellent accuracy, and rapid response. A variety of photosensitive materials, in particular semiconductors, were chosen to detect glucose based on its fluorescence intensity [5,6,7]. The optical properties of semiconductor nanoparticles were commonly modified using size-selective production and doping nanocrystals with the appropriate transition metal ions [8]. However, some of these compounds have the severe disadvantage of harming people. The main contributors to their cytotoxicity are heavy metal ion disintegration and release and highly reactive oxygen species [9]. On the other hand, several non-toxic materials are utilized in blood glucose testing, such as Mn-doped CdTe/ZnS [10]. Fluorescence resonance energy transfer sensors based on CdSe-ZnS quantum dots were reported by Freeman et al. to detect glucose with a limit of detection of 1.8 mg dL−1 [11]. To detect glucose in blood samples as low as 25 mg dL−1, Yu et al. introduced glucose oxidase (GOx) optical biosensors [12]. As an alternative, Zhang et al. described employing Pt electrodes coated with GOx/GNP/SWNT/PAA to create real-time enzyme glucose sensors for saliva samples [3]. Among all the semiconductor nanomaterials recently investigated in medicine, ZnS and ZnS nanocrystals doped with transition metal ions such as Cu2+, Co2+, and Mn2+ primarily exhibit properties such as water solubility, biocompatibility, high luminescence, image stability, and especially, low cytotoxicity. Because, at room temperature, ZnS has a bandgap energy of about 3.7 eV, making it transparent to solar spectrum wavelengths, fascinatingly, ZnS emission becomes more refined when doped with additional ionic transition metals such as Ni, Fe, Mg, Co, Mn, and Cu [13,14,15]. When combined with Mn2+ ions, ZnS nanoparticles emit light at 585 nm [16]. The energy transfer from the ZnS band gap to the Mn2+ dopant (from the triplet state 4T1 to the ground state 6A1) of the Mn2+ incorporated into the ZnS host lattice is what causes this phosphorescence emission [17].
Despite having improved luminescence properties, ZnS-doped compounds containing various transition metal ions frequently exhibit issues such as poor nanostructure quality and limited control over the emission color [17]. The production of ZnS-doped Mn using capped chitosan (CH) was recently described by Sharma et al. [ 17]. Since chitosan is hydrophilic, biodegradable, biocompatible, antigen-free, non-toxic, and biofunctional, it is the ideal polymer for biological applications [18]. In this context, chitosan stabilizes the ZnS nanoparticles (NPs) and alters the sample’s photoluminescence (PL) intensity and emission wavelength [19]. Additionally, GOx is the industry standard enzyme for bio probes due to its improved glucose selectivity. In particular, GOx is more resistant to extreme temperatures, pH levels, and ionic strengths than most other enzymes. As a result, making and affixing biological probes to semiconductor nanoparticles is relatively straightforward [20].
In this study, we prepared nanomaterials from ZnS-doped Mn with varying chitosan ratios ($0.75\%$, $1\%$, $1.25\%$, and 1.5 %wt). Then, utilizing optical methods in a low testing range from 0.125 to 0.636 mM, the produced materials for GOx-based sensors were used to detect pure glucose and glucose in phosphate-buffered saline (PBS). We used PBS, regarded as a common buffer solution, to assess the biosensor’s selectivity. By estimating the sensitivity of ZnS-doped Mn with chitosan and GOx, our sensors’ high sensitivity, dependability, and quick response to a deficient glucose level were demonstrated. This work paves the way for the development of a diagnostic technology based on a small saliva sample that can deliver quick results with high reliability for clinical examination of patients and treatment outcome forecasting.
## 2.1. Chemicals
In our experiments, the raw materials used without any further purification for preparing the samples were zinc acetate (Zn (CH3COO)2·2H2O) ($99.99\%$, Merck KGaA, Darmstadt, Germany), sodium sulphide nonahydrate (H19Na2O9S) ($99.99\%$, Shanghai Zhanyun Chemical Co., Ltd., Shanghai, China), chitosan (C56H103N9O39) ($99.99\%$, Shanghai Zhanyun Chemical Co., Ltd., Shanghai, China), manganese acetate (Mn (CH3COO)2·2H2O) ($99.99\%$, Merck KGaA, Darmstadt, Germany), acetic acid (CH3COOH) ($100\%$, Merck KGaA, Darmstadt, Germany), and distilled water.
## 2.2. Preparation of ZnS: Doped Mn Nanomaterials Capped with Chitosan (CH-ZnS/Mn)
First, we mixed 8.78 g zinc acetate and 0.08 g manganese acetate into 80 mL deionized water for 30 min at room temperature to make Suspension A. Meanwhile, Suspension B was created by completely dissolving 0.5 g chitosan (CH) in 40 mL acetic acid $1\%$. Suspension B was combined with Suspension A to make a final mixture with CH ratios of $0.75\%$, $1\%$, $1.25\%$, and 1.5 %wt. The 2.4 g sodium sulfide was then gradually added to the mixture and mixed for two hours. Precipitation began almost immediately, and the concentration of the residue increased. The mixture was transferred to a Teflon autoclave and incubated at 80∘C for two hours. Following the hydrothermal treatment, the precipitation was cleaned several times by centrifuging it for five minutes at 4000 rpm with ethanol. Finally, the cleaned granules were dried in a vacuum oven for eight hours at 60∘C.
## 2.3. Measuring the Optical Properties of Glucose Sensors Based on CH-ZnS/Mn
The optical absorbance of the produced CH-ZnS/Mn in the 220–800 nm range was measured using a DeNovix UV–Visible spectrometer (Model: DS-11 FX+). The samples were placed in 10 mm-thick cuvettes. The cuvettes were filled with 1500 μL of 1000 mg/L ZnS-doped Mn solution before gradually adding 0.2 mM glucose solution with volumes ranging from 100 μL to 700 μL. All samples’ photoluminescence (PL) characteristics were evaluated using a 10 nm slit-width spectrophotometer (SpectraPro HRS-300, Teledyne Princeton Instruments, Trenton, NJ 08619 USA). As previously stated, each sample was placed in a 10 mm-thick cuvette before being added.
## 3.1. Characterizations of CH-Capped ZnS-Doped Mn Nanomaterials
The produced materials were measured using the XRD and SEM images as the initial stage in characterizing their structure and shape. The 1 %wt CH-ZnS/Mn materials’ XRD and SEM results are displayed in Figure 1. Similar XRD spectrum patterns can be seen in the other materials with various CH percentages. In Figure 1A, three peaks at positions [111], [220], and [311] demonstrated the cubic sphalerite structure of ZnS-doped Mn (ZnS/Mn) (JCPDS Card No.5-0566). This observation indicates that the %wt CH may not significantly impact the structure of the starting components. The particles in those manufactured materials are shaped. The diameter of the grains is less than 500 nm (see Figure 1B). In the next section, the prepared materials were utilized in glucose sensing applications by UV–Vis measurements.
## 3.2. UV–Vis Measurements of Glucose Sensors Based on CH-ZnS/Mn Materials
The UV spectra of 1 %wt CH-ZnS/Mn nanoparticles and their reactions to various glucose concentrations in water are shown in Figure 2A. The quenching effect of glucose is visible in the spectra. The biosensor’s absorbance decreased without a peak shift when the glucose concentration increased. In addition, the absorbance varied linearly with the glucose level in the range of 0.125 mM to 0.636 mM based on the absorbance at 230 nm (see Figure 2B). Figure 3 demonstrates the sensors’ performances with different CH-ZnS/Mn concentrations. The relationships between the raw absorbance at 230 nm and the sensitivity at 230 nm versus the glucose concentration are shown in Figure 3A,B, respectively. In Figure 3B, the sensitivity was calculated by the equation S=(A0−A)/A0, where S is the sensitivity. A0 and A are the absorbances before and after adding glucose, respectively. Because of its high absorbance and high sensitivity (the highest precision) shown in Figure 3, 1000 mg/L of 1 %wt CH-ZnS/Mn was chosen for the absorbance glucose sensors’ fabrication.
## 3.3. Photoluminescent Measurements of Glucose Sensors Based on CH-ZnS/Mn Materials
In this section, we investigated the potential use of CH-ZnS/Mn nanomaterials for room-temperature photoluminescence (RTP) and employed photoluminescent (PL) measurements as an analytical analysis for the glucose biosensors. As shown in Figure 4, the quenching effect of glucose in water was observed. Based on the photoluminescent intensity, we can develop a functional operating system for TRP glucose sensors and calculate the sensitivity (S) corresponding to the glucose concentration by the equation S=(I0−I)/I0, where I0 is the photoluminescence intensity before adding glucose and I is the corresponding intensity in contact with glucose. We examined the same region using UV–Vis measurements between 0.125 and 0.636 mM. The relationship between the intensity in this linear working range and the glucose levels can be described by the linear function $y = 0.682$x+0.087. This result was determined from the average of 15 measurements.
## 3.4. CH-ZnS/Mn-Based Glucose Sensor Performance for Glucose in the Artificial Saliva and Phosphate-Buffered Saline and Its Sensitivity
In the previous section, the performance of the sensors for glucose in water was analyzed. Here, we examined the operating performance of the CH-ZnS/Mn sensor for glucose in phosphate-buffered saline (PBS) using UV–Vis and PL measurements. With glucose in PBS and PBS separately, the procedures in Section 3.2 and Section 3.3 were repeated. Our experiments showed that the UV spectra (Figure 5A) and PL measurements (Figure 6A) produced strong quenching signals in reaction to the glucose in the PBS. As a result of our observations, calibration lines that plot the sensitivity or the absorbance/PL against the glucose concentration can be developed. In particular, the calibration line for UV–Vis performance can be estimated as y=−0.323x+1.322, where x is the glucose concentration and y is the absorbance (see Figure 5C). As shown in Figure 5B,D, when the sensors and PBS (without glucose) were combined, the added volume changed from 100 μL to 700 μL, but the absorbance barely changed, and the trend of the change was not linear. This result demonstrated that glucose, not PBS, was responsible for the effects on the CH-ZnS/Mn sensors.
As we introduced the glucose in the PBS suspension, the emission spectra were captured at an excitation wavelength of 365 nm and are displayed in Figure 6A. When the glucose concentration increased, the absorbance and photoluminescence dropped, demonstrating agreement between the absorbance and emission spectra. When ZnS-doped Mn with a 1 %wt CH cap responded to the glucose in the PBS, the emission peaked at about 530 nm (Figure 6A). The quenching effect was represented in a calibration curve for the biosensor’s operating range of 0.125 to 0.636 mM (Figure 6B). After adding the glucose, the position of the emission peak remained constant. The sensitivity changed linearly with the concentration of the added glucose as a function of $y = 1.098$x+0.065, where x is the glucose concentration (mM) and y is the sensitivity. With this high sensitivity and accuracy, the promising applications in clinical examinations of the fluorescent glucose sensors based on 1 %wt CH-ZnS/Mn were confirmed. Comparing the synthesized sensors to earlier works, Table 1 shows that they not only had a low limit of detection (LOD) and a low cost, but also a straightforward measurement setup.
## 3.5. The Effect of %wt CH on the Biosensor’s Performance
To investigate the effect of the %wt CH in the glucose sensing application of the ZnS/Mn-based sensors, we prepared four materials with the CH ratios of 0.75 wt%, 1 wt%, 1.25 wt%, and 1.5 wt%. Each material was suspended at a concentration of 1000 mg/L and tested for the UV and PL performances with different glucose concentrations. As shown in Figure 7, except for the 0.75 %wt CH-ZnS/Mn sensors, the rest of the sensors had similar slopes. However, the 1 %wt CH materials had the highest absorbance. This observation was also seen in the PL spectra (Figure 8), where the 1 %wt CH showed the most-linear and highest photoluminescence intensity. Hence, we recommend using the 1 %wt CH-ZnS/Mn for glucose sensing applications.
## 4. Conclusions
As a result of this investigation, we successfully manufactured CH-ZnS/Mn nanomaterials for glucose biosensors that performed significantly linearly and consistently in both water and PBS in the range of 0.125 mM to 0.636 mM. The developed sensor has a limit of detection of 0.125 mM. As per our recommendation, 1000 mg/L of 1 %wt CH-ZnS/*Mn is* the optimal sensor for glucose in water and PBS. In the following steps, we can test our sensors using various samples, such as glucose in artificial saliva, natural saliva, blood, or urine. Additionally, as mentioned in a recent publication, scientists employed CH-ZnS/Mn nanomaterials for various biological applications, including detecting bacteria [25], selenite [26], and other applications. We can optimize the materials developed for multiple purposes to create dependable, quickly reacting, and affordable gadgets.
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|
---
title: 'Taking a Load Off: User Perceptions of Smart Offloading Walkers for Diabetic
Foot Ulcers Using the Technology Acceptance Model'
authors:
- M. G. Finco
- Gozde Cay
- Myeounggon Lee
- Jason Garcia
- Elia Salazar
- Tze-Woei Tan
- David G. Armstrong
- Bijan Najafi
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10006940
doi: 10.3390/s23052768
license: CC BY 4.0
---
# Taking a Load Off: User Perceptions of Smart Offloading Walkers for Diabetic Foot Ulcers Using the Technology Acceptance Model
## Abstract
People with diabetic foot ulcers (DFUs) are commonly prescribed offloading walkers, but inadequate adherence to prescribed use can be a barrier to ulcer healing. This study examined user perspectives of offloading walkers to provide insight on ways to help promote adherence. Participants were randomized to wear: [1] irremovable, [2] removable, or [3] smart removable walkers (smart boot) that provided feedback on adherence and daily walking. Participants completed a 15-item questionnaire based on the Technology Acceptance Model (TAM). Spearman correlations assessed associations between TAM ratings with participant characteristics. Chi-squared tests compared TAM ratings between ethnicities, as well as 12-month retrospective fall status. A total of 21 adults with DFU (age 61.5 ± 11.8 years) participated. Smart boot users reported that learning how to use the boot was easy (ρ =−0.82, p ≤ 0.001). Regardless of group, people who identified as Hispanic or Latino, compared to those who did not, reported they liked using the smart boot ($$p \leq 0.05$$) and would use it in the future ($$p \leq 0.04$$). Non-fallers, compared to fallers, reported the design of the smart boot made them want to wear it longer ($$p \leq 0.04$$) and it was easy to take on and off ($$p \leq 0.04$$). Our findings can help inform considerations for patient education and design of offloading walkers for DFUs.
## 1. Introduction
Of the estimated 30 million people in the US with diabetes, $34\%$ will develop a diabetic foot ulcer (DFU) in their lifetime [1]. DFUs, which precede $80\%$ of amputation in people with diabetes, are associated with impaired physical function, reduced quality of life, and increased risk of death [2]. Ulcers requiring acute care can result in treatment costs of up to USD 70,000 per event, varying with the severity of the wound [3]. The annual direct cost related to DFUs in the US is almost USD 40 million, despite being a preventable complication of diabetes [4,5]. The standard of care for DFU management is protective offloading with either an irremovable or removable offloading boot, which allows the wound to heal while the person remains ambulatory [6,7,8,9].
However, inadequate adherence to the prescribed use of offloading devices could be a potential barrier to ulcer healing. Irremovable knee-high offloading devices are recommended for offloading intervention [10]. Removable offloading devices are recommended as a second option but are often more frequently prescribed than irremovable devices due to cost and healthcare team expectations of increased patient adherence [11]. People who wore offloading devices for 90 days had significantly higher acceptance of removable boots, compared to irremovable walkers or contact casts [12]. Despite higher rates of healing in irremovable devices [13,14], time to healing and amputation rates in removable walkers were comparable to the irremovable device literature [15]. Further, people who used removable walkers showed significantly more activity beginning at week 4, suggesting changes in adherence [15].
Despite adherence being a barrier to ulcer healing, few studies have investigated patient perceptions of offloading devices to help inform ways to improve adherence [16]. Several factors have been associated with low adherence to removable walkers, such as being male, a longer time with diabetes, not having peripheral arterial disease and higher perceived walker heaviness, as well as low wound healing and postural instability [17,18]. Additionally, a thematic analysis of people who wore a removable walker for anywhere between 1 week to 3 years found that although people reported they understood the benefits of the device, they also felt pressure from managers/coworkers not to wear it at work, did not like the height imbalance, and stated that the device felt heavy [19]. While studies have examined perceptions of offloading devices, no literature has examined perceptions surrounding the addition of technology offloading devices.
With advances in wearables, digital health, and remote patient monitoring technology, new solutions have emerged to help actively engage patients in caring for their wound, rather being passive recipients of wound care. However, patients’ acceptance of these solutions, as well as factors that may influence perceptions, are still unknown. Park et al. proposed the concept of smart offloading to reinforce adherence in using offloading devices and tested its proof of concept validity, comfort level, and ease of use in healthy adults without DFUs [20]. Further, Najafi et al. proposed the concept of smart insoles in people with a history of DFU and found users who received one alert every two hours were significantly more adherent to use their prescribed footwear [21]. To our knowledge, no prospective study has examined the acceptability and factors affecting adherence to smart-offloading devices for people with active DFUs. Thus, additional literature on perspectives of offloading devices with and without technology could help inform ways to promote adherence in people with DFUs. This knowledge could help inform factors that may be associated with the acceptance of smart offloading, particularly in older adults with diabetic foot syndrome.
This study is the first to explore perceptions surrounding smart offloading with real-time feedback in people with DFUs, and what participant characteristics may be associated with acceptability. The objective of this study was to examine user perspectives of irremovable, removable, and sensorized offloading walkers to provide insight on ways to help promote adherence. This study also sought to gain insight about factors associated with the acceptance of a smart offloading device with a remote patient monitoring component. Research outcomes were user perspectives on offloading boots, which were expressed through a questionnaire based on the Technology Acceptance Model.
## 2. Materials and Methods
This manuscript presents preliminary qualitative findings from an ongoing parallel randomized control trial (ClinicalTrials.gov Identifier: NCT04460573) to investigate the influence of a sensorized offloading walker on health outcomes in people with DFUs, termed Smart Monitoring of patient *Activity via* Remote Technologies for Best Optimizing Offloading Therapy (SMARTBOOT). All participants signed an approved consent form before enrolling in this study. The study protocol and consent form were approved by the University of Southern California Institutional Review Board (protocol number: HS-20-00526). A computer-generated list (MATLAB software) randomly assigned participants in a 1:1:1 ratio to one of three offloading device groups: [1] irremovable cast walker (iRCW, reference group), [2] original removable cast walker (oRCW, control group), or [3] smart removable cast walker (sRCW, intervention group). The offloading component was identical between groups and only the method for managing adherence was different. All participants wore their offloading device for 12 weeks, or until their ulcer was deemed healed by a physician.
Participants were recruited from the Keck School of Medicine (Los Angeles Metropolitan, CA, USA). To be included in the study, individuals had to be over 18 years of age, have diabetes mellitus, have a plantar ulcer, have evidence of peripheral neuropathy, and be ambulatory at home with or without assistance, and be willing and able to provide informed consent. Individuals were excluded from participating in the study if they had major foot deformity so that the patient could not fit to standard offloading (e.g., Charcot neuroarthropathy), active infection, major lower limb amputation, changes in psychotropic or sleep medication in the last 6 weeks, any clinically significant medical or psychiatric condition, severe cognitive impairment, or laboratory abnormality that would interfere with the ability to participate in the study. Additionally, individuals were excluded from participating in the study if they were being considered for revascularization during the study, concurrently participating in exercise training, or unable or unwilling to attend prescribed clinic visits or comply with protocol. Only participants who completed all self-report data (TAM and all participant characteristics reported in this manuscript) were included in the analysis. Figure 1 depicts the number of participants assessed for eligibility, and those who were excluded or included. After providing informed consent, demographics were collected, which included age, sex, weight, height, and number of 12-month self-reported retrospective falls.
Study groups are depicted in Figure 2. The iRCW was sealed with patches of leather, so participants could not readily remove the boot, the oRCW was off-the-shelf with no additional modifications, and the sRCW had a sensor-based system that was designed to provide feedback on adherence.
The sRCW and its validity were described in detail in our prior publication on healthy controls [20]. In summary, sRCW includes an identical offloading as iRCW and oRCW, however it uses a six-degree-of-freedom inertial measurement unit (Sensoria Health, Seattle, WA, USA, Figure 3) attached on the strut of offloading, enabling real-time measurement of adherence, walking steps, and walking cadence. Participants received real-time feedback about adherence and walking steps using a smartwatch with a dedicated patient monitoring app. The Bluetooth Low Energy module enabled real-time communicate of parameters of interest with the smartwatch. The microcontroller in the smartwatch processed the data and showed real-time (with maximum 5 s lag time) boot condition (boot on or boot off), activity condition (active or resting), step count, and notifications. Additionally, data were streamed to a secured cloud-based system for a remote patient monitoring solution, via a 4G LTE Internet of Things (sim card enabled). This allowed the remote monitoring of parameters of interest (e.g., adherence, daily steps with and without adherence, and cadence), which could be used by clinicians to personalize patient education during weekly visits. Participants received real-time notifications from their smartwatch to encourage adherence via visual (e.g., happy face for good adherence, sad face for poor adherence) and vibration/audio feedback (walking while not wearing offloading). Additionally, participants had a daily comprehensive report via watch interface about level of adherence and daily steps.
Participants completed the following patient-reported outcomes: the Montreal Cognitive Assessment (MoCA) to assess cognition [22], the Falls Efficacy Scale International (FES-I) to assess falls efficacy [23], and the Patient Reported Outcomes Measurement Information System (PROMIS-29) to assess quality of life [24].
To assess perspectives on device acceptability, participants also completed a 15-item questionnaire based on the Technology Acceptance Model (TAM) [25], with a 5-point Likert scale, with the following options: strongly disagree, disagree, neutral, agree, and strongly agree. The 15 items are listed in Figure 4.
Participants who reported identifying as Hispanic or Latino were classified as Hispanic or Latino, while those who did not were classified as Non-Hispanic or Latino. Additionally, participants who reported experiencing at least one fall in the past 12 months were categorized as fallers, while those who did not report experiencing any falls in the past 12 months were categorized as non-fallers.
Spearman correlations were performed to determine associations between participant characteristics and TAM ratings. Chi-squared tests of independence were performed to examine the relationship between TAM ratings with ethnicity (Hispanic or Latino, Non-Hispanic or Latino), as well as TAM ratings with 12-month retrospective fall status (faller, non-faller). All statistical analyses were performed using IBM SPSS Statistics 25 (IBM, Chicago, IL, USA). Statistical significance in all tests was considered to be a 2-sided p-value of p ≤ 0.05.
## 3. Results
A total of 21 adults with DFUs (age 61.5 ± 11.8 years; $85.7\%$ male) were randomized to use an iRCW ($$n = 10$$), oRCW ($$n = 6$$), or sRCW ($$n = 5$$). Participant characteristics by ethnicity and fall status are depicted in Table 1. People who identified as Hispanic or Latino, compared to those who did not, had significantly higher cadence (Table 1). Fallers, compared to non-fallers, had significantly higher T-scores on PROMIS-Cognitive Function and PROMIS-Depression items, which is interpreted as having higher indications of cognitive function and depression (Table 1).
The majority of participant characteristics had no significant correlations with any of their self-reported TAM ratings, which are presented in Supplementary Table S1. Correlations with significance are depicted in Figure 4. Due to the high number of correlations with a p-value of p ≤ 0.05, only those with a p-value of p ≤ 0.001 are discussed. Participants who used the smart boot (ρ = −0.82, $p \leq 0.001$) reported that learning how to use the boot was easy (Figure 4). Participants who had lower cadence (ρ = 0.74, $p \leq 0.001$) or deeper ulcers (ρ = −0.55, $p \leq 0.001$) reported that the boot helped them follow physician orders (Figure 4). Participants with lower T-scores on the PROMIS-Pain Interference, indicating less pain, reported feeling more connected to their care provider (ρ = 0.66, $p \leq 0.001$) (Figure 4).
Chi-squared results by ethnicity and fall status are depicted in Table 2. Individuals who identified as Hispanic or Latino reported the boot helped with their daily activities (ρ = −0.59, $p \leq 0.001$) and looked good (ρ = −0.57, $p \leq 0.001$). Individuals who identified as Hispanic or Latino, compared to those who did not, reported they liked using the boot ($$p \leq 0.05$$) and would like to use it in the future ($$p \leq 0.04$$). Individuals with fewer retrospective falls reported the boot’s design made them want to wear it longer (ρ = 0.65), they liked using it (ρ = 0.55), and would like to use it more in the future (ρ = 0.55) (all $p \leq 0.001$). Non-fallers, compared to fallers, reported the design of the boot made them want to wear it longer ($$p \leq 0.04$$) and it was easy to take on and off ($$p \leq 0.04$$).
## 4. Discussion
This study sought to examine user perspectives of irremovable, removable, and sensorized offloading boots (smart boot) to provide insight on ways to help promote adherence and gain insight about factors associated with the acceptance of a smart offloading device with a remote patient monitoring component. Correlation results suggest smart offloading may ultimately help promote adherence, since sensorized boot users were more inclined to report that learning how to use the boot was easy. Additionally, participants with lower cadence or deeper ulcers tended to report that the boot helped them follow physician instructions, regardless of group. Chi-squared results suggest that participants who identified as Hispanic or Latino, as well as those who had fewer or no retrospective falls, tended to rate their offloading boot more favorably regardless of group. These findings provide supporting evidence that older adults could find a sensorized offloading boot easy to use for DFU management. Further, people who do not identify as Hispanic or Latino, report falling in the past 12 months, or report less severe symptoms (e.g., higher cadence, shallower ulcer) may need additional targeted patient education to promote adherence.
Age, dropout from the study, group assignment, fear of falling, or cognition did not show significant associations with TAM ratings. Based on previous work that has indicated people prefer lower-profile walkers that are removable [12], we expected TAM ratings would differ by group assignment. However, participants only wore and rated the one walker they were assigned. Future research could examine preferences after using multiple walker types. Additionally, we expected cognition and age would be associated with perceptions on ease of use and the adoption of technology (e.g., impaired eyesight, dexterity, ability self-care) [26,27,28]. Future work could focus on examining if age or cognition influences perceptions of sensorized boot use or adherence.
In previous research, people with diabetes who identify as Hispanic or Latino have been shown to experience higher rates of foot ulcers and subsequent amputations, be more likely to develop chronic foot wounds despite receiving regular care, and be less likely to receive diabetic foot care and attempted limb salvage [29,30,31,32]. In this study, findings indicated that people who identified as Hispanic or Latino tended to report the offloading boots more favorably, regardless of group. This suggests the overall design of the boot, regardless of the modifications to the boot in each of the three groups, may help reduce ethnicity-related health disparities in DFU management. Higher cadence may also influence more favorable perceptions, since people who identified as Hispanic or Latino also had significantly higher cadence compared to those who did not identify as Hispanic or Latino. To help determine this, future work could examine open-ended perceptions of participants to determine what aspects of the boot they thought “looked good” or “helped with their daily activities” when rating those items favorably.
Participants who reported having fewer falls in the past 12 months (correlation results) or were non-fallers (Chi-squared results) tended to report more favorable perceptions, particularly regarding the design and ease of taking the device on and off. This appears to be aligned with prior work that found postural instability was a factor associated with low adherence to boot use [18]. However, no significant relationship of $p \leq 0.001$ was found between fear of falling and TAM ratings. This suggests that self-reported number of 12-month falls may be a better indicator of boot acceptability than fear of falling. Fallers also had significantly higher indications of cognitive function (better) and depression (worse) compared to non-fallers, which may have also influenced perceptions. More work is needed to directly examine these relationships.
This study had a limited sample size of 21 participants, and acceptability was determined by a single questionnaire. Our findings could help inform directions for a thematic analysis, which would provide more detailed user perceptions on specific factors to help promote adherence. While this study focused on patient factors, the WHO recommends four other dimensions of factors (social/economic, therapy-related, condition-related, and health-system related) that should also be considered [33]. For example, participant hygiene or exposure to physical therapy could also influence acceptability.
## 5. Conclusions
Overall, findings from this study suggest that smart offloading with a remote patient monitoring solution may help promote adherence among older adults to wear offloading boots prescribed for DFUs. The design of the particular walker that was used in this study, regardless of being irremovable or removable, was better accepted among people who identified as Hispanic or Latino. Further, findings suggest clinicians could provide additional patient education for people who report experiencing at least one fall over the previous 12 months, particularly in putting on and taking off the walker. Manufacturers could also consider designs that improve perceptions of stability and appearance of the walker. Ultimately, smart technology and considerations surrounding ethnicity and fall status may help improve adherence in older adults with DFUs who are prescribed offloading walkers.
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|
---
title: 'Self-Monitoring Diabetes-Related Foot Ulcers with the MyFootCare App: A Mixed
Methods Study'
authors:
- Bernd Ploderer
- Damien Clark
- Ross Brown
- Joel Harman
- Peter A. Lazzarini
- Jaap J. Van Netten
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10006972
doi: 10.3390/s23052547
license: CC BY 4.0
---
# Self-Monitoring Diabetes-Related Foot Ulcers with the MyFootCare App: A Mixed Methods Study
## Abstract
People with diabetes-related foot ulcers (DFUs) need to perform self-care consistently over many months to promote healing and to mitigate risks of hospitalisation and amputation. However, during that time, improvement in their DFU can be hard to detect. Hence, there is a need for an accessible method to self-monitor DFUs at home. We developed a new mobile phone app, “MyFootCare”, to self-monitor DFU healing progression from photos of the foot. The aim of this study is to evaluate the engagement and perceived value of MyFootCare for people with a plantar DFU over 3 months’ duration. Data are collected through app log data and semi-structured interviews (weeks 0, 3, and 12) and analysed through descriptive statistics and thematic analysis. Ten out of 12 participants perceive MyFootCare as valuable to monitor progress and to reflect on events that affected self-care, and seven participants see it as potentially valuable to enhance consultations. Three app engagement patterns emerge: continuous, temporary, and failed engagement. These patterns highlight enablers for self-monitoring (such as having MyFootCare installed on the participant’s phone) and barriers (such as usability issues and lack of healing progress). We conclude that while many people with DFUs perceive app-based self-monitoring as valuable, actual engagement can be achieved for some but not for all people because of various facilitators and barriers. Further research should target improving usability, accuracy and sharing with healthcare professionals and test clinical outcomes when using the app.
## 1. Introduction
Diabetes-related foot ulcers (DFUs) are a leading cause of morbidity, mortality, and healthcare cost burdens globally [1,2]. In Australia alone each day, 50,000 people live with a DFU, 1000 are in hospital, 12 have an amputation, and 4 die because of a DFU, at an estimated annual direct cost of AUD 1.6 billion [3,4]. Best-practice treatment of people with DFU requires multidisciplinary team treatment in specialised DFU clinics, with various clinicians working together typically (bi-)weekly over several months to provide effective clinical care to heal the DFU [5]. However, the majority of DFU care performed is by the patients themselves or their carers away from the clinic, as self-care. Recommended DFU self-care typically includes patients regularly changing wound dressings, checking their DFU for changes and infection, and wearing offloading devices to relieve pressure and protect the ulcer [5]. Such recommendations are typically implemented in consultation between patients, carers, and clinicians to fit within the personal circumstances of the patient.
Self-monitoring is a key component of self-care [6]. For people with DFUs, self-monitoring holds potential to offer awareness of DFU healing and the impact of daily behaviours and self-care. Self-monitoring can also provide information to recognise complications and mitigate risks such as hospitalisation and amputation. This is important because patients often have a limited understanding of DFUs and the significance of self-care on DFU healing [7]. Poor mobility combined with the location of their DFU (most often on the plantar surface of their foot) also means that patients can find it difficult to see, reach, and care for the DFU themselves [8]. Perhaps most importantly, patients need to be able to perform self-care consistently over many months of DFU treatment [9], and during that time DFU healing changes can be hard to detect on a daily basis, which can be demoralising [8]. Hence, experts recommend that there is a need for simple and accessible methods for patients and carers to monitor DFUs at home [10].
To address this need, we created a new mobile phone app, “MyFootCare”, for patients and carers to monitor DFUs in their own homes and to receive encouraging feedback. MyFootCare encourages people to use their smartphone camera to take a digital photo of their foot. It uses visual analytics to help patients extract and monitor DFU size from their foot photo to track their healing progress more objectively. Similar mobile apps for monitoring DFU healing have been developed for use in healthcare services [11,12], with clinicians stating that such an app could potentially aid people with diabetes in checking their feet at home [11]. A study based on an imagined app showed that DFU patients also see potential in using photos for DFU self-monitoring [13]. Several systems have been developed to monitor feet at home [12,14,15,16], but these systems were largely used by people at risk of developing DFU, who had their photos assessed remotely by healthcare professionals. Contrary to these, MyFootCare was designed for people who already have a DFU to help them see, monitor, and care for a plantar DFU.
The specific aims of this mixed methods, but predominantly qualitative, study were to investigate [1] the value of MyFootCare perceived by patients and [2] the enablers and barriers for engaging with MyFootCare in naturalistic conditions over extended periods of time.
## 2.1. MyFootCare Design
MyFootCare was created by the investigator team through a user-centred design process with patients to identify self-care challenges [8] and to trial app prototypes [17,18], which have been described in detail elsewhere. In brief, three workshops with 14 podiatrists were conducted to further develop design ideas, conduct usability tests, and to obtain their support for this study.
MyFootCare was implemented as a fully functional Android app, based on Java frameworks and OpenCV [19], a free real-time computer vision development library. To measure the DFU size from foot images, the app uses a morphological watershed algorithm [20] provided by OpenCV to segment the foot from the image background and then the ulcer from the foot. The app relies on the surface area of the foot as a scale to measure DFU size. This approach was taken for usability reasons, so that users did not need to provide a reference point (like a ruler or a sticker on the foot) when taking a foot photo. As a result, the ulcer size was not measured as an exact cm2 measurement but as a percentage of the ulcer size from the first foot photo taken. The mobile phone flash was used to evenly illuminate the foot and keep the background dark. The prototype was developed and evaluated on a Samsung Galaxy S8 mobile phone.
MyFootCare offers the following features:Progress graph: users can monitor DFU size over time through a graph on the home screen (Figure 1a). The graph starts at $100\%$ based on the DFU size from the first photo taken. A star on the graph visualises the goal to reach a $50\%$ reduction within 4 weeks (Figure 1b). Reaching this goal can predict complete wound healing over an extended 12-week period [21].Foot check: to monitor the DFU size, users need to have a photo taken of their foot by another person, such as a relative or carer (Figure 2). Next, users need to manually analyse the photo by drawing lines to assist the app in segmenting the ulcer from the foot and the background (Figure 3). Users can review the foot image to identify changes and complications (e.g., infection) and add notes for discussion with their podiatrist or general practitioner. Finally, users receive feedback on their ulcer size, quantified as a percentage of the ulcer size from the first foot check (Figure 3d). Feedback messages and badges are customised to provide encouraging feedback tailored to their progress (e.g., “You’ve Reached Your Goal!”; see Appendix A for all messages).Photo gallery: users can review all their foot checks (image, progress, and notes) and show them to healthcare professionals via their gallery (Figure 1c).Motivational image: the upper half of the home screen shows an image that visualises a goal that users wish to achieve when their DFU has healed (e.g., be able to walk the dog, Figure 1a). Users could choose from 10 different images or upload their own photo. This is important because setting a realistic goal based on something that people want to achieve (rather than avoid) is typically one of the first steps in establishing a self-care plan [22,23].Notifications: users receive reminders to take a foot selfie. The timing and message of reminders can be tailored to fit with the time of their dressing changes (Figure 1d).
## 2.2.1. Study Design
The study was designed as a mixed methods prospective 3-month cohort study, as this period is the typical DFU healing duration and recommended by international criteria for clinical studies [24]. The study was predominantly qualitative, based on semi-structured interviews. Quantitative measures were created from app log data and ratings of app features. Ethics approval was obtained from the Prince Charles Hospital (#HREC/18/QPCH/185), and the study was registered before commencement (ACTRN12618001276246).
## 2.2.2. Participant Recruitment
Eligible participants were people with a DFU on the plantar surface of the foot who were treated at a diabetic foot clinic, who owned a smartphone and who were assisted in their DFU management by a carer (e.g., a spouse, or home care nurse). DFUs were defined as a full thickness wound on the foot (i.e., below the malleoli) of a person with diagnosed type 1 or type 2 diabetes mellitus [1]. A smartphone was defined as an internet-enabled mobile phone and was a requirement so participants would have some familiarity with mobile applications.
Participants were recruited through three clinics in Brisbane, Australia. Eligible patient participants and their carers were invited to participate by their treating clinician. As a further incentive, participants were offered an AUD 50 voucher at the start of the study and another AUD 50 voucher if they completed the study. During the entire study period, participants continued to receive standard care from their clinic.
The recruitment target of 12 patients was based on our prior experience with reaching data saturation in similar studies. Prior research also shows that data saturation often occurs after 12 interviewees [25]. A total of 12 participants is also the most common sample size in human–computer interaction studies as it is seen to provide a balance between cost and return on significant insights on user engagement digital technology [26].
## 2.2.3. Data Collection
Demographics and DFU information were recorded by their podiatrist in the validated Queensland High Risk Foot Form clinical record [27].
Semi-structured interviews were conducted at weeks 0, 3, and 12 in person at the participant’s clinic by one of the investigators (DC, BP) based on an interview guide (Supplementary Materials). Interviews were audio-recorded and transcribed verbatim. Interactions with MyFootCare during the interviews were screen-recorded.
Before interview 1, the investigators used MyFootCare to take a high-quality foot photo during the participant’s consultation. This photo served as a baseline ($100\%$) for further foot checks.
Interview 1 (day 0, 60 min) focused on understanding the participant’s background and on introducing MyFootCare. First, we discussed the participant’s DFU and diabetes history, self-care practices at home and their impact on their everyday life, and their smartphone use. Second, we showed participants all features of the MyFootCare app and we used a think-aloud technique [28] to gain their initial impressions and questions about each app feature. Finally, participants rated the perceived usefulness of each app feature on a scale from 1 (not useful) to 10 (very useful) and explained their rating.
During interview 1, participants either received a smartphone (Samsung S8) for the study duration with MyFootCare installed on it, or we installed MyFootCare on their personal phones if the participants preferred and it was a similar phone model. Participants were asked to use MyFootCare each time they changed their wound dressing away from the clinic. They also received a stylus and a printed guide to assist with their foot checks.
After 10 days, participants received a phone call from one of the investigators to check if they had questions or needed technical assistance.
Interview 2 (week 3, 45 min) focused on understanding the participant’s initial app engagement. First, participants were asked to reflect on their progress. Second, participants were asked to open their MyFootCare app and show the investigators how they used the app and to raise any usability issues they may have experienced. Finally, participants rated the perceived usefulness of each app feature using the same scale as in interview 1, having now used the app for several weeks.
Interview 3 (week 12, 60 min) focused on understanding the participant’s ongoing app engagement and perceived value of MyFootCare for self-care. First, participants were asked to reflect on the progress of their DFU and self-care. Second, participants were asked to open their MyFootCare app and tell the investigators about their app data (graph and photo gallery), their experiences with taking photos and analysing them, and their reflections on factors that may have supported or impeded their progress and app use. Finally, participants rated the perceived usefulness of each app feature again and reflected on the value provided.
Quantitative data on MyFootCare engagement were automatically collected on the participant’s phone by the app. MyFootCare automatically logged each interaction with the app with timestamps in a log file and all foot images taken were stored on the phone. A second log file contained the time stamp and wound size for each foot check. Log files were exported at the end of interview 3.
## 2.2.4. Data Analysis
We analysed the data based on the principles of a reflexive thematic analysis approach [29,30]. In contrast to positivist thematic analysis, a reflexive approach highlights that themes are not discovered but generated by the researcher [30]. The researcher plays an active role in the thematic analysis, and their interpretation of the data and the themes generated are shaped by their background and by theoretical frameworks [29]. In this project, the analysis was led by the first two authors—a human–computer interaction researcher (BP) and a podiatrist (DC). The following frameworks guided our analysis:Self-care, which the WHO defines as “the ability of individuals, families, and communities to promote, maintain health, prevent disease, and to cope with illness with or without the support of a health-care provider” [31].Perceived value of patient-generated health data [32,33] for patients, which is defined as functional (to support health outcomes), emotional (understand and regulate emotions), social (share experience and support with peers), transactional (enrich consultations with health professionals), efficiency (eliminate unnecessary appointments), and self-determination value (empower patients).User engagement, which is defined as the process of how people start and continue to use technology for a certain purpose [34], which can be cyclical and include phases of disengagement and re-engagement [35]. Engagement is different from adherence, in that it is more dynamic and shaped by subjective factors such as a person’s goal, challenge, and experience with technology [36].
Our reflexive thematic analysis consisted of:Familiarisation: all interviews were transcribed verbatim by a professional transcription service. Immediately after each interview, we created notes with initial observations about the value and engagement with MyFootCare. Generating initial codes: coding started after completing the interviews. We created inductive codes both deductively (e.g., perceived functional value) and inductively to create more specific codes for our particular context (e.g., functional value from seeing the plantar side of the foot). The qualitative data analysis software NVivo was used to code all transcripts. Constructing themes: our research aims were used to construct themes that provide a coherent and insightful account of how patients engage and the value they perceive. Crosstab queries in NVivo allowed us to compare the frequency of codes between participants with different engagement levels. Reviewing and defining themes and producing the report: these three phases were intertwined and iterative. Themes were reviewed through regular meetings with all authors. Definitions were created and refined throughout the writing of the report and reviewed with all authors until consensus was reached.
All quantitative data were analysed using Microsoft Excel for Mac 16. Descriptive statistics used to display variables included frequencies (proportions), mean (standard deviation (SD)), and median (interquartile range (IQR)).
## 3.1. Participants
All participant characteristics are displayed in Table 1. In total, we recruited 12 participants; 9 ($75\%$) were male, median (IQR) age 53 (46–65) years, and 10 ($83\%$) had type 2 diabetes. Three ($25\%$) participants had MyFootCare installed on their personal Android phone and nine ($75\%$) received a Samsung S8 smartphone with MyFootCare installed for the study duration. All 12 participants completed the study with a third and final interview at week 12. Only P02 asked to conduct his final interview earlier at week 7 because of a health concern.
## 3.2. Value from Digital DFU Self-Care
Table 2 shows participants’ ratings of the perceived usefulness of MyFootCare at weeks 0, 3, and 12. The median (IQR) rating of the overall MyFootCare app was 10 [10, 10] (“very useful”) from a 10-point Likert scale at week 0, and this did not change at week 3 and week 12. Of the individual app features, participants also rated as very useful the ability to track progress through a graph (10 [8,10]), see wound photos in the gallery (10 [10,10]), and share data with health professionals (10 [3,10]) throughout the study, whereas participants rated the reminder notifications (8 [5,10]) and motivational image (6 [4,9]) features lower and these scores decreased throughout the study.
The qualitative data presented below provides the reasons behind the ratings and the value that participants perceived (based on [32]). Participants with limited app engagement sometimes rated the app’s potential rather than the value that materialised for them.
## 3.2.1. Functional Value: Monitoring Progress through Graph and Photo Gallery Features
Performing digital foot checks with MyFootCare provided clear value to DFU care. Ten participants reported that MyFootCare supported their health outcomes (functional value) because it allowed them to see the progress of their ulcer with their own eyes.
## 3.2.2. Functional Value: Reflection on Care and Health Decisions
Participants valued MyFootCare because it allowed them to reflect on events that affected their care and to aid with health decisions. During interviews, all participants used the graph to reflect on the reasons for changes in their wound size, for example, because they were more active on their feet. Five participants reported that reflection on MyFootCare data played a role in care decisions.
## 3.2.3. Transactional Value: Enhancing Consultations with Healthcare Professionals
The participants saw clear potential value in sharing MyFootCare data with a health professional to aid with clinical consultations. Seven participants reported that they intend to show MyFootCare to their health professional (GP, endocrinologist, and surgeon) to review the ulcer progress without having to take off and re-dress the bandage. These health professionals lacked equipment to re-dress their ulcers, and hence participants were reluctant to take off their dressing during consultations. Three participants also wished to share MyFootCare data electronically, e.g., by sending an email.
In practice, participants often did not share their photos because healthcare professionals appeared busy and did not prompt patients to show photos or because they were taking their own photos.
It is important to note that the value for consultations is not purely hypothetical because three participants shared foot photos with a healthcare professional during the trial study.
## 3.3. Engagement with MyFootCare
Figure 4 displays the log data over the 12-week duration of the study. Overall, participants used MyFootCare a mean (SD) 16 (11.5) times over the 12-week duration or 1.4 [1] times per week. Three different usage sub-groups emerged with four ($33\%$) participants “continually” using (28 (11.3) over 12 weeks; ≥20 digital foot checks overall), four ($33\%$) others “temporarily” using (16 (2.5) over 12 weeks; ≥10 and <19 foot checks), and the other four ($33\%$) “failing” to use MyFootCare after the first few weeks (5 (1.4) over 12 weeks; <10 digital foot checks).
Our qualitative findings seemed to confirm and further tease out the three distinct patterns identified in the quantitative findings. Enablers and barriers to engagement for each group are described below. The engagement level of each participant is indicated through a “C”, “T”, or “F”, e.g., “P03C” for continuous engagement of participant 3.
## 3.3.1. Enablers for Continuous Engagement
Participants who continuously engaged with MyFootCare were also dedicated to diabetes and DFU self-care. The following enablers allowed them to integrate MyFootCare with their self-care routines, although we noted some enablers were also identified by other participants.
MyFootCare installed on personal phone: all three participants who had MyFootCare installed on their personal phone were continuous users. This enabled them, as they had MyFootCare with them all the time. For example, P03C even took photos during her stay in hospital, which also meant she could show photos to the surgeon. It also meant that other features, such as reminder notifications and the motivational image, were more accessible.
Familiarity with foot selfies: six participants already had photos of their own foot ulcer on their personal phone. They were used to reviewing these photos and to reflecting on the reasons that promoted or inhibited healing. However, prior to using MyFootCare, foot photos were usually mixed in with all their personal photos and difficult to retrieve.
Dedicated caregivers who take high-quality photos: it took time for carers to learn how to take a good photo and to understand how this impacts the accuracy of the analysis. We saw that all four continuous users had caregivers who put in considerable effort to ensure they produced high-quality photos. For example:
## 3.3.2. Barriers Leading to Temporary Disengagement
Four participants engaged with MyFootCare for parts of the trial study (Figure 4). The following barriers were not directly related to MyFootCare, yet they were stated as the main reasons for pausing their engagement.
Work commitments: a main barrier was the tension between (paid and unpaid) work commitments and the need to care for and rest the foot. For example, one participant described the tension between having to work to support his family and needing to rest to heal the ulcer as a “Catch-22”.
Health disruptions: a second barrier for engagement were health disruptions. For example, one participant permanently disengaged from MyFootCare when he was hospitalised because of complications arising from cancer, followed by almost daily health appointments. The MyFootCare app was secondary to these needs during that time.
## 3.3.3. Barriers Leading to Failed Engagement
Four participants failed to engage with MyFootCare: they used the app occasionally at the start of the study but generally stopped using MyFootCare after a few weeks (Figure 4). These participants highlighted barriers related to their health, as well as barriers related to the usability of MyFootCare.
Frustration with lack of healing progress: three participants reported frustration with the lack of progress on their ulcer, which also affected their engagement with MyFootCare as they could not see any progress on the graph.
Lack of confidence using smartphones: during the interviews, we observed that the skills and confidence with which participants used smartphones varied considerably. Participants with lower confidence reported using their own phone primarily for communication and rarely for apps or to take photos. We observed that some participants navigated MyFootCare with difficulty because they were not used to the shape and operating system of the study phone, or they found it difficult to read information.
Frustration with having to re-do foot checks: having to retake the photo or repeat the analysis was a major source of frustration, which we also witnessed during some of our interviews when we analysed photos together with participants.
The main reason for having to redo foot checks were poorly taken photos. This was the case when photos were taken from above rather than parallel to the foot, when they did not show the whole foot, or when they contained background distractions like bright lights or the skin of the leg and arm (Figure 5a). A second reason was that the lines drawn during the analysis to separate the background, foot, and ulcer were poorly drawn. Instead of drawing around the foot and staying away from the edge, we observed how participants tried to cut as close to the edge as possible, similar to cutting out a picture. Some participants drew too many lines and thereby cut across edges, like when trying to colour in an area (see Figure 5b). Participants also struggled with the last step of the analysis, where they needed to tap the wound without touching the edge or outside of the wound. We provided a stylus to assist them with this step. However, images with small wounds or wounds close to the edge of the foot remained a challenge.
Lack of accuracy and reliability: inaccurate foot check results and limited reliability between different foot checks performed by the same participant were also a major barrier. This was largely a consequence of the limitations of MyFootCare (limited camera resolution and non-standardised photos) and of problems associated with the manual analysis process (when participants had enough of redoing foot checks and accepted inaccurate outlines of the foot and wound, as described above). In addition, occasional analysis errors made the graph difficult to read because these outliers changed the scale of the graph and minimised the actual progress (see Figure 5c).
## 4.1. Principal Results
The majority of participants perceived self-monitoring DFUs with MyFootCare as valuable for their self-care, in particular to see ulcer healing progress from the foot check feature (photos and graph). Similar to previous studies [14,15,16], we found that the majority of people perceived foot photos as valuable for self-monitoring at home. Additionally, MyFootCare provided a progress graph to provide objective DFU size information. While such a feature has been mentioned as potentially useful in previous studies by clinicians [11] and by patients imagining such an app [13], the current study presents experiences from patients actually using progress data in their daily lives. The results showed that most patients valued such data to verify subjective observations from the photos, even though they recognised that the accuracy of the progress graph was limited. Furthermore, the timeline of the graph encouraged many participants to reflect on actions and events that affected their self-care and healing progress. Such personal reflection is important because it can lead to patients feeling a higher degree of control in their health care, congruent with psychological empowerment [37].
Participants saw potential value in sharing MyFootCare data with healthcare professionals who typically do not treat or view the ulcer during consultations, such as their general practitioner. Thus, capturing photos can help patients to prepare for consultations and take on a more active role in their interactions with healthcare professionals, as found in other studies, e.g., by recalling information and health decisions [38,39]. Reviewing photos during consultation also benefits healthcare professionals because it prompts discussion about health experiences [40], adherence to treatment plans [41], and the broader lives of patients [42]. Unfortunately, such sharing rarely occurred in this study because participants perceived healthcare professionals as too busy and not interested in their observations. This observation also aligns with recent studies that show that patients rarely share their data unless they get asked by the clinician [43], and clinicians spend little to no time asking patients [44]. This seems a missed opportunity, and in Section 4.2, we recommend several practical implications that may improve this in future.
We identified and then explored three distinct engagement patterns with MyFootCare from participants: continuous, temporary, or failed engagement. Understanding these patterns is important because they reflect the reality of digital health interventions, where many systems are seen as valuable but have mixed uptake because of challenges with accessibility, privacy, and accuracy [33]. The patterns in this study were comparable to similar studies of digital systems for DFU self-care. An 8-week trial by Anthony et al. [ 16] showed that $77\%$ of users were willing to take regular foot photos once a week, which is similar to the $66\%$ (continuous or temporary) users in our study who took on average at least one photo per week. A 6-month trial of the “Foot Selfie” system [14] showed that $93\%$ of participants were imaging their feet at least every other day. However, in both studies, photos were taken primarily to screen the foot for ulcers. In contrast, the participants in our study used MyFootCare as part of their self-care routine when they changed wound dressings. They used MyFootCare more continuously when it was on their own phone, when they were familiar with foot selfies, and when they had a dedicated carer to take high-quality photos.
In examining the barriers to engagement, we found that participants took breaks from using MyFootCare when they experienced health disruptions, e.g., when they received care in hospitals. As predicted by technology engagement frameworks [34,35], we found that participants re-engaged when they were back at home.
Participants stopped using MyFootCare when they had their DFU for a long time and could not see any progress. Our findings align with related work with chronic disease patients [6], which suggests that their willingness to self-monitor is associated with the sense of control that they perceive over the disease. If patients feel that they cannot control health outcomes and do not expect to see any improvements, then they are less willing to self-monitor because the cost (in terms of time and effort required) is perceived to be higher than the anticipated benefits [6]. Participants also stopped using MyFootCare permanently when they did not feel confident using smartphones or when they experienced frustration from ongoing usability problems. These barriers could potentially be addressed by caregivers who are comfortable using smartphones and who can conduct the manual analysis (as well as take photos). We seek to address usability barriers in future work (see Section 4.3), but we do not expect that this would change the low engagement levels of long-time DFU patients with MyFootCare. These patients require a different approach [5].
## 4.2. Practical Implications
For healthcare professionals interested in engaging DFU patients more actively in their self-care through digital systems or foot photos, we suggest three recommendations. First, it seems important to identify suitable patients who are more likely to engage. For example, patients are more likely to engage if they have suitable smartphones and feel confident using them. DFU patients are often older adults [45], and diabetes can also negatively affect their vision [46] and their hand function and dexterity [47], which can all affect their use of smartphones. Patients require the support of a caregiver to take high-quality foot photos in their own home. Familiarity with having foot photos taken is also beneficial.
Second, digital self-care worked best for patients and carers who were already dedicated to self-care and who were looking for additional ways to promote DFU healing. MyFootCare did not motivate participants who had difficulties with adhering to other forms of self-care, such as dressing changes and offloading. Such patients need other forms of (behavioural) support [8].
Finally, healthcare professionals need to provide ongoing support to patients. Initially, they need to help them set up MyFootCare with the first foot check. Follow-ups are required to assist patients with usability issues, to actively inquire during their consultation into data collected, and to promote the integration of digital foot checks with existing dressing-change routines. Like others [48], we suggest that healthcare professionals can benefit from training, so that they can educate their patients on digital technologies and elicit patient data during consultations more effectively.
## 4.3. Limitations and Future Work
This study was based on 12 participants, which limited the validity of the quantitative results presented. Whilst quantitative information is provided in figures and tables, this information is descriptive only, and the main findings about the perceived value of MyFootCare, as well as the barriers and enablers for engagement, are based on qualitative data. Due to the small cohort and the 3-month period, we did not find an association between app engagement and ulcer healing; however, our methods were also not designed to detect such an association. If we would have found one, it could also have been by chance. A larger and primarily quantitative study is needed to investigate such an association. Instead, the strength of the current study was its high ecological validity through qualitative results about app engagement in real-world contexts over a 3-month period. This 3-month period was also sufficient to achieve our aims to evaluate engagement and app usage, as app engagement did not change after week 6.
This study identified several barriers that need to be addressed in future work. First, more work is required to simplify the foot check. Related work [16] suggests that patients benefit from having a selfie stick to take their own photos. We had tried selfie sticks in our usability tests but found them too difficult to use as they required long arms and flexibility. However, we see potential in a dedicated apparatus such as the “Foot Selfie” system [14], which consists of a phone holder and an apparatus to rest their foot on and allows patients to take photos on their own with minimal training.
Second, patients would benefit from simplifying the analysis to segment ulcer and foot in an image and to improve the accuracy and reliability. The current analysis requires a manual process to identify the wound patient, which can help to engage the patient but which also introduces subjectivity to the segmentation of the DFU. Even with clinicians, studies document that manual wound measurements involve a trade-off between accuracy and feasibility (time required and risk of contamination) [49]. To address these limitations, we see large potential in machine learning techniques, where recent studies provide reasonable results in segmenting DFUs in foot images [50,51]. We envision that analysis of foot images could be automated through a system that securely exchanges foot images taken on a patient’s phone with an internet-based analysis service. Furthermore, machine learning techniques also show potential to identify complications like infection and ischemia [52] in foot images, which could be used to alert patients to seek treatment.
Third, the current MyFootCare design is limited to tracking a single wound on the plantar surface of the foot. We focused on plantar wounds because these are the most common and are not visible to the patient [53]. However, participants with DFUs at the edge of the plantar surface reported difficulty with their analysis, as did participants with very small wounds. A different app design is needed to better support these patients.
Finally, MyFootCare did not allow remote monitoring by healthcare professionals. Patients can benefit from two-way communication with healthcare professionals to reduce the number of visits to the clinic [13]. When designing MyFootCare, we decided not to include electronic data sharing for a number of practical reasons: potential privacy risks, difficulty with diagnosing ulcers from images alone [54], and potential interference with the care provided by podiatrists. On a more fundamental level, we felt that remote monitoring by healthcare professionals would disempower the patients because it could increase their reliance on clinical care instead of empowering patients in their own care. Hence, in the spirit of participatory healthcare [55], we focused on how MyFootCare can provide patients with new insights for their own care, which they can share during consultations to give them a voice in their conversations with healthcare professionals.
## 5. Conclusions
For people whose DFUs are healing and who can access MyFootCare on their own phone, using an app for self-monitoring their DFU provides value through new health insights and through reflection on the events that promote or hinder progress. Successful engagement depends on various facilitators and barriers and can be achieved for some but not for all people. More work is needed to improve MyFootCare to address usability issues and to enhance its accuracy through standardised photos.
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|
---
title: 'App Design Features Important for Diabetes Self-management as Determined by
the Self-Determination Theory on Motivation: Content Analysis of Survey Responses
From Adults Requiring Insulin Therapy'
journal: JMIR Diabetes
year: 2023
pmcid: PMC10007004
doi: 10.2196/38592
license: CC BY 4.0
---
# App Design Features Important for Diabetes Self-management as Determined by the Self-Determination Theory on Motivation: Content Analysis of Survey Responses From Adults Requiring Insulin Therapy
## Abstract
### Background
Using a diabetes app can improve glycemic control; however, the use of diabetes apps is low, possibly due to design issues that affect patient motivation.
### Objective
This study aimed to describes how adults with diabetes requiring insulin perceive diabetes apps based on 3 key psychological needs (competence, autonomy, and connectivity) described by the Self-Determination Theory (SDT) on motivation.
### Methods
This was a qualitative analysis of data collected during a crossover randomized laboratory trial ($$n = 92$$) testing 2 diabetes apps. Data sources included [1] observations during app testing and [2] survey responses on desired app features. Guided by the SDT, coding categories included app functions that could address psychological needs for motivation in self-management: competence, autonomy, and connectivity.
### Results
Patients described design features that addressed needs for competence, autonomy, and connectivity. To promote competence, electronic data recording and analysis should help patients track and understand blood glucose (BG) results necessary for planning behavior changes. To promote autonomy, BG trend analysis should empower patients to set safe and practical personalized behavioral goals based on time and the day of the week. To promote connectivity, app email or messaging function could share data reports and communicate with others on self-management advice. Additional themes that emerged are the top general app designs to promote positive user experience: patient-friendly; automatic features of data upload; voice recognition to eliminate typing data; alert or reminder on self-management activities; and app interactivity of a sound, message, or emoji change in response to keeping or not keeping BG in the target range.
### Conclusions
The application of the SDT was useful in identifying motivational app designs that address the psychological needs of competence, autonomy, and connectivity. User-centered design concepts, such as being patient-friendly, differ from the SDT because patients need a positive user experience (ie, a technology need). Patients want engaging diabetes apps that go beyond data input and output. Apps should be easy to use, provide personalized analysis reports, be interactive to affirm positive behaviors, facilitate data sharing, and support patient-clinician communication.
## Background
Achieving treatment goals for patients with diabetes requires sustained behavioral lifestyle changes such as meal planning, monitoring carbohydrate (carb) intake and blood glucose (BG), and exercising. Diabetes apps can function as electronic care plans by helping patients plan and incorporate healthy behaviors into their daily routines [1]. The apps have been shown to lead to the improvement of glycemic control, with hemoglobin A1c (a blood test measuring average BG over the past 3 months) reduction typically in the range of $0.4\%$ to $1.9\%$ [2-7]. The most common app functions include the documentation of BG reading, diet, and medication use; BG analysis report; data export; and email capability [8]. Visual displays of BG readings help patients link this data to their behaviors, thus facilitating behavior changes to improve glycemic control [9]. Systematic reviews have found that the effectiveness of the apps increased with greater interactivity [10,11]. Interactive feedback could be an automated message from an app algorithm [5] (eg, “you have met your BG goal setting five times this week”), a text message from a dietician who reviewed data and customized a meal plan, [3] or an alert message whenever a BG reading is out of range compared to the goal [3,4,8,12].
Despite more than 1100 apps available on the market, their adoption and use vary, possibly due to design issues [13,14] and variations in technology development [15]. To date, only a few rigorous evaluation studies of app designs have involved patients [16], and most have evaluated the quality of all available apps in the market without involving end users such as patients and clinicians [17,18]. A recent systematic review showed that patient adoption of diabetes apps weighs heavily on patient perception of benefits, ease of use, and clinician recommendation to use diabetes apps [19]. Thus, the Agency for Healthcare Research and Quality stressed the need to understand the patient perspective on the use of diabetes apps [20]. Our research question focused on adults with type 1 or 2 diabetes on insulin therapy: What diabetes app functions are helpful as explained by a theory on motivation, called the Self-Determination Theory (SDT), to promote self-management behaviors? The purpose of this study, therefore, was to describe how patients with diabetes perceive diabetes apps to address the 3 psychological needs of competence, autonomy, and connectivity as described by the SDT [21]. Our analysis also allowed us to provide evidence that would refine this theory on motivation as it applies to the use of mobile apps in the population with diabetes requiring insulin.
## Theoretical Framework
Motivation is an important factor in user experience with technology [22,23]. The SDT [21] on motivation, as expanded by Szalma [24] for motivational design on effective human-technology interaction, guided this study. The SDT posits that people are driven to engage in behaviors because they believe those behaviors will personally benefit them [25]. According to the theory, humans have 3 basic psychological needs that influence behaviors [21]. Competence is the need to master tasks and learn skills [26]. Autonomy is the need to feel in control of one’s behaviors and goals [27]. Relatedness or connectivity is the need to feel attached to other persons [26,28]. The SDT has been used in educational, business, and health care settings [29-31]. It is used to explain the human-technology interaction [24]. Ryan et al [32] reported that the ease of technology use directly and positively affected the satisfaction of psychological needs. This theory thus provides the basis for this study as we organized participant responses according to the 3 psychological needs outlined in the theory.
## Design
This study was part of a crossover randomized laboratory trial [33] to test 2 top-rated, free commercial apps (OnTrack and mySugr), identified as the “the Best Diabetes Apps 2016” by Healthline [34]. The within-subject design helped control for patient characteristics because the same individual tested the 2 apps in random order. Quantitative measures of these diabetes apps’ usability, including user satisfaction, time, success, and accuracy rates, have been reported elsewhere [33]. The data for the analysis presented here include field notes of observations during app use, audio recordings taken during the tests, and participant responses to an electronic survey with open-ended questions that queried what app functions patients perceived as being the most useful and most important in supporting diabetes self-management.
## Ethics Approval
This study was approved by the University of Minnesota Institutional Review Board (MOD00001221).
## Participants
Using a flier posted on a bulletin board or on the web, 92 participants were recruited from the following venues: Facebook ($$n = 46$$); participant referrals ($$n = 8$$); Federally Qualified Health Center clinic ($$n = 7$$); university campus ($$n = 6$$); public housing ($$n = 6$$); Craigslist ($$n = 5$$); veteran’s clinic ($$n = 4$$); diabetes support groups ($$n = 3$$); and miscellaneous sites from a state fair, church, and library ($$n = 7$$). Inclusion criteria were [1] aged ≥18 years; [2] having type 1 or type 2 diabetes; [3] having used an Android phone for 6 months or longer; [4] having used insulin therapy for 6 months or longer; [5] adequate English proficiency; and [6] smartphone proficiency (ie, they used the device for more than phone calls, emails, texting, or taking pictures). Exclusion criteria were [1] inability to read or speak English and [2] prior use of the OnTrack or mySugr app or use of any diabetes app in the past 6 months. Individuals were screened for eligibility on the phone, and written informed consent was obtained prior to the start of each study session.
## Procedures
From July to November 2017, we conducted 92 sessions of in-person tests of the apps that lasted an average of 1 hour. The testing took place in a private meeting room inside a public library or building. Participants viewed a YouTube training video posted by each app developer. They then practiced using the apps by the following protocol: [1] enter a carb intake; [2] enter an exercise activity; [3] enter an insulin dose; [4] enter a BG reading; [5] locate a BG report for specific days of the week; [6] locate a BG report for each meal; and [7] email a BG report. Then, each participant tested the 2 apps in a randomized order to carry out the same tasks listed in the practice protocol. Each participant received a US $50 gift card upon study completion.
## Data Collection
The first author (HF) kept field notes detailing her observations of participant reactions during the test of the apps and audio recorded the tests. The field notes and audio recordings were transcribed verbatim in a Microsoft Word file by a research assistant. The survey was administered on an iPad (Apple Inc.) and included questions on demographic characteristics, technology use, and diabetes history. In addition, based on the SDT [21], the survey also included questions about motivation for self-management and psychological needs for competence, autonomy, and connectivity. Details of these measures are reported in prior publication [33]. To explore participant responses to the app, the survey queried participants about their perceptions of app usability and satisfaction, preferences for a “dream” app and indications of what function(s) would be the most useful, and identification of the most important functions in a diabetes app.
## Data Analysis
Field notes, audio recordings, and survey responses were analyzed based on key constructs from the SDT [21]. The analytic team, consisting of 4 members (HF, JFW, CJP-M, and TJA), analyzed the transcripts with the aid of Dedoose [35], a web-based, qualitative data analysis software. Directed content analysis, as described by Hsieh and Shannon [36], was used. With this approach, an existing theoretical framework (SDT) was used to organize data according to predetermined categories that are aligned with key constructs in the theory: competence, connectivity, and autonomy. Data that failed to contribute to the categories were coded and used to suggest modifications or extensions of the theory. A codebook was developed based on the initial reading and updated with independent coding from an analysis team. The team reached consensus on the code definition that were clear and mutually exclusive (see Table 1 for conceptual and operation definitions for codes used).
Competence was conceptually defined as app features to help patients gain skills to keep BG in the target range [24]. Competence was operationalized as app functions to help patients understand the meaning of their data. This refers to how the app records data, analyzes data, and provides reports on which numbers are not in the target range and why. Autonomy was conceptually defined as app features that help patients set safe goals on diet, insulin dose, or activity level based on personal trends of BG and carb intake [24]. Autonomy was operationalized as app data visualization to help patients identify abnormal highs or lows, which are important for setting up reasonable targets to change behaviors associated with those abnormal readings. Connectivity was conceptually defined as app features to facilitate interactions between persons and the technology involved, which means enabling the sharing of home-monitored data and communicating with clinicians [24]. Connectivity was operationalized as app print report options, exports of data and analysis reports, and reports sent to clinicians or others through email.
Analysis occurred in several steps consistent with content analysis procedures as described by Miles et al [37]. First, based on the SDT [21], the team reviewed the conceptual definitions of the 3 main categories (eg, competence, autonomy, and connectivity) and, through discussion and consensus, developed operational definitions of each that were clear and mutually exclusive. See Table 1 for the conceptual and operational definitions of each of the categories. Second, a codebook was developed that outlined rules for coding data to each of the categories. The codebook was refined through several iterations of coding. Third, a table was developed that included each of the 3 categories as column headings and a column heading labeled “other” for codes that did not align with any of the categories. Data from each participant were placed on a row that was identified with the participant’s ID number. Fourth, all data were read by all team members and divided into text units (eg, coherent phrases or sentences relevant to the study purpose). The text units were coded with a label that captured the essence and, based on the coding rules, placed in the appropriate cells on the table. Fifth, the analytic team met to gather similar codes from each column into subcategories through a process of discussion and consensus. The subcategories in the 3 main columns (ie, competence, autonomy, connectivity) were described.
The team used several procedures to enhance the trustworthiness of the study findings based on criteria outlined by Lincoln and Guba [38]. First, participants were carefully chosen based on comprehensive inclusion criteria that ensured they had sufficient backgrounds to fully engage with the app testing. Second, expert consensus was achieved with a 4-member research team experienced in diabetes self-management, the SDT [21], and app use, working together to reach consensus in the interpretation and grounding of the theory of the SDT. Third, transferability was enhanced with detailed descriptions of the study population and context. Fourth, auditability was ensured with a detailed audit trial maintained in the Dedoose software chronicling all analytic decisions of the study. Finally, research bias was addressed through frequent team discussions that encouraged researcher reflexivity.
**Table 1**
| Conceptual definition and code | Conceptual definition and code.1 | Conceptual definition and code.2 | Operational definition |
| --- | --- | --- | --- |
| Help gain skill to keep BGa in-target-range to promote competence | Help gain skill to keep BGa in-target-range to promote competence | Help gain skill to keep BGa in-target-range to promote competence | Help gain skill to keep BGa in-target-range to promote competence |
| | Carbb counting | App feature to have carb counting help, search a food database, link carb content, and planned how much carb to eat | App feature to have carb counting help, search a food database, link carb content, and planned how much carb to eat |
| | Help planning | App use to plan meal or plan behavior change in diet, meds, activity, or lifestyles as well as medication and diabetes supply due for refill. - planning action - different from alert/ reminder that is reminding a behavior | App use to plan meal or plan behavior change in diet, meds, activity, or lifestyles as well as medication and diabetes supply due for refill. - planning action - different from alert/ reminder that is reminding a behavior |
| | Monitor or track BG, carb intake, physical activities, medication use, and others | App use to monitor, track, record, or log BG, BG testing frequency, carb, activity, medication use, mood, emotional status, stress, or painThe convenience of recording data on the go or app with built in glucometer function to test and record | App use to monitor, track, record, or log BG, BG testing frequency, carb, activity, medication use, mood, emotional status, stress, or painThe convenience of recording data on the go or app with built in glucometer function to test and record |
| | Report summary | Report or records to help understand home-monitored data as a benefit for app use, including BG averages and hemoglobin A1c statistics | Report or records to help understand home-monitored data as a benefit for app use, including BG averages and hemoglobin A1c statistics |
| | See BG out-of-range | App analysis of BG in-target-range and out-of-range | App analysis of BG in-target-range and out-of-range |
| Set safe and practical short- and long-term goals to promote autonomy | Set safe and practical short- and long-term goals to promote autonomy | Set safe and practical short- and long-term goals to promote autonomy | Set safe and practical short- and long-term goals to promote autonomy |
| | Trends of frequent high or low BG | Data analysis to see the trends and pattern of BG including consistency of the changes (fluctuation) | Data analysis to see the trends and pattern of BG including consistency of the changes (fluctuation) |
| | BG or carbs trends by time | Able to see data BG or carb in relation to time of the day | Able to see data BG or carb in relation to time of the day |
| | BG or carbs trends by days or months | Able to see BG or carb in relation days of the week, or one week - a specific format to see which day of the weekAble to see BG or carb with a monthly average to give a grand overview | Able to see BG or carb in relation days of the week, or one week - a specific format to see which day of the weekAble to see BG or carb with a monthly average to give a grand overview |
| Facilitate supportive interaction between persons and technology involved to promote connectivity | Facilitate supportive interaction between persons and technology involved to promote connectivity | Facilitate supportive interaction between persons and technology involved to promote connectivity | Facilitate supportive interaction between persons and technology involved to promote connectivity |
| | Share data or reports to get feedback from clinicians on home-monitored data | Enable data upload, export, or email to send data or reports to cliniciansPrint reports to bring to clinic visit with clinicians | Enable data upload, export, or email to send data or reports to cliniciansPrint reports to bring to clinic visit with clinicians |
| | Support from other | Sharing with app reports with family, friend, or other non-clinician involved in their diabetes care | Sharing with app reports with family, friend, or other non-clinician involved in their diabetes care |
| General app design to promote positive user experience | General app design to promote positive user experience | General app design to promote positive user experience | General app design to promote positive user experience |
| | Automatic | Automatic upload data which includes sync with glucose meter, insulin pump, continuous glucose monitoring, or another medical device | Automatic upload data which includes sync with glucose meter, insulin pump, continuous glucose monitoring, or another medical device |
| | Alert or reminders | App feature to set up alarm or reminder alert for BG testing, exercise, diet change, etc. | App feature to set up alarm or reminder alert for BG testing, exercise, diet change, etc. |
| | Color | Color as an important design element | Color as an important design element |
| | Cost | Financial expense to use the app | Financial expense to use the app |
| | Icon, emoji, button | Design element for app screen or app functions | Design element for app screen or app functions |
| | Interactivity | Interactive feedback or response such as a sound | Interactive feedback or response such as a sound |
| | Patient-friendly | Easy to useSimple and understandable terms/icons | Easy to useSimple and understandable terms/icons |
| | Tutorial or self-help | Tutorial, help function, or resource to help users learn to use the app | Tutorial, help function, or resource to help users learn to use the app |
| | Voice over | Respond to voice, eliminate typing or taping of icon | Respond to voice, eliminate typing or taping of icon |
## Sample Characteristics
In all, 92 persons participated in the study. Their mean age was 54 (range 19-79) years. The majority were female ($\frac{54}{92}$, $59\%$), White ($\frac{57}{92}$, $62\%$), and college educated ($\frac{61}{92}$, $66\%$; Table 2).
Most ($\frac{64}{92}$, $70\%$) participants had type 2 diabetes and had used insulin for an average of 12 (SD 12) years. The participants reported a wide variety of diabetes complications including short-term memory loss; retinopathy; mobility impairment with the use of a cane, walker, or wheelchair; hemiparesis related to stroke; hand tremor; and peripheral neuropathy affecting hand dexterity. The majority ($\frac{57}{92}$, $62\%$) were comfortable or very comfortable using a smartphone. Additionally, 60 participants reported whether they were working ($$n = 35$$) or not working ($$n = 25$$)—student ($$n = 3$$), retired ($$n = 13$$), homeless ($$n = 2$$), and disabled ($$n = 7$$). Participants reported the most important app functions related to promoting competence as described by the SDT; on the other hand, what they reported as dream app functions were general app designs unrelated to the SDT (Figure 1). Of the 436 text units that were highlighted, 292 ($67\%$) were coded to 1 of the 3 categories of needs based on the SDT [21]: competence ($$n = 212$$, $48.6\%$), autonomy ($$n = 47$$, $10.8\%$), and connectivity ($$n = 33$$, $7.6\%$). The remaining 144 ($33\%$) text units were not aligned with any of the 3 categories. The categories are discussed below.
## Competence
Participants found that the apps could improve their sense of competence by helping them monitor data (ranked 1st), create analysis reports (ranked 2nd), gain knowledge about reasons for out-of-range BG (ranked 4th), and plan behavior changes in self-management activities (ranked 5th), including counting carbs linked to a food library (ranked 7th; see Table 3).
Some appreciated receiving information that guided them in adjusting their insulin doses. One participant stated, “It helps me know my high and low blood sugar reading so I can adjust insulin dose. If it is real high in the morning, then at night I take more insulin. Now I do trial and error. My way is not the best.” Participants liked the automatic carb counting function. One said, “[You] take a picture [and let it] analyze for you and tell you how many carbs and everything it is.”
**Table 3**
| Motivational design and app design features | Motivational design and app design features.1 | Rank (ranged from 1-15) | Frequency (N=436), n (%) | Quotes |
| --- | --- | --- | --- | --- |
| Help gain skill to keep BGa in-target-range to promote competence | Help gain skill to keep BGa in-target-range to promote competence | Help gain skill to keep BGa in-target-range to promote competence | Help gain skill to keep BGa in-target-range to promote competence | Help gain skill to keep BGa in-target-range to promote competence |
| | Help record, monitor, or track BG, carbb intake, physical activities, medication use, and others conveniently on a smartphone | 1 | 69 (16) | “Ability to track sugar and foods without relying on memory”“Ability to enter as much information regarding the event (meal, exercise, etc.) as I possibly can. If I’ve exercised prior to meal or if I am sick, I want to be able to note that along with the medication or meal entry. -- tagging information to an event” |
| | See a report with convenient view | 2 | 49 (11) | “Tracking my glucose readings, having at-a-glance reports and comparisons”“See blood sugar report and diet report in the apps - that way helps you maintaining your diabetes and keeping it in control” |
| | See out-of-range BG and explanations for abnormal readings | 4 | 40 (9) | “The app should let you know that you are doing good or bad in any given time”“BG report when high, you can tap on it - lead you to see what you eat made it high.” |
| | Plan changes in diet, exercise, BG testing, and medication use | 5c | 32 (7) | “Telling me how much insulin to use with what food and exercise”“Fix your not normal readings of BG before going to see doctor” |
| | Carb count and provide a food library | 7 | 22 (5) | “Adding carbs and being able to find food items with. The carbs planned out” |
| Set safe and practical short- and long-term goals to promote autonomy | Set safe and practical short- and long-term goals to promote autonomy | Set safe and practical short- and long-term goals to promote autonomy | Set safe and practical short- and long-term goals to promote autonomy | Set safe and practical short- and long-term goals to promote autonomy |
| | Trends of frequent high or low BG | 9d | 17 (4) | “Tracks your diabetes - system going up and down”“Blood glucose Trends on the home page” |
| | BG or carbs trends by time | 9d | 17 (4) | “Tell you when your blood sugar had a big jump”“Recording all records of bs testing, tracking foods eaten around those reading times” |
| | BG or carbs trends by days or months | 10 | 13 (3) | “Ability to easily see patterns throughout the day over a period of the past 30 days”“Glucose levels compare to other hours and days. Want to know if this week, if any meal BG readings are in range.” |
| Facilitate supportive interaction between persons and technology involved to promote connectivity | Facilitate supportive interaction between persons and technology involved to promote connectivity | Facilitate supportive interaction between persons and technology involved to promote connectivity | Facilitate supportive interaction between persons and technology involved to promote connectivity | Facilitate supportive interaction between persons and technology involved to promote connectivity |
| | Quicker feedback from clinician | 6 | 29 (7) | “Let my doctor know instead of waiting 3 months, and doctor tell me what to do to improve my diabetes”“Able to send report to doctor or print at home a paper copy to bring to an appointment” |
| | Support from other | 14 | 4 (1) | “Talk with loved one [about their] data”“Within the app – meet each other weekly, get together, video, message, phone call, more secure too” |
App functions help patients to record and understand data and plan behaviors as skill to keep BG in the target range. First, the convenience to track electronically whether BG is in the target range (80-130 mg/dL before eating and <180 mg/dL after eating) [39] is highly valued [40]. This is consistent with patient surveys that found diabetes apps are important for BG monitoring [41]. Understandable “Glucose Diary View” is the most practical [42]. Abnormal BG readings should be color-coded [39] and summarized into a 1-page standardized report [43]. An electronic report can increase patient knowledge to plan behavior changes such as eating right (making it easier to count carbs and plan meals) and calculating short-acting insulin dose to lower elevated BG readings due to excessive carb intake. These features are all valuable to patients because they help them to gain insight and understanding about abnormal BG readings so they can achieve competence in diabetes care, which is consistent with a study on the requirements of diabetes apps for underserved patients [44].
Carb counting is a commonly desired app function, where a smartphone takes a picture of the food; analyzes the portion size, carb content, and corresponding insulin dose; and suggests a time for insulin administration. This finding broadly supports app use to improve adherence of medical nutrition therapy [2-4,45]. Currently, many diabetes apps have low-carb diet recipes, multidevice integration, and automatic features, but the cost can be expensive. For example, Glucose Buddy Premium has a subscription cost ranging from US $19.99 to US $59.99 per month to access the full food database [46,47]. Future research should be undertaken to investigate ways to offset the cost of app technology such as subsidizing the expense while the health system could bill insurance for remote patient monitoring, given that the Centers for Medicare and Medicaid Services can reimburse the transmission of home-monitored data and summary report by clinic staff [48]. Offering analysis tool to count carbs and calculate insulin dose is a form of “virtual dietician.” *Research is* in progress to develop and test apps that leverage machine learning to perform image recognition and automate recommendations of behavior change [49].
## Autonomy
Participants found that the apps improved their sense of autonomy. They felt more self-sufficient because the apps showed if their BG was trending high or low in relation to the time (ranked 9th) and in relation to the day of the week (ranked 10th). Being provided with a data visualization of these personal patterns increased their sense of empowerment and assisted them in identifying short- and long-term goals for changing behaviors. One participant explained, “a function that easily helps me find when I most commonly have hypoglycemia.” Information provided by the apps aided their decision-making regarding how and when to change behaviors to keep BG in the target range. This could be done with data visualization; one participant stated the benefit to see “how my trends are changing.” App functions of trend analysis help set safe and practical short- and long-term goals by time, day of the week, and month, which aids personalizing options to change. Participants reported the need to visualize the trends or patterns of frequent high or low BG (ie, what) by day of the week and time (ie, when). This finding is consistent with prior research showing that diabetes apps helped patients identify and incorporate healthy behaviors into their daily routine [1]. Seeing demarcations of BG changes between months, weeks, days, and time of the day is very important to show patients when dangerous BG levels occur and to set reasonable goals to change behaviors [50]. Goal or target setting helps patients plan behaviors and provides a warning when they are outside the target [51,52]. Personalizing options should include tracking mental health factors such as mood, stress, and illness, because these factors are associated with hyperglycemia and poor glycemic control. Effective self-management is important economically, since many adults diagnosed with diabetes are not able to maintain work. They exit the work force earlier ($30\%$ higher) compared to those without diabetes [53].
## Connectivity
Participants found the apps enhanced a sense of connectivity because the clinicians could receive emails or print reports on home-monitored data to better understand patients’ self-management behaviors (ranked 6th). One participant said, “An app that can send my numbers directly to [the doctor] if there is a concern [about frequent] lows or highs.” Participants also felt connected because of the bidirectional messaging functions of the apps. These functions supported monitoring of BG, and readings could be compared to hemoglobin A1c laboratory readings in the clinic. Connectivity was also enhanced by informal coaching support from others (ranked 14th). One patient stated, “help people share what other people not understanding. [ 1] report, [2] sharing - support for other patients with diabetes.” App functions can facilitate supportive interaction by sharing data or app reports with clinicians and “loved ones” to gain support for behavior change. This is consistent with several studies that showed data sharing or showing data from the mobile devices with their clinicians during a medical visit is highly valuable for patients [50,54,55]. Greater app interactivity with a clinician appears to improve glycemic control [11,56]. A simple explanation for this finding may be that successful diabetes self-management takes teamwork [54,55]. Informal coaching support by other people or even a virtual coach in an app is valuable. Artificial intelligence could provide confirmation of positive behavior change, such as reaching a BG value in the target range, to provide immediate feedback to patients. A trial of an artificial intelligence virtual coach with 187 adults with type 2 diabetes, unfortunately, did not demonstrate a difference in changing hemoglobin A1c but did improve health-related quality of life [57]. Very few long-term studies of diabetes apps have been conducted [58]. However, due to the COVID-19 pandemic, telehealth visits had an unprecedented increase in use from $0.3\%$ in 2019 to $29.1\%$ in 2020 among a 2019 cohort ($$n = 1$$,357,029) versus a 2020 cohort ($$n = 1$$,364,522) [59]. Leading companies in web-based diabetes care—Livongo, One Drop, mySugr, Cecelia Health, Steady Health, and Virta Health—noted a rise in subscribers during the pandemic [60]. Future studies using the mobile health platform for telehealth, including a diabetes app, should be undertaken.
## Top General App Design
Most participants reported the necessity for a diabetes app to save time regardless of functions. They described that the app needs to be efficient and “easy,” requiring minimal user effort. They desired the app to use patient-friendly terminology and display easy-to-understand reports (ranked 3rd; see Table 4).
Automatic features (ranked 5th, same as to plan behavior change) is the integration between devices so that their data are interoperable. One participant explained, “*Have this* app be able to read my pump and. An app I reason I don’t use app, having an orange and apple that they don’t talk to each other. An app that easy and talk to my pump.” Voice recognition (ranked 8th) is the elimination of typing text, which was best described by one participant: “speaking function to record all data.” App alerts (ranked 9th) are helpful to remind users to do activities such as retest BG and repeat insulin for elevated BG after eating a meal. App interactivity (ranked 11th) is giving behavior confirmation as one participant explained: “You did it, completed 1 entry.” Other app designs (ranked from 12th to 15th)—color; cost; icon, emoji, or button options; tutorial or self-help; and fun, technical support, and link to pharmacy—were of interests to participants.
**Table 4**
| App design features | Rank (ranged from 1-15) | Frequency (N=436), n (%) | Quotes |
| --- | --- | --- | --- |
| Patient-friendly | 3 | 43 (10) | “To put language that patients could understand - small words - for example blood sugar instead of glucose.”“I like the pick and choose option but maybe more screens so there's less congestion. (Less busy screen) simple screen shot that leads to new screens. Don't like scrolling.”“Easy to read and understand the report and information you put in it - make numbers bigger” |
| Automatic: integration of devices plus easy view of data | 5a | 32 (7) | “Pump, and meter integration that also downloads my CGM readings to form a graph with minimal interaction from me.”“A graph to be able to connect with my meter” |
| Voice recognition | 8 | 19 (4) | “Voice command to record my BG reading and carb intake”“App talks to me that my blood sugar is too high or too low” |
| Set up alert or reminders | 9b | 17 (4) | “Track carb, when went over the amount, it alarms you to don't eat any more carb.”“Reminder for to check your blood and make sure exercise (tell you exercise, a schedule) - like to tell you to go a walk at what time” |
| Interactivity | 11 | 11 (3) | “For the app to show me the cravings for the carb, to motivate you not to eat the carb, when I eat carb, the app should go off”“Interactive apps. I really like when ‘slimy’ congratulated me or said it happens, when my sugars were not good.” |
| Color | 12 | 7 (2) | “Color to differentiate functions.”“Tap in red color to give your time and more detail.” |
| Cost | 13 | 5 (1) | “Don’t have to buy a meter for it.”“Willing to pay for the app if it works” |
| Icon, emoji, button | 14 | 4 (1) | “More icon per se where a picture would be used instead.”“The activity (have emoji) hit emoji when you start jogging and hit emoji again to stop.” |
| Tutorial or self-help | 15 | 3 (1) | “Help function - no paragraph, video to see how to use this function.”“Help function to help you use the app (like to email in the app).” |
| Miscellaneous: fun, link to pharmacy, technology support | 15 | 3 (1) | Link to pharmacy order within the app and “your pharmacy deliver to you.”“For people to have a hot line, get stuck to get help technical support, a live person to help with the app. If I did not go back to last app that she showed him how to send and get gmail to send report.” |
## Principal Findings
The aim of the research question and purpose of the study was to investigate how adults with diabetes requiring insulin therapy perceive diabetes apps based on the 3 key psychological needs described by the SDT [21]: competence, autonomy, and connectivity. Our findings provide evidence on the usefulness of the SDT in mobile health technology and describe specific app functions that address psychological needs. The results are consistent with Szalma’s [24] description of a theoretical model of motivational design based on the extension of the SDT. Newly identified categories about general app design did not fit with the SDT’s psychological needs, but they addressed the technology needs for patients to use an app with minimal effort.
## Top General App Functions or Features
Themes unsupportive of the SDT emerged that focused on the acceptability of general app design features. These themes did not support the SDT, but they described patients’ technology needs. The theme of being patient-friendly is highly relevant for user-centered app design. A patient-friendly app implies a match between the app and the patient’s real world [61,62], and icons and wording need to speak the users’ languages and concepts. For example, “blood sugar” is preferred to “blood glucose.” Eliminating medical jargon would decrease barriers and make it easy for patients to understand knowledge gained from using apps [50]. Automatic features to integrate devices that test BG and upload results into apps ranked in the top 5, which is consistent with a survey study among patients with type 1 diabetes, $91.6\%$ of whom agreed that it is the most important function ($$n = 167$$) [51]. Voice recognition decreases the user’s need to type data. Alert notifications can remind patients who are on multiple insulin injections and need frequent BG testing (>4 times a day). Patients desired app alerts to remind them of behavior (eg, repeat BG testing) [63]. An interactive app is about giving the patient a response to promote user interaction, not just data in and data out. A change in emoji, an app message of “good job,” or a sound are ways of interaction between the user and the technology. Color can help customize user experience. An app tutorial or technology support is an important resource to increase user confidence to interact with the app. Overall, these themes around acceptable design features are important for patient engagement to promote a positive user experience and boost patient confidence to use the technology.
## Limitations
Three major limitations in this study were [1] the laboratory setting, [2] only 2 top-rated, commercially free apps being tested, and [3] the urban population. The first weakness is that participants only used the apps once in a research visit rather than in their home setting with real data. It is possible that using the apps in the home setting would have changed participants’ opinions about the desired app features. Future work is required to establish the viability of actual app use at home and in other settings (eg, use an app for 2 weeks and attend focus groups to discuss the facilitators and barriers of app use). A second weakness is testing only 2 top-rated free apps, which may not be representative of the diabetes apps on the market. However, mySugr has remained in Healthline’s 2022 list of best diabetes apps [45], and OnTrack has been recommended by educators from the American Diabetes Association [46] and the University of Michigan [47]. Apps requiring payment were not included in this study. Payment for increased functionality may increase patient engagement and potentially create bias to use the app to get a return on the investment [64]. A third weakness is that the results may not be applicable to a rural population who may have no or inadequate internet service. App responsiveness may depend on the type of internet connection. Notwithstanding these limitations, this study offers valuable insight to addressing behavior needs for self-management by adults with diabetes requiring insulin therapy. Several strengths of this study include the diverse sample of racial or ethnic minority participants and a variety of diabetes complications, which increase study generalizability. Additionally, this study had a sample of 92 participants, which is much larger than most usability study sample of 30 participants.
## Conclusions
The SDT helped to explain patient perspectives on the roles of diabetes apps as an electronic tool to address their psychological needs of competence, autonomy, and connectivity in diabetes care. Our findings also validated that the 3 concepts of the SDT guided the initial coding, further analysis, and development of operational definitions. Using an app can promote competence in keeping BG in the target range through electronic monitoring of BG, creating analysis reports, and gaining knowledge about reasons for out-of-range BG to plan behavior. The app can promote autonomy to set safe and practical BG goals by showing trends of high and low readings in relation to time, day of the week, and months. An app can promote connectivity by printing reports for clinic visits or emailing reports to a clinician, thereby helping patients receive feedback from clinicians. Patient technology needs, such as being patient-friendly and requiring minimal user effort, are also important. Continued efforts are needed to understand long-term adoption of diabetes apps to support self-management by patients, as well as the integration of diabetes apps in the telehealth setting for clinicians.
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|
---
title: 'Recommendations for the Quality Management of Patient-Generated Health Data
in Remote Patient Monitoring: Mixed Methods Study'
journal: JMIR mHealth and uHealth
year: 2023
pmcid: PMC10007009
doi: 10.2196/35917
license: CC BY 4.0
---
# Recommendations for the Quality Management of Patient-Generated Health Data in Remote Patient Monitoring: Mixed Methods Study
## Abstract
### Background
Patient-generated health data (PGHD) collected from innovative wearables are enabling health care to shift to outside clinical settings through remote patient monitoring (RPM) initiatives. However, PGHD are collected continuously under the patient’s responsibility in rapidly changing circumstances during the patient’s daily life. This poses risks to the quality of PGHD and, in turn, reduces their trustworthiness and fitness for use in clinical practice.
### Objective
Using a sociotechnical health informatics lens, we developed a data quality management (DQM) guideline for PGHD captured from wearable devices used in RPM with the objective of investigating how DQM principles can be applied to ensure that PGHD can reliably inform clinical decision-making in RPM.
### Methods
First, clinicians, health information specialists, and MedTech industry representatives with experience in RPM were interviewed to identify DQM challenges. Second, these stakeholder groups were joined by patient representatives in a workshop to co-design potential solutions to meet the expectations of all the stakeholders. Third, the findings, along with the literature and policy review results, were interpreted to construct a guideline. Finally, we validated the guideline through a Delphi survey of international health informatics and health information management experts.
### Results
The guideline constructed in this study comprised 19 recommendations across 7 aspects of DQM. It explicitly addressed the needs of patients and clinicians but implied that there must be collaboration among all stakeholders to meet these needs.
### Conclusions
The increasing proliferation of PGHD from wearables in RPM requires a systematic approach to DQM so that these data can be reliably used in clinical care. The developed guideline is an important next step toward safe RPM.
## Remote Patient Monitoring
The use of remote patient monitoring (RPM) solutions and production of patient-generated health data (PGHD) to enable continuous monitoring of patients outside clinical settings are increasing with the growing availability of health wearable devices and the connected mobile apps and web portals [1]. The COVID-19 pandemic has accelerated the use of RPM to monitor mild cases of the disease remotely, given the limited capacity of acute care facilities [2].
As the pandemic is not yet over, RPM will likely contribute more to health care delivery owing to the availability of various affordable technologies and the need for remote treatment and monitoring. However, despite the urgent need and rapid implementation and use of RPM, investigation on how quality PGHD can best be collected and managed to lead to accurate decision-making is still lacking.
## Ensuring the Quality of PGHD
Patients may collect some data as instructed by clinicians, mainly from medical wearables. Patients may also collect data, on their own accord or on advice from clinicians, from consumer wearables. There are fundamental similarities between the data collected upon patient initiation and those collected upon clinician initiation, whether from consumer or medical wearables, that erode the regulators’ distinctions: the data are generated outside the controlled environment of the clinic; the data collection is the responsibility of the individual wearer; and the data are shared electronically with parties who operate outside a controlled clinical setting, namely wearable companies. Thus, RPM data collected from wearables, whether upon patient initiation or upon clinician initiation, are covered by the broad concept of PGHD.
Outside the clinic, consumer and medical wearable technologies used in RPM capture a large amount of data continuously in rapidly changing circumstances during a patient’s daily life under the patient’s or caregiver’s supervision [3]. The wearable platform includes sensors that capture data automatically and a mobile app and web portal where the person enters data manually. Inside the clinic, RPM solutions are not integrated well into patient records or clinical workflows, and various digital health devices and platforms are used for different RPM purposes [4]. The quality of PGHD collected from disparate devices is compromised by various technical, behavioral, or operational issues that occur during data capture by the patient or caregiver, during the transmission of the data from the patient to the clinician, and during the clinician’s review of the data for decision-making [5].
Health data quality plays a vital role in health care systems. Clinicians need to trust the available data to make accurate decisions and provide efficient and timely care for their patients. Data are of good quality when they are fit for their intended use [6], that is, when they are accurate, accessible, consistent, complete, interpretable, timely, relevant, and compliant with the standards defined by health care organizations [7]. Any quality issue with data can affect patient safety, the reimbursement of health services, and the quality of clinical outcomes and other aspects of health care delivery [8].
Data quality management (DQM) refers to the processes of ensuring data quality when data are collected, stored, analyzed, reviewed, and used in clinical decision-making [9]. The core outcome of DQM is establishing the fitness of data for its intended use. National and international health care and health information–related organizations provide guidelines for the quality management of patient data that are generated within clinical settings [8-12]. However, in RPM, data are collected outside the clinical setting, and different stakeholders are involved at different stages of PGHD management both outside and inside the health care settings.
This paper describes the DQM recommendations provided to ensure that data from wearables are fit for use in clinical care.
## Overview
Recommendations for the quality management of PGHD arose from a mixed methods study on the quality management of PGHD from wearables and were constructed following a guideline development convention in health care [13-18] through the stages listed in Textbox 1.
Most of the data collection in this research was done before the COVID-19 pandemic. However, the rapid deployment of RPM during the pandemic emphasizes the need for guidelines, such as the one constructed in this study, to improve the use of RPM initiatives and efficiently integrate them into the routine care.
**Figure 1:** *Continuum of integrating multiple qualitative findings to create new evidence.*
## Ethics Approval
The stakeholder involvement studies received approval from the Human Ethics Advisory Group at the Department of General Practice at the University of Melbourne [5,20]. The ethics approval number for the validation study from the same group is 1955682.1.
## Recommendations and Key Themes
The ensuing guideline encompasses 19 recommendations. These recommendations were grouped according to 7 overarching DQM aspects. Table 1 lists these 7 aspects; their adapted definition for this research; and the key themes identified from the literature review, interviews, and workshop studies. The sociotechnical issues to be considered in relation to each DQM aspect have been discussed in the corresponding recommendations. Through this style of presentation, PGHD stakeholders can understand what actions they and others need to take to collect, manage, and use trustworthy PGHD in RPM.
**Table 1**
| DQM aspects and key themes | DQM aspects and key themes.1 | Recommendations |
| --- | --- | --- |
| PGHD accessibility: authorized users of PGHD access t hem across all data management stages | PGHD accessibility: authorized users of PGHD access t hem across all data management stages | PGHD accessibility: authorized users of PGHD access t hem across all data management stages |
| | Patients’ and clinicians’ access to PGHD | Both raw and processed PGHD from wearables should be accessible to the patient and clinician. |
| | Patients’ and clinicians’ awareness of PGHD access by others | A mechanism should be available to the patient and clinician to set up notice recurrence on where, when, how, and by whom PGHD from wearables are accessed. |
| | Patients’ consent to PGHD access by different clinicians | A mechanism should be available to the patient to change permissions for clinicians to access PGHD from wearables. |
| PGHD accuracy: error-free data | PGHD accuracy: error-free data | PGHD accuracy: error-free data |
| | Automatic and manual PGHD collection | PGHD should be collected automatically by the wearable device, with as little as possible manual intervention. |
| | PGHD annotation | Annotation function for manually and automatically entered PGHD should be available to the patient and clinician in order to comment on inaccurate data. |
| | Wearable calibration | The wearable should be calibrated automatically as required by the clinical standard of care of diseases. |
| PGHD completeness: no PGHD are missing | PGHD completeness: no PGHD are missing | PGHD completeness: no PGHD are missing |
| | No active data collection | A protocol should be available to the patient and clinician that defines PGHD “downtime,” that is, the time range during which it is acceptable if the wearable is not collecting data. |
| | Resuming PGHD collection after downtime | A protocol should be available to the patient and clinician for resuming PGHD collection when the acceptable downtime period is exceeded. |
| | Context for incomplete PGHD | Annotation function should be available to the patient in order to provide context for any period of missing PGHD. |
| PGHD consistency: data convey the same meaning no matter whether they are collected from one or different brands of wearables | PGHD consistency: data convey the same meaning no matter whether they are collected from one or different brands of wearables | PGHD consistency: data convey the same meaning no matter whether they are collected from one or different brands of wearables |
| | PGHD definitions and formats | PGHD from wearables should be collected based on clinically accepted and structured data definitions and standard formats. |
| | PGHD integration with electronic medical records | PGHD from wearables should be integrated into the patient’s clinical care record. |
| | PGHD exchange within and outside care settings | PGHD from wearables should be consistently exchanged inside and between clinical settings. |
| PGHD interoperability: data presentation highlights the key message that is understood by PGHD stakeholders | PGHD interoperability: data presentation highlights the key message that is understood by PGHD stakeholders | PGHD interoperability: data presentation highlights the key message that is understood by PGHD stakeholders |
| | PGHD contextualization | PGHD from wearables should be accompanied by contextual data that are clinically important to patient management. |
| | Dynamic and static PGHD visualization | Dynamic visual representation as well as a static snapshot (such as in PDF format) of PGHD from wearables should be available to the patient and clinician. |
| | The patient’s understanding of PGHD | Alerts should be sent to the patient during PGHD collection by the wearable when data are outside the acceptable range, accompanied by clinical advice on action to take. |
| PGHD relevancy: data are pertinent to the standard of care for the condition being monitored | PGHD relevancy: data are pertinent to the standard of care for the condition being monitored | PGHD relevancy: data are pertinent to the standard of care for the condition being monitored |
| | PGHD relevancy to the standards of care | There should be a shared understanding between the patient and clinician of relevant data for the disease based on the standards of care and make sure that all the relevant data are collected. |
| PGHD timeliness: availability of up-to-date PGHD for patients and clinicians when needed | PGHD timeliness: availability of up-to-date PGHD for patients and clinicians when needed | PGHD timeliness: availability of up-to-date PGHD for patients and clinicians when needed |
| | PGHD availability to patients when needed | PGHD from wearables should be available to the patient within a timeframe (continuously to periodically) according to the standards of care of diseases. |
| | PGHD availability to clinicians when needed | PGHD from wearables should be available to the clinician within a timeframe (continuously to periodically) according to the standards of care of diseases. |
| | Time frame for PGHD sharing between patients and clinicians | A timeframe for sharing PGHD from wearables should be available to the patient and clinician. |
## PGHD Accessibility
PGHD accessibility was characterized by data access methods, privacy protection, and data ownership issues to be explored in RPM.
## Recommendation 1: Both Raw and Processed PGHD From Wearables Should Be Accessible to the Patient and Clinician
The extent to which patients and clinicians currently have access to all the recorded PGHD is questionable. PGHD accessibility largely depends on who owns the data to have complete access to them. PGHD have not yet been fully incorporated into clinical workflows; therefore, these data are neither controlled nor owned by health care organizations. Rather, the raw and processed PGHD from each wearable platform are accessed and controlled by the device company outside the health care setting.
Access to raw and processed PGHD during data collection may increase patients’ awareness of their health status and whether they are required to take action or change their behavior and improve self-care. Now, the trend in wearable design is shifting toward data visibility to patients [23,24]. Nevertheless, clinicians may intentionally disable the access of raw data to patients during data collection, as it would lead to patient behavior change that might conflict with the purpose of the RPM program. The ability to access all raw and processed PGHD could also be limited by wearable companies. Medical device manufacturers should share comprehensive and contemporary health information with patients upon request [25]. Therefore, patients are within their rights to request health information that is captured, stored, and analyzed by and retrieved from a legally marketed medical device. Different policies suggest that wearable developers, regardless of the wearable type, should provide patients complimentary access to PGHD [26-28].
Considering these policies, PGHD ownership has not yet been defined clearly enough to determine who owns part or all of the data, affecting patients’ access to PGHD [29,30].
In terms of clinicians’ access to raw and processed PGHD, the necessity to access all the collected raw and processed data depends on which data are needed for decision-making. Our findings showed that it would be difficult for a clinician to find the log-in details of a patient’s wearable portal if the patient has changed their portal account information or the device without informing the clinician. Clinicians’ access to PGHD might also be prevented by patients, which might reveal that they have not followed their care plans [31].
Collecting various types of PGHD from different wearables outside the clinical environment means that data are stored across different platforms. Ideally, PGHD should be accessible to the people who collect them, and access methods should be transparent. The purpose of giving patients and clinicians access to PGHD is to enable them to have a clear picture of the former’s health status.
## Recommendation 2: A Mechanism Should Be Available to the Patient and Clinician to Set Up Notice Recurrence on Where, When, How, and by Whom PGHD From Wearables Are Accessed
It is important that patients and clinicians be aware of who else has access to PGHD during data management from outside the health care setting to inside it. Patients and clinicians in this project had little understanding of how and by whom PGHD are accessed during data management stages in RPM [32]. Clinicians placed responsibility on the wearable developer for informing patients about who can access their data. Also, the installation terms and conditions of a large number of health wearables’ apps indicate that the wearable developers are the owners of PGHD and have authority to grant data access to others [26]. A review study of 4 known wearable products showed that the privacy policy of only 1 platform asserted PGHD as users’ sole and exclusive property [33]. However, these companies’ statements were not accompanied by strategies to support patients’ awareness of the accessibility of their data to others. Patients should be informed about what PGHD are collected and accessed, including possible lawful access by third parties; whether these data are identifiable or depersonalized; and how they are accessible for clinical decision-making [27,28,34,35]. Patients need transparency about PGHD access not only before data collection but also throughout all the PGHD management stages. Not knowing who has access to their data can deter or inhibit PGHD collection [23].
Initiatives such as the privacy notice checklist developed by the US Office of National Coordinator for Health Information Technology are to be used by wearable developer companies to disclose their privacy and security policies to patients and inform them about what happens to their PGHD once they purchase and use the device [36]. However, this notice appears to be more applicable to wearables for self-management than to those for RPM. In addition, one-off use of the privacy notice checklist cannot ensure the notification of all PGHD accesses during all data management stages. For example, PGHD might be transferred from the patient to the clinician through communication networks that might be hacked. Many RPM programs lack robust cybersecurity mechanisms [37].
Using PGHD in clinical practice means that clinicians might also need to be notified about PGHD flow to be able to track patient monitoring instructions from other clinicians if necessary. Patients and clinicians should be able to set up notification recurrence of PGHD access based on their preference.
## Recommendation 3: A Mechanism Should Be Available to the Patient to Change Permissions That Clinicians Have to Access PGHD From Wearables
The patients’ and clinicians’ awareness of circumstances under which PGHD are accessed does not give patients the authority to consent to PGHD access by others.
It is unclear to PGHD stakeholders how patient’s consent to PGHD access should look [38]. Patients in the RPM of our 3 use cases, diabetes, cardiac arrhythmia, and sleep disorders, sign a consent form at the beginning of the program. However, the continuous nature of data collection and access in RPM might require constant PGHD access authorization when different clinicians need to access the data for different purposes of patient care [29,39]. There are concerns that the clinicians may access PGHD at a stage where the data have not been granted access to by the patient. This might not be ethical even if done to benefit the patient [40].
Patients themselves may have little awareness of PGHD consent, and their attention may be confined to the terms and conditions statement before pressing the consent button for installing the wearable components. However, the wearable developers’ privacy policies and terms of service are often difficult to read and understand [41,42].
Appropriate consent management mechanisms enable patients to manage their consent preferences. Nevertheless, there is not yet a well-established consent mechanism for continuous data collection and use [28]. As various types of PGHD might be collected through different wearable platforms, sensitive data might be released when using one consent at the beginning of the RPM program. For example, patients might not want to provide details about their behaviors or lifestyles to clinicians in a certain time frame if it would lead to judgment or being shamed for perceived unhealthy choices during data collection. Thus, a process of dynamic consent might be more feasible to give patients control over the level of access to their data for different purposes and the choice of whether these data are anonymized or identifiable [43,44]. It could provide more personalized approaches and improve the continuous patient-clinician communication. Also, it gives patients the ability to understand and decide to what extent they are willing to share their data. Moreover, defining different levels of permission enables patients to review consent over a period to update or withdraw data at any time without affecting previously collected data [28].
## Validation Results
Each of these 3 recommendations about PGHD accessibility was rated as “essential” to the to the safety and quality of care in RPM (reached an aggregated $60\%$ agreement as being important to very important).
Each of the 3 recommendations about PGHD accuracy was rated as “essential” (each one reached an aggregated $60\%$ agreement as being important to very important).
Each of the 3 recommendations about PGHD completeness was rated as “essential” (all reached an aggregated $60\%$ agreement as being important to very important).
recommendations 10 and 11 about PGHD consistency were rated as “essential” (they reached an aggregated $60\%$ agreement as being important to very important), whereas recommendation 12 was rated by 9 ($90\%$) of the 10 participants and reached less than $60\%$ agreement for inclusion in the defined categories.
Each of the 3 recommendations about PGHD interpretability was rated as “essential” (reached an aggregated $60\%$ agreement as being important to very important).
Half of the participants rated PGHD relevancy recommendation as very important ($40\%$) to important ($10\%$), whereas $40\%$ addressed it as moderately ($30\%$) to slightly important ($10\%$).
All of the recommendations about PGHD timeliness were rated as “essential” to the safety of and quality of care in RPM (reached an aggregated $60\%$ agreement as being important to very important).
## PGHD Accuracy
PGHD accuracy is compromised by a patient’s errors or other error sources during data management, as well as uncontrolled possibilities for data revision.
This aspect depends on the technical features of the wearable and its components and the behaviors of the patient or caregiver at the point of data collection. Clinicians’ trust of PGHD accuracy is significantly impacted by the differentiation between medical grade and consumer wearables. Clinicians trust the level of accuracy in PGHD captured by medical wearables owing to their preassessment and approval from regulatory bodies. Our findings showed that consumer wearables were not used in RPM because of not being regulated for clinical use. However, even a medical wearable may not work accurately in some instances, as identified in our interviews and workshop studies. Moreover, a study showed that the inaccuracy of continuous glucose monitoring (CGM) wearables was the most critical impediment ($53\%$) to the use of these devices by diabetic adults [45]. Nevertheless, as the wearables collect data longitudinally, clinicians may trust the overall trends rather than doubting whether a single data point was captured correctly.
## Recommendation 4: PGHD Should Be Collected Automatically by the Wearable Device, With as Little as Possible Manual Intervention
The way PGHD are collected can pose risks for data accuracy. Automated sensing via algorithms embedded into wearables can provide persistent collection and analysis, providing a comprehensive picture of a patient’s status over time. Automation can lower the tracking burden, improve PGHD accuracy, and accelerate data filtering for timely access [39]. For a patient with low digital health literacy, automated data collection can reduce the level of disengagement with the device.
In addition to automatic data collection, some wearables require types of PGHD such as meal, activity, and mood data to be entered manually in the wearable platform on a daily basis. This can place a burden on patients and result in inaccurate and inconsistent recordings [46]. Yet, there has been no innovation to change the manual collection of these types of PGHD into a seamless automatic process; however, the extent of engagement in manual PGHD collection and documentation might depend on the patients’ level of understanding of the data and the message that PGHD could convey to the patients [47]. From clinicians’ perspectives [48,49], automated data collection and transmission to the associated app is a more accurate mechanism to evaluate peak flow variability than a patient’s difficult and time-consuming manual calculations.
Some PGHD types that patients were required to record manually—such as activity data in the remote monitoring of patients with diabetes—could be automatically captured via consumer wearables. Synchronization shortages between different types of medical and consumer wearables and a lack of adoption of consumer wearables in RPM are barriers to increasing automation. Although it might not yet be possible for some data elements to be captured automatically, there could be strategies to limit free-text entries. For example, wearable developers can reduce the possibility of errors in manual data entry in the associated apps by requiring the user to choose from a list of options instead of entering free-text [50].
However, having all PGHD collected automatically may lead to less control by patients over their health status and reduce their engagement in their self-care [51]. In addition, behavioral factors such as improper application of a sensor on the body or changing the device settings can have adverse impacts on PGHD accuracy. Automation can provide more accurate data if it does not negatively impact patients’ engagement in self-care.
## Recommendation 5: Annotation Function for Manually and Automatically Entered PGHD Should Be Available to the Patient and Clinician in Order to Comment on Inaccurate Data
Whether PGHD are collected automatically or manually, the ability to annotate them during data collection is a critical contribution to their accuracy [31]. In addition to the annotation of manual entries, the annotation of automatically collected data can help patients prevent errors in them and mark questionable data to discuss with clinicians [52].
The rapidly changing environment surrounding the patients may contribute to inaccuracies in manual and automatic captures [53]. Sometimes, the wearable works inappropriately or the patient makes mistakes in wearing the device or entering data; however, the feature of annotating both data collected manually and those collected automatically is not designed in many wearable platforms and is often overlooked in the testing of wearables for use in RPM [47].
According to our findings, patients can add notes on inaccuracies only through their diaries to discuss them with clinicians during the clinical consultations. However, the annotation feature could be embedded in the wearable design to reflect on data inaccuracy in real time instead of writing a diary note that might be forgotten. Patients could be notified of the incorrect values to annotate data or redo data collection instead of sending incorrect data to the clinician [30]. The wearable developers could also enable passive data annotation upon patients’ request [28]. Nonetheless, it is uncertain, if the patients themselves do not notice the errors, how they could annotate PGHD given that wearables often lack feedback mechanisms to alert the wearer about inaccuracies [54].
There is a concern that patients may use this functionality to override real actions. Therefore, the patients and caregivers need to be educated on PGHD annotation and build trust upon this functionality to enhance patient-clinician interaction and shared decision-making [46,55].
Clinicians should also be able to annotate the processed PGHD to understand data collection barriers and provide more efficient personalized care plans [29]. However, as the processed PGHD are usually represented as static snapshots to the clinician, it would be difficult for a clinician to annotate the reports and highlight the problematic areas of PGHD [56,57].
## Recommendation 6: The Wearable Should Be Calibrated Automatically as Required by the Clinical Standard of Care of Diseases
Both medical and consumer wearables may collect inaccurate data. Therefore, it is important to ensure that wearables are calibrated to guarantee accurate sensing [30,39,58].
Some wearables need one-off calibration by the clinicians before initiating remote monitoring, whereas for other types such as CGM devices, the patient should frequently calibrate the device via a glucometer to ensure PGHD accuracy. Nonetheless, a patient’s responsibility in terms of how often and when they should calibrate the wearable device in RPM is often unclear [31]. The need to calibrate wearables not only is a burden on patients or their caregivers but also increases the likelihood of inaccuracies. Patients should be taught the importance of calibration and when it should be done. For example, from a clinician’s point of view, the 12-hour calibration for CGM wearables prescribed by most wearable developer companies [59] is not clinically acceptable; rather, calibration should be done 3 times a day when the blood glucose is not rapidly changing. Similarly, the best time to calibrate the device is when the glucose level is stable [60]. A study showed that nearly half of the participants reported calibrating CGM at more intervals than recommended by the wearable developer to ensure data accuracy [45].
Although regular calibration might be a burden, understanding its value would encourage patients to do it correctly [55]. However, considering the possibility for error to arise from the manual calibration of CGM devices with glucometers, an automatic calibration mechanism could be preferable.
Moreover, the wearable developers could conduct dynamic testing of the products. Clinicians want more collaboration with wearable developers to define strategies for continuous wearable assessment that can be achieved through various RPM interventions. Ideally, there should be a consensus among clinicians and wearable developers regarding guiding patients on the frequency of and providing instructions on calibration based on clinical principles.
## PGHD Completeness
Incomplete PGHD may be a result of technical or behavioral issues. Battery failure, wearable dysfunction, lack of synchronization in different time zones, internet disconnection, or patient’s neglect are among the accidental causes of insufficient PGHD. This may compel clinicians to reorder data collection. Moreover, there might be deliberate data omissions for both manual and automatic entries because of demotivation, lack of digital literacy, body pain, or perception of having no changes owing to seeing similar trends over time [61].
Lack of continuous follow-up may also result in incomplete data. Different health care settings define data sharing time frames differently in the remote monitoring of the same health condition; this can create confusion in patient and clinician communication. Lack of continuous interaction with patients during remote monitoring could result in a lack of engagement in self-care and motivation to collect data [62]. None of the published RPM studies have reported an approach to identify the exact reason for data incompleteness.
## Recommendation 7: A Protocol Should Be Available to the Patient and Clinician That Defines PGHD Downtime, That Is, the Time Range During Which It Is Acceptable if the Wearable Is Not Collecting Data
Given the constant automated sensing capabilities of wearables, it is unclear whether patients are required to wear the devices continuously in different RPM programs to provide sufficient data for their care planning. There was lack of awareness among PGHD stakeholders in our studies on standardizing “downtime” when patients can stop data collection in different RPM programs. From clinicians’ point of view, a CGM wearable should be worn for at least $80\%$ of the RPM period so that it can provide complete data for interpretation and decision-making. However, it is thought to be burdensome for patients to have to wear the device day and night and calibrate it frequently [55]. *New* generations of wearables seem to address “downtime” by letting patients turn off the device. Alerts could be designed in wearables to help clinicians discuss the reasons for incomplete data with patients.
As mentioned in the PGHD Accuracy section, clinicians would prefer focusing on data trends over time rather than single data points; therefore, some degree of missing data is acceptable [51]. Some clinicians do not see PGHD completeness as fundamental for sound decision-making [63]. However, it is important to know the extent of the impact of incompleteness on data interpretation and decisions made for patient care in the remote monitoring of different diseases [51,64]. Having a predefined and transparent downtime protocol on which the patient and clinician agree could clarify the completeness of PGHD [65].
## Recommendation 8: A Protocol Should Be Available to the Patient and Clinician for Resuming PGHD Collection When the Acceptable Downtime Period Is Exceeded
Applying the acceptable time frame in which patients can stop collecting PGHD cannot be thoroughly understood unless patients are aware of when to resume data collection.
However, this might not happen if patients forget to do so. Moreover, owing to a lack of technical infrastructure for the real-time transmission of data from outside to inside the health care setting, clinicians are not aware of the missing data during data collection and thus are unable to alert patients to resume data capture [66,67]. This could be considered in the wearable design; for instance, wearables can provide patients the ability to set an alarm based on the predefined acceptable downtime schedule.
## Recommendation 9: Annotation Function Should Be Available to the Patient in Order to Provide Context for Any Period of Missing PGHD
Any missing data need to be supplemented by contextual information to help clinicians identify the causes and discuss them with patients [63]. Contextual information about the missing data can help clinicians understand whether the problem was technical, behavioral, or related to the process of data transmission. Incomplete data in themselves do not explain the circumstances that led to their incompleteness [51].
Although some wearables were reported to provide notification of missing data, they still lack contextual information. This could place a burden on patients to be constantly attentive to record the causes of incompleteness. Innovative mechanisms could be designed to increase the interaction between the device and the wearer to record contexts for the incomplete data in a real-time or on a daily basis. The annotation feature that was mentioned in the PGHD Accuracy section is equally important to enable patients to enter information regarding missing data [56].
## PGHD Consistency
PGHD consistency is characterized by the ability to compare PGHD of one measurement from different devices as well as the ability to relate PGHD to the corresponding conventional clinical measurement.
Various wearables and associated mobile apps and web portals used in RPM programs may not represent data in a consistent manner. PGHD inconsistency can happen during data collection, transmission, and review, which immensely impacts data presentation that may have not been thoroughly recognized by PGHD stakeholders.
## Recommendation 10: PGHD From Wearables Should Be Collected Based on Clinically Accepted and Structured Data Definitions and Standard Formats
Both consumer and medical wearable platforms may fail to represent data in clinically standardized formats [30,68,69]. Nonstandard presentation can result in confusion in data interpretation and inability to discern whether PGHD reports show normal or abnormal trends [51]. The standardization of health data elements is intended to define what data are to be collected, decide on how the collected data should be represented, and specify how the data should be encoded for transmission [70].
Collecting PGHD from different types and brands of wearables, each with its own data presentation format, could result in inconsistent reports [49,51]. Most of the recent PGHD-related policies advise developing standardized formats for PGHD collection that align with the clinical data standards, which are defined as protocols, terminologies, and specifications that are used during data management stages [23,50,54,71,72].
To ensure consistent definitions and formats for PGHD, 2 approaches should be considered: PGHD consistency at data collection and PGHD consistency at the data processing stage. Patients may need to be advised to collect PGHD from one type or brand of wearables for the remote monitoring of a particular health condition to provide consistent reports. Clinicians in our studies and others [62] preferred to give patients autonomy over device selection and stated that patients should have the right to select a convenient and easy-to-use wearable device. Nonetheless, because PGHD cannot be further filtered by information systems within a health care setting to fix the inconsistencies that emerge from collecting data in different formats, data that are presented for review might be difficult to interpret. Inconsistent reports at the data review stage are a consequence of collecting data from disparate wearable platforms. The second approach is to standardize PGHD at the data processing stage regardless of the wearable used in data collection. In this case, robust technical infrastructure needs to be in place to allow gathering PGHD from different wearables and their apps and portals in one database to filter and process data and present standardized reports that are similar to clinical data presentation formats.
Universally accepted data definitions and data exchange formats are required to facilitate effectual data transfer. Data should be codified according to the known clinical standards. In addition, ontologies that could aggregate and enrich PGHD with definitions, synonyms, and term relationships can be developed to provide standardized formats and make data semantically exchangeable [73].
## Recommendation 11: PGHD From Wearables Should Be Integrated Into the Patient’s Clinical Care Record
Lack of PGHD integration with electronic medical record (EMR) systems is another barrier to PGHD consistency [30,39,74-76]. Current RPM programs are project oriented and not embraced in routine clinical practices. Moreover, most current EMR systems are not designed to seamlessly gather various types of data from outside the clinical setting in a straightforward manner [77]. PGHD should be combined with the patient’s clinical record to identify potential correlation with past conditions and be used in future interventions [30,46,54].
Despite the clinicians’ preference for the patients to choose their own brand of wearable, PGHD integration with EMRs constrains the selection of wearable. Patients should only use wearables in RPM that follow the interoperability standards used in the health care setting.
Most policies addressed the necessity of integrating PGHD with EMRs [23,26,35,54], but few provided specific suggestions. For example, the American Medical Association’s best practices for digital health implementation recommend that standard communication templates be designed before implementing RPM intervention to ensure consistency in data documentation during the whole process [72]. Therefore, PGHD integration with EMRs might be facilitated by modifying the EMRs, developing external dashboards, or limiting the choices of the brand of wearable used for data collection.
## Recommendation 12: PGHD From Wearables Should Be Consistently Exchanged Inside and Between Clinical Settings
In addition to the need for standardized formats and integration with EMRs, PGHD exchange inside and outside the health care setting needs to be consistent by following health data exchange protocols.
If >1 health care setting is actively represented in an RPM program for one disease cohort or different departments implement RPM in one health care setting, data should be exchanged consistently to be understandable by different clinicians. This requires standardized formats for various types of PGHD.
Interoperability initiatives developed by the Australian Digital Health Agency [78] defined standards to facilitate data and information exchange and provided compliance mechanisms in connected health programs. These standards are broad and cover both PGHD and clinical data collected from outside and within health care settings. More specific interoperability standards were introduced by the Personal Connected Health Alliance with its Continua guidelines for data interoperability in personal connected health devices [79]. These initiatives need to be tested in various RPM programs to assess the consistency in data exchange.
## PGHD Interpretability
Interpretability is affected by the way in which PGHD are presented as well as by the availability of contextual information regarding PGHD.
Not understanding the presented data can reduce patients’ and clinicians’ motivation for data collection and review [31,80,81]. Challenges of PGHD interpretation can occur at any stage of data management. This aspect was mentioned as the most challenging feature in our studies.
## Recommendation 13: PGHD From Wearables Should Be Accompanied by Contextual Data That Are Clinically Important to Patient Management
The increasingly high volume and dynamically changing nature of PGHD make it difficult and time-consuming to gain a holistic view of a patient’s status from the data alone [31,74,82].
Most PGHD are not supplemented by contextual information about the circumstances in which the data were collected. Lack of context can lead to misunderstanding; misinterpretation; and, consequently, unsound decisions [80]. For example, when a clinician tries to discern a pattern in a processed PGHD report, it may be unclear whether a graph showing a lack of activity reflects the patient’s demotivation, a problem in the wearable’s function, or a medication interruption [56]. In this situation, relying on the patient’s verbal expression without recorded contextual information is not sufficient to draw an understanding of the patient’s situation per trend.
Similar to its application in PGHD completeness, context for PGHD is important for the data review stage so that clinicians can understand what the patient was doing at the point of data collection [31,52,74,83]. Among the wearables studied in this project, only CGM devices allowed the manual capture of limited contextual data—such as those about mood and exercise—in cases where the automatically captured data were reported to be erroneous or incomplete. PGHD from medical wearables can be contextualized by data that are automatically captured from consumer wearables [80,84]. However, no mechanism exists to integrate these 2 types of wearables in RPM, and there is uncertainty about which contextual data are more relevant to patient care. In addition, the ability to understand and interpret contextual information is still beyond clinicians’ expertise [85].
More collaboration between patients, clinicians, and wearable developers is needed to identify what contextual data need to be collected for each health condition and whether these data elements should be incorporated within the wearable design or require wearable integration.
## Recommendation 14: Dynamic Visual Representation as Well as a Static Snapshot (Such as in PDF Format) of PGHD From Wearables Should Be Available to the Patient and Clinician
Clinicians review the processed PGHD reports either from the patient’s or clinician’s portal that the wearable developers design for them with static visualization of data that can be downloaded in a PDF format.
Having a snapshot of all the data collected over time provides a summary of the patient’s status, but as the amount of PGHD increases, such a report could be progressively more complex for their clinician to interpret [86]. Designing interactive visualization tools based on clinicians’ needs can result in easy PGHD interpretation [52,76,82,87].
Interactive visualizations could enable clinicians to highlight the most concerning areas and customize the reports based on different variables [87,88]. Interactive visualization supported by annotation capability can facilitate the cointerpretation of PGHD report, such as the ability to add highlights in a graph to detect changes. It is beneficial to patients to have a saved version of points and notes of what they and their clinicians identified and discussed for use in the next consultations. Likewise, in the subsequent clinic visit, the clinician could readily recollect what the previous consultation was focused on, which helps recognize patterns and set efficient care plans [56]. A dynamic and interactive visualization could also layer PGHD displays based on clinicians’ preferences. Studies have shown that different layers of data presentation, such as a holistic summary, an individual data summary, and detailed individual data, support the comprehensive interpretation of PGHD [52,76,82].
Notwithstanding, it is a challenge for wearable developers to design data presentation formats that please all clinicians with varying levels of digital health literacy. Collaboration among PGHD stakeholders is needed to determine who should design interactive dashboards for PGHD presentation, whether the dashboards should be implemented within EMRs or somewhere else in the health care setting, and how the reports should be presented to the patient and clinician to inform shared understanding and decision-making.
## Recommendation 15: Alerts Should Be Sent to the Patient During PGHD Collection by the Wearable When Data Are Outside the Acceptable Range, Accompanied by Clinical Advice on Action to Take
In addition to clinicians’ interpretation of PGHD, it is important to ensure that patients can also interpret the data correctly. Not understanding PGHD can reduce the motivation to continue data collection. Efforts toward changing the patients’ roles from passive participants to active players in RPM require patients to understand PGHD and make necessary changes during data collection [89].
However, wearables do not provide understandable contextual information on PGHD. Most wearables display PGHD without further explanations of their meaning, the normal range, what will happen to the patient’s health status if their measurements are out of range, or what actions the patient could take if their measurements are out of range. This is problematic if the patient cannot immediately communicate with their clinician when they see significant changes occur in their data trends and do not know what action to take.
There are alarms embedded in some types of consumer wearables that notify out of range measurements [85]. Although PGHD from these tools may not require urgent actions, such features could improve patients’ interpretations of the data and better inform and influence behavior change.
Some medical wearables are equipped with a feature that alarms when the raw data go outside the normal range. Devices without this feature could be dangerous to a patient’s health, as immediate medical action may not be undertaken when needed. Nevertheless, the questions of to what extent patients could interpret PGHD augmented with contextual information during RPM without a clinician’s intervention and how sound a patient’s decision would be based on the interpretation are largely unexplored.
Cointerpretation of PGHD improves the shared understanding of data reports and generates an additional layer of meaning for PGHD in patient care plans [90]. These strategies particularly depend on patients’ and clinicians’ training and collaboration with the wearable developers to improve PGHD presentation design and interpretation.
## PGHD Relevancy
PGHD relevancy is characterized in various manners depending on the scope and coverage of data for each health condition. The values of conventional clinical data collected inside the health care setting are defined based on the standards of care. However, PGHD include a wide range of heterogeneous and new types of data whose relevancy to the monitored health condition might be unclear. Only 1 common theme was found in the previous studies of this project for PGHD relevancy, which resulted in the recommendation discussed next.
## Recommendation 16: There Should Be a Shared Understanding Between the Patient and Clinician of Relevant Data for the Disease Based on the Standards of Care, and Make Sure That All the Relevant Data Are Collected
PGHD relevancy was perceived as the most distinguishing DQM factor in using PGHD from consumer wearables versus those from medical wearables in RPM, and its lack was perceived as the most predominant barrier to the adoption of PGHD in clinical practice [91]. Patients and clinicians might have different perspectives on which types of PGHD are relevant to patient care [31]. Patients’ enthusiasm to use a wide range of consumer wearables and collect new types of PGHD that have not been collected easily before (eg, heart rate, sleep quality, and activity level) increases their expectation from clinicians to review the data. By contrast, clinicians might not be convinced of the extent to which the data are relevant to the health condition and supplement the clinical data collected from medical wearables to provide a better picture of patients’ status.
Clinicians involved in this project along with other studies indicated that PGHD from consumer wearables have not yet been proven to correlate with most health conditions and that they are different from other clinical data in terms of clinical value [39,76,82].
Even if PGHD are collected from medical wearables, it would still be challenging to identify whether all the data are relevant to the specific health condition. Conversely, some wearables cannot capture all the relevant PGHD; therefore, important relevant data might be missed, which might lead to incorrect decisions about patient care [76,83]. The need for the collection and analysis of relevant data was addressed by recent PGHD-related policies [23,54,72]. Only 2 clinical guidelines developed to address the details and level of relevancy of PGHD collected from wearable devices for the remote monitoring of patients with diabetes and those with cardiac arrhythmia were identified [60,65]. More guidelines are needed to determine relevant PGHD for the remote monitoring of each health condition.
## PGHD Timeliness
PGHD timeliness is characterized by the timing and frequency of PGHD availability to patients and clinicians.
## Recommendation 17: PGHD From Wearables Should Be Available to the Patient Within a Timeframe (Continuously to Periodically) According to the Standards of Care of Diseases
Our findings showed that the timing of PGHD availability to patients was overlooked. Although accessing data during data collection is critical when a decision needs to be made, some wearables do not provide real-time PGHD access to patients during data collection. As discussed in the PGHD Accessibility section, depending on the health condition and the clinical purpose of RPM, PGHD presentation to patients in real time might be deliberately disabled by clinicians. However, studies have shown that accessing real-time data from the wearable increased patients’ awareness of the wearable’s function, further engaged them in self-care, and enhanced shared decision-making [92-94].
As PGHD collection in RPM are led by clinicians, patients may not be fully aware of their rights in accessing PGHD at data collection and how it might impact their safety. PGHD access in real-time or periodic mode needs to be defined according to the standards of care of the health condition [65]. RPM interventions could be designed based on patient-centered care models where time frames could be established so that patients can access their data during data collection to make a proper decision, change their behavior, or immediately contact the clinician.
## Recommendation 18: PGHD From Wearables Should Be Available to the Clinician Within a Timeframe (Continuously to Periodically) According to the Standards of Care of Diseases
The most challenging issue reported about PGHD timeliness is the lack of clinicians’ access to data in real time [39,77,83]. As PGHD are not yet integrated with EMRs, it is difficult and time-consuming to frequently receive the data and follow-up with patients. PGHD integration with EMRs would provide possibilities for generating alerts on newly added PGHD in the EMR system in a real-time or near real–time basis so that clinicians can be updated on a patient’s status and provide prompt feedback [83]. Notwithstanding, the technical integration by itself is not the ultimate solution. PGHD need to be fully incorporated into clinical workflows such that clinicians could receive data based on predefined protocols and be able to provide timely advice to patients [95]. Timely access to PGHD without immediate feedback to patients would lead to patient demotivation on data sharing [96]. However, RPM interventions may have different protocols for PGHD availability to clinicians. In some cases of remote monitoring of patients with diabetes, clinicians remotely obtain PGHD reports from patients during data collection, whereas in others, they see the report after data collection during the clinic consultation. Having predefined protocols might facilitate clinicians’ access to PGHD within a specific time frame.
## Recommendation 19: A Timeframe for Sharing PGHD From Wearables Should Be Available to the Patient and Clinician
As noted earlier, RPM programs apply disparate time frames for PGHD sharing. This way of accessing data can be challenging. Patients who access data in real time may also need to receive a clinical advice immediately, whereas data are not available to clinician in the same time frame. Frequent data sharing during data collection could help recognize some behaviors that might not be identified when the collection period is finished.
PGHD need to be available when there is an urgent need for clinical advice so that the patient can change the way of data collection or their behavior accordingly [30,97]. However, findings showed that this depends on the health condition; for example, the guideline on using wearables in cardiac RPM emphasized that these services should not be mistaken with acute care; therefore, there is no urgent need for real-time feedback [65]. Hence, based on the health context, having transparent protocols on data sharing could help clinicians review PGHD and set patients’ expectations for data transmission and feedback [76].
As different RPM programs may need different approaches on data timeliness based on the standards of care, there should be a single time frame defined for the remote monitoring of each health condition to ensure consistency among the programs.
## A Staged Model of Quality Management of PGHD in RPM
Figure 2 illustrates the recommendations according to the importance of their consideration at different stages of data management. This model can assist PGHD stakeholders in understanding what DQM actions need to be taken to efficiently collect, manage, and use PGHD in RPM.
As shown in Figure 2, all DQM aspects of PGHD require attention at the data collection stage. It indicates that the quality management of PGHD is critical when data are collected outside the clinical environment under patients’ or their caregivers’ supervision. Data access, consistency, and timeliness were the most critical DQM aspects to be considered during PGHD transmission from the patient to clinician. These 3 aspects along with PGHD interpretability require emphasis when clinicians review the data reports for shared decision-making and creating patient care plans.
As there are interconnections among DQM aspects, this model indicates that collaborative actions need to be undertaken by different PGHD stakeholders to practice DQM and ensure high-quality PGHD in RPM.
**Figure 2:** *Recommendations for the quality management of patient-generated health data (PGHD) at the 3 stages of PGHD management. EMR: electronic medical record.*
## Principal Findings
This paper presented the development of 19 recommendations for 7 DQM aspects of PGHD collected from wearable devices in RPM programs. The guideline aims to assure that high-quality data are collected, managed, and used in RPM programs to improve the safety and quality of these programs and enhance PGHD fitness for use in routine clinical practice.
The guideline was constructed by following 4 steps of guideline development process through 5 qualitative studies. The guideline was then conceptualized to address 3 main concepts: PGHD management process, DQM aspects of PGHD, and sociotechnical issues that influence the quality management of PGHD during the data management process.
The DQM guideline for PGHD is distinguished from conventional DQM guidelines for clinical data in several ways: [1] it emphasizes the need for action corresponding to each DQM aspect at each stage of PGHD management; [2] it considers both external sociotechnical factors and internal organizational factors that impact the quality management of PGHD in RPM; [3] it recognizes patients’ and clinicians’ needs for each DQM aspect of PGHD, as the key PGHD stakeholders in RPM. This guideline is intended mainly for using PGHD for patient care. It is anticipated that the guidelines can also be used alongside conventional DQM guidelines for clinical data to assure PGHD quality and when these data are integrated into EMR systems.
To effectively apply the guideline in the remote monitoring of various health conditions, wearable devices should not be considered as stand-alone tools that work in isolation. Instead, they should be looked at as one component of a bigger ecosystem where different stakeholders interact with each other, with the devices, data, technical infrastructure of the health care setting, and standards to ensure that high-quality PGHD are collected, managed, and used for patient care [3]. The guideline can be best applied when RPM is implemented for >1 health condition across the health care system and when PGHD are collected from >1 type of wearable device and system interconnections are facilitated [98]. Also, realizing the value of high-quality PGHD for patient care can potentially blur the reliability distinctions between the 2 types of wearables, consumer and medical wearables. Being approved by regulatory agencies as a medical grade wearable does not ensure that the PGHD from it achieve a satisfactory level of quality. PGHD from consumer wearables are rarely used in current RPM services, and the research findings mainly included PGHD from medical wearables, so unseen challenges might exist to the quality management of PGHD from consumer devices. Advances in the capabilities of consumer devices and patients’ and clinicians’ accessibility to them are likely to see greater crossover between medical and consumer wearables in the future.
The DQM guideline for PGHD in RPM cannot be successfully implemented and used if the health system does not address the factors listed in Textbox 2.
The implementation of PGHD quality management in RPM can benefit the health care system and those who are considered the stakeholders of PGHD and who might advantage from the incorporation of these data into clinical practice, including the groups listed in Textbox 3.
## Limitations
PGHD collection for self-management purposes without clinical use was out of the scope of this research. Moreover, this study did not concentrate on the concept of data quality as used in the biomedical engineering domain, such as the accuracy of the formula or algorithms embedded in wearables. We also limited the exploration of PGHD to their use in direct patient care, engaging with the stakeholders in this kind of use, and excluded the secondary uses of data, such as in outcomes research, surveillance, reimbursement strategies, and purposes other than patient care.
The recommendations of the DQM guideline of PGHD were defined at a high level. They would benefit from the addition of details that specify the roles and responsibilities of different stakeholder groups. This would require the guideline to be investigated more deeply with participation from different stakeholder groups to identify further considerations in different contexts.
The guideline might be questioned as not being specific to one health condition when it is known that RPM initiatives are distinctive in different contexts of care. However, it was extracted from the RPM initiatives for 3 chronic conditions that showed similarities and commonalities in the quality management of PGHD. It is worth noting that digital health implementation is moving toward focusing on the patient as a whole rather than the disease. Therefore, RPM initiatives, as well as the data they collect, could also shift their focus from a specific disease and wearable to services for the integrated management of all the health conditions that a patient might have [72]. Nevertheless, for further exploration, the guideline can be implemented in each disease-based RPM to provide more specific recommendations based on particular needs. Understanding what makes PGHD more reliable for shared decision-making can motivate PGHD stakeholders to have a shared understanding of the value of these data and use them more efficiently to achieve better health outcomes.
## Comparison With Prior Work
Research on the adoption, integration, and evaluation of RPM, wearables, and PGHD in clinical practice is rapidly growing [99-106], particularly during the COVID-19 pandemic, when many RPM initiatives were implemented around the world.
However, a few studies focused on PGHD quality [51,62,69,107] had aims and scopes that were different from those of our research. This is the first study of its kind that adapted 7 common aspects of DQM and investigated them in PGHD context during PGHD management stages. It also involved various groups of international PGHD stakeholders to share their experiences, concerns, and expectations regarding the quality management of PGHD and constructed and validated a set of recommendations as a novel guideline. This process helped reach a consensus among the participants on the recommendations they could follow to effectively collaborate for better patient care.
## Conclusions
Although the quality of PGHD is addressed as a vital factor in increasing their reliability in clinical decision-making, this research is the first of its kind to explore the quality management of PGHD through 7 aspects during data management stages. The guideline developed in this research provides a major step forward in this regard. It gives PGHD stakeholders a framework for improving the quality management of PGHD collected and used in RPM underpinned by collaboration.
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|
---
title: 'Effects of Fetal Images Produced in Virtual Reality on Maternal-Fetal Attachment:
Randomized Controlled Trial'
journal: Journal of Medical Internet Research
year: 2023
pmcid: PMC10007014
doi: 10.2196/43634
license: CC BY 4.0
---
# Effects of Fetal Images Produced in Virtual Reality on Maternal-Fetal Attachment: Randomized Controlled Trial
## Abstract
### Background
Maternal-fetal attachment (MFA) has been reported to be associated with the postpartum mother-infant relationship. Seeing the fetus through ultrasound might influence MFA, and the effect could be increased by more realistic images, such as those generated in virtual reality (VR).
### Objective
The aim was to determine the effect of fetal images generated in VR on MFA and depressive symptoms through a prenatal-coaching mobile app.
### Methods
This 2-arm parallel randomized controlled trial involved a total of 80 pregnant women. Eligible women were randomly assigned to either a mobile app–only group ($$n = 40$$) or an app plus VR group ($$n = 40$$). The VR group experienced their own baby’s images generated in VR based on images obtained from fetal ultrasonography. The prenatal-coaching mobile app recommended health behavior for the pregnant women according to gestational age, provided feedback on entered data for maternal weight, blood pressure, and glucose levels, and included a private diary service for fetal ultrasound images. Both groups received the same app, but the VR group also viewed fetal images produced in VR; these images were stored in the app. All participants filled out questionnaires to assess MFA, depressive symptoms, and other basic medical information. The questionnaires were filled out again after the interventions.
### Results
Basic demographic data were comparable between the 2 groups. Most of the assessments showed comparable results for the 2 groups, but the mean score to assess interaction with the fetus was significantly higher for the VR group than the control group (0.4 vs 0.1, $$P \leq .004$$). The proportion of participants with an increased score for this category after the intervention was significantly higher in the VR group than the control group ($43\%$ vs $13\%$, $$P \leq .005$$). The feedback questionnaire revealed that scores for the degree of perception of fetal appearance all increased after the intervention in the VR group.
### Conclusions
The use of a mobile app with fetal images in VR significantly increased maternal interaction with the fetus.
### Trial Registration
ClinicalTrials.gov NCT04942197; https://clinicaltrials.gov/ct2/show/NCT04942197
## Introduction
Pregnancy brings various lifelong psychological changes and frequently affects a woman’s values, identity, marital relationship, parenting, and mood [1-3]. Depression is a serious psychological complication of childbearing, and its prevalence has increased during the COVID-19 pandemic [4]. Prenatal check-ups include many laboratory tests and ultrasonographic examinations, usually reassuring pregnant women by confirming fetal well-being; however, this can also cause stress, and depression or anxiety can develop [2,5]. Postpartum depression is a critical problem, since it results in various harmful consequences, such as avoidance of childcare, child abuse, and suicide [6-9].
Several studies have revealed that strong maternal emotions and bonding with the baby have protective effects against postpartum depression and anxiety, and these studies have tried to determine which factors stimulate or improve maternal-fetal attachment (MFA) [10,11]. Delavari et al [12] demonstrated a significant inverse association between MFA and the development of postpartum depressive symptoms in a longitudinal study. Pregnant women with weak MFA are less likely to engage in health-promoting activities and more likely to be reluctant to perform childcare; thus, adverse outcomes in behavior and development of their children might increase [13-15]. In the natural course, MFA increases gradually as gestation progresses, stabilizes in late pregnancy, helps adaptation to physiological changes, and determines maternal responsibility [16,17]. Güney and Uçar [18] reported that maternal subjective recognition of fetal movement enhanced MFA.
Virtual reality (VR) is the newest trend in computer-based technology that simulates objects or situations regardless of location or time [19,20]. It has been applied in numerous fields of health care, such as providing treatment [21-23], facilitating pain management [24-26], simulating surgery [27,28], providing guidance for rehabilitation [29], and enhancing medical education [30,31]. Because pregnant women are not able to see their babies before giving birth, they perceive the existence of the fetus by indirect factors, such as fetal movement or grayscale videos viewed during ultrasonography examinations. Since VR, with the assistance of ultrasonography, can reproduce the appearance of the fetus very realistically, we hypothesized that the vivid images of the fetus possible with VR could help promote MFA.
The purposes of this study were to investigate whether fetal images generated in VR helped pregnant women to imagine or perceive fetal appearance and to determine the positive effect on MFA and the protective effect against depressive symptoms.
## Study Design
This 2-arm, parallel randomized controlled trial was conducted at the Department of Obstetrics and Gynecology at Seoul National University Bundang Hospital, Republic of Korea. Participants were recruited among pregnant women who visited the institution for routine prenatal check-ups and pregnancies after 20 weeks of gestation and were identified as being eligible. Patients with a history of any psychiatric disorder (eg, mood disorder or anxiety disorder) were excluded from this study. Recruitment began in June 2021 and enrollment was completed in October 2021.
The target sample size was calculated based on changes in Cranley score reported in a previous study [32]. Given an average Cranley attachment score of 2.8 (SD 0.51), 32 patients were required in each study arm to determine an increase in Cranley score of 0.3 using the software G*Power 3, with an α value of.05, a power of $80\%$ (β=.20), and a 2-tailed independent t test (or Student t test) [33]. Allowing for a $25\%$ dropout rate, we decided to enroll a total of 88 patients (44 participants per arm).
## Ethics Approval
Prior to initiation of this study, it was approved by the Seoul National University Bundang Hospital Institutional Review Board (B-2106-688-302), and the protocol was registered on ClinicalTrials.gov (NCT04942197). Written informed consent was obtained from all participants.
## Randomization and Protocol
During the study period, 88 eligible women were recruited and assigned randomly to either the VR intervention group or a standard-care group at a ratio of 1:1 by restricted randomization, which was generated with statistical software from the Medical Research Collaborating Center of the institution (Figure 1). Clinicians who met the participants in practice were not involved in the randomization process and were blinded to the allocation to each group until the participants completed the study protocol. Maternal baseline demographics and obstetric information were collected throughout the study period.
**Figure 1:** *Flowchart of participation in the study.*
As the baseline work-up, the participants in both groups received and filled out three questionnaires: [1] an MFA assessment, [2] a depressive-symptoms assessment, and [3] a basic health behavior status and medical information survey. After they finished the 3 questionnaires, all participants underwent 3D fetal ultrasonography and were shown the fetal images on an ultrasonography monitor; however, only the VR intervention group was shown fetal images that merged ultrasonographic data to produce VR images; these were viewed in a headset. The fetal images were generated in VR from the data obtained through ultrasonography of the subject’s own fetus and were shown only to the VR group. A prenatal-coaching mobile app was provided afterwards, and consistent education on how to use the various functions of the app was given to all participants. Approximately 2 weeks later, the researchers checked the interim feedback on the proper use of the prenatal-coaching mobile app, and a fourth questionnaire to assess maternal understanding of fetal appearance was given to all participants. All participants completed the trial in 4 weeks, lasting from enrollment to the end of trial. They then repeated the 3 questionnaires from the beginning of the study (Figure 1). Every questionnaire was provided to the participants before and after fetal ultrasound examinations at the outpatient clinic; the average time for completion of each questionnaire was approximately 10 minutes.
All participants received the prenatal-coaching mobile app Aluvuu (available for both Android and iOS; Girjae Soft) when they were enrolled. This prenatal-coaching app provides services that enable submitting results for maternal weight and blood pressure and checking changes with intuitive graphs, and it includes a glucose-monitoring diary for participants with gestational diabetes mellitus or pregestational diabetes mellitus. Relevant information from authoritative guidelines on helpful activities (eg, yoga, exercise, and stretching) and a recommended diet were supplied according to gestational age (Figure 2).
**Figure 2:** *Screenshots of the prenatal-coaching mobile app: (A) glucose-concentration monitoring diary for mothers with gestational diabetes mellitus; (B) graph demonstrating maternal weight change; (C) diagram showing maternal blood pressure, glucose concentration, diet, and exercise at a glance; (D) information on helpful activities during pregnancy by gestational age (eg, yoga for late pregnancy).*
All participants viewed 3D ultrasonography images; however, the VR experience and the fetal images produced in VR were provided only to the VR intervention group (Figure 3A). All the images generated in VR were sent to the prenatal-coaching mobile app, so that the users could see the images whenever they wanted. The users could share the saved pictures of their fetus with their family members and monitor growth via measurements of fetal body parts, such as head diameter (biparietal diameter), abdominal circumference, and leg length (femur length; Figure 3B). Progress in fetal growth at each ultrasonography examination was demonstrated in graphs that were saved in a private library folder. The users could compare the fetal image with everyday objects such as apples to help understand the actual size of the fetus (Figure 4A). In addition, the VR experience allowed the participants to actively engage their imagination about their expected baby by modifying specific structures of the fetal face, such as the eyes, nose, mouth, and cheeks, by touching the screen (Figure 4B).
**Figure 3:** *Fetal images produced in virtual reality: (A) fetal face and reconstructed image of the fetus generated in virtual reality based on 3D ultrasound–measured data of fetal body parts; (B) screenshots of the prenatal-coaching mobile app showing data converted from 3D ultrasounds and a graph demonstrating fetal growth (estimated based on data from ultrasound measurements).* **Figure 4:** *Additional services provided by the prenatal-coaching mobile app for the fetal images produced in virtual reality. (A) Progress in fetal growth at each gestational age in ultrasound images. (B) Interface for users to modify specific structures of the fetal face, such as the eyes, nose, mouth, and cheeks, in virtual reality.*
## Questionnaires and Assessments
The most important questionnaire assessed MFA, which was the primary outcome of this study. Two well-known evaluation scoring systems were used: the Cranley method and the Condon method [34-36] (Multimedia Appendix 1; Table S1). The Cranley scoring method has 24 items that measure 5 behavior domains: role taking, differentiation of self from fetus, interaction with the fetus, attributing characteristics to fetus, and giving of self [37,38]. The choices for participants are scored from 1 to 5; thus, the total score ranges from 24 to 120. The Condon scale has 19 items that describe maternal attitude to the fetus; they are divided into 2 subscales [39]. The first subscale measures the quality of attachment and includes subjective maternal experiences, such as pleasure from interaction with the fetus and conceptualization of the fetus as an individual. The intensity of attachment is the second subscale; this measures the amount of time devoted to activities involving the fetus, such as talking, thinking, and dreaming about the fetus.
Maternal depressive symptoms were assessed by questionnaires that are generally used to screen for postpartum depression disorder, because there are no specific tests to determine pregnancy-related depressive symptoms that develop in the antenatal period. In fact, it is recommended that most postpartum depression scales be used regularly for high-risk patients starting with antenatal check-ups, not only during the postpartum period [40,41]. The Edinburgh Postnatal Depression Scale (EPDS) and Postpartum Depression Screening Scale (PDSS) were selected to evaluate the presence of depressive symptoms or disorder status for all participants [42,43]. The EPDS is the most widely applied screening test for postpartum depression; it has 10 items that rate the degrees of emotion that childbearing women have felt in the previous 7 days [44,45] The PDSS was developed to signify the concept or definition of postpartum depression; it uses 35 items to judge qualitative degrees of sleeping disturbance, eating disturbance, anxiety, insecurity, emotional lability, cognitive impairment, loss of self, guilt, shame, and contemplating harming oneself [46]. The recommended cutoff scores for minor postpartum depression are 60 for the PDSS and 9 for the EPDS [47].
The fourth questionnaire, which was given to participants at the interim follow-up, was a self-report to determine how much the participants understood the appearance of the fetus (Multimedia Appendix 1; Figure S1). The questions were as follows: [1] “Do you understand and imagine how big the actual size of your baby?” [ 2] “Do you understand and imagine the lengths of your baby’s arms and legs?” and [3] “Do you understand and imagine the detailed appearance of your baby’s face?” The participants could respond “definitely no,” “very little,” “moderately,” “very much,” or “definitely yes.” The replies were scored numerically from 1 to 5.
## Statistical Analysis
Maternal obstetric information and the scores from all the questionnaires were compared in the groups. Continuous variables were analyzed with a 2-tailed Student t test and proportions were compared using the Fisher exact test. A P value <.05 was considered significant. All statistical analyses were performed using SPSS (version 25.0; IBM Corp).
## Results
Among 88 participants enrolled at the beginning of the study, 4 from each group were lost to follow-up; therefore, data from 80 participants were included and analyzed. Maternal baseline characteristics, such as age, parity, height, weight, education, and use of assisted reproductive technology for the conception, were comparable between the 2 groups (Table 1). The proportion of twin pregnancies was $30\%$ in the intervention group and $35\%$ in the control group. Other obstetric complications included preeclampsia/superimposed preeclampsia, chronic hypertension, gestational diabetes mellitus, pregestational diabetes mellitus, preterm labor, preterm premature rupture of membranes, short cervical length, oligohydramnios, fetal growth restriction, alleged myoma uteri, and underlying malignancy; these were not statistically different between the 2 groups.
**Table 1**
| Characteristics | Characteristics.1 | Intervention (n=40) | Control (n=40) | P value | P value.1 |
| --- | --- | --- | --- | --- | --- |
| Age (years), n (%) | Age (years), n (%) | 35 (3.9) | 34 (3.3) | .95 | .95 |
| Nulliparous, n (%) | Nulliparous, n (%) | 30 (75) | 30 (75) | >.99 | >.99 |
| Height (cm), mean (SD) | Height (cm), mean (SD) | 162.6 (5.7) | 160.9 (4.2) | .14 | .14 |
| Weight at delivery (kg), mean (SD) | Weight at delivery (kg), mean (SD) | 72.5 (9.6) | 70.2 (10.5) | .34 | .34 |
| BMI at delivery (kg/m2), mean (SD) | BMI at delivery (kg/m2), mean (SD) | 27.3 (3.0) | 27.1 (3.8) | .89 | .89 |
| Prepregnancy weight (kg), mean (SD) | Prepregnancy weight (kg), mean (SD) | 58.9 (7.8) | 57.1 (9.9) | .36 | .36 |
| Prepregnancy BMI (kg/m2), mean (SD) | Prepregnancy BMI (kg/m2), mean (SD) | 22.3 (2.7) | 22.0 (3.5) | .70 | .70 |
| Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | Education, n (%) | .81 |
| | High school graduate | 3 (7.5) | 1 (2.5) | | |
| | Bachelor’s degree | 28 (70) | 33 (82.5) | | |
| | Master’s degree or PhD | 9 (22.5) | 6 (15) | | |
| Assisted reproductive technology, n (%) | Assisted reproductive technology, n (%) | 15 (37.5) | 21 (52.5) | .26 | .26 |
| Twins, n (%) | Twins, n (%) | 12 (30) | 14 (35) | .81 | .81 |
| Preeclampsia/superimposed preeclampsia, n (%) | Preeclampsia/superimposed preeclampsia, n (%) | 3 (7.5) | 7 (17.5) | .31 | .31 |
| Chronic hypertension, n (%) | Chronic hypertension, n (%) | 1 (2.5) | 1 (2.5) | >.99 | >.99 |
| Gestational diabetes, n (%) | Gestational diabetes, n (%) | 4 (10) | 7 (17.5) | .52 | .52 |
| Pregestational diabetes mellitus, n (%) | Pregestational diabetes mellitus, n (%) | 0 (0) | 1 (2.5) | >.99 | >.99 |
| Use of insulin due to diabetic disorder, n (%) | Use of insulin due to diabetic disorder, n (%) | 1 (2.5) | 2 (5) | >.99 | >.99 |
| High risk for preterm births, n (%) | High risk for preterm births, n (%) | 8 (20) | 7 (17.5) | >.99 | >.99 |
| Preterm labor with use of tocolytics, n (%) | Preterm labor with use of tocolytics, n (%) | 6 (15) | 6 (15) | >.99 | >.99 |
| Preterm premature rupture of membranes, n (%) | Preterm premature rupture of membranes, n (%) | 1 (2.5) | 1 (2.5) | >.99 | >.99 |
| Short cervical length in midtrimester, n (%) | Short cervical length in midtrimester, n (%) | 6 (15) | 2 (5) | .26 | .26 |
| Oligohydramnios, n (%) | Oligohydramnios, n (%) | 2 (5) | 1 (2.5) | >.99 | >.99 |
| Fetal growth restriction, n (%) | Fetal growth restriction, n (%) | 1 (2.5) | 1 (2.5) | >.99 | >.99 |
| Alleged myoma uteri, n (%) | Alleged myoma uteri, n (%) | 5 (12.5) | 6 (15) | >.99 | >.99 |
| Underlying malignancy, n (%) | Underlying malignancy, n (%) | 1 (2.5) | 1 (2.5) | >.99 | >.99 |
Table 2 demonstrates the primary outcome of this study. The mean values for gestational age at each evaluation, including the initial baseline evaluation and the follow-up evaluations, were comparable between the 2 groups. The median interval between the baseline evaluation and the second follow-up was 5.8 (IQR 4.6-7.9) weeks. The total scores obtained with the Cranley method and the Condon method did not show a statistical difference at either baseline or the follow-ups; however, among changes from baseline to follow-up, the mean value of 1 subscale of the Cranley test, interaction with the fetus, showed a greater increase in the intervention group than the control group (0.4, SD 0.5 vs 0.1, SD 0.4; $$P \leq .004$$). Table 3 shows the proportions of participants who had increased scores at follow-up evaluations compared to the initial baseline results. The rate of participants with an increased score was $43\%$ for the intervention group, while it was $13\%$ for the control group ($$P \leq .005$$).
We also compared scores for depressive symptoms between the 2 groups (Table 4). The mean values of the EPDS test and the PDSS test at baseline and follow-up were comparable, and the rates of minor depression, determined by the recommended cutoffs, were also similar. The proportion of lower scores at the follow-up evaluation compared to baseline for the EPDS test was higher in the intervention group than in the control group; however, the difference did not reach statistical significance ($53\%$ vs $38\%$, $$P \leq .21$$). The interim questionnaire to evaluate how much the participants recognized the fetal appearance showed an increased proportions of participants with a high score (≥4) in the intervention group. The rate of participants with a high score did not change, or tended to decrease, in the control group (Multimedia Appendix 1, Figure S1).
**Table 4**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Intervention (n=40) | Intervention (n=40).1 | Control (n=40) | Control (n=40).1 | P value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Test results at initial baseline evaluation | Test results at initial baseline evaluation | Test results at initial baseline evaluation | Test results at initial baseline evaluation | Test results at initial baseline evaluation | Test results at initial baseline evaluation | Test results at initial baseline evaluation | Test results at initial baseline evaluation |
| | Gestational age at baseline evaluation (weeks), mean (SD) | 27.8 (2.5) | 27.8 (2.5) | 28.2 (3.5) | 28.2 (3.5) | .498 | .498 |
| | EPDSa score, mean (SD) | 6.8 (4.4) | 6.8 (4.4) | 6.1 (3.4) | 6.1 (3.4) | .43 | .43 |
| | EPDS score ≥10, n (%) | 7 (17.5) | 7 (17.5) | 5.40 (12.5) | 5.40 (12.5) | .76 | .76 |
| | PDSSb score, mean (SD) | 47.5 (11.4) | 47.5 (11.4) | 48.6 (10.2) | 48.6 (10.2) | .67 | .67 |
| | PDSS score >60, n (%) | 7 (17.5) | 7 (17.5) | 5 (12.5) | 5 (12.5) | .76 | .76 |
| Test results at follow-up evaluation | Test results at follow-up evaluation | Test results at follow-up evaluation | Test results at follow-up evaluation | Test results at follow-up evaluation | Test results at follow-up evaluation | Test results at follow-up evaluation | Test results at follow-up evaluation |
| | Gestational age at follow-up evaluation (weeks), mean (SD) | 33.3 (2.4) | 33.3 (2.4) | 33.9 (3.0) | 33.9 (3.0) | .40 | .40 |
| | EPDS score, mean (SD) | 6.8 (4.0) | 6.8 (4.0) | 6.3 (2.9) | 6.3 (2.9) | .57 | .57 |
| | EPDS score ≥10, n (%) | 8 (20) | 8 (20) | 6 (15) | 6 (15) | .77 | .77 |
| | Lower EPDS score than initial baseline evaluation, n (%) | 21 (52.5) | 21 (52.5) | 15 (37.5) | 15 (37.5) | .21 | .21 |
| | PDSS score, mean (SD) | 47.5 (7.8) | 47.5 (7.8) | 48.9 (7.8) | 48.9 (7.8) | .41 | .41 |
| | PDSS score >60, n (%) | 3 (7.5) | 3 (7.5) | 4 (10) | 4 (10) | >.99 | >.99 |
| | Lower PDSS score than initial baseline evaluation, n (%) | 18 (45) | 18 (45) | 17 (42.5) | 17 (42.5) | .59 | .59 |
## Principal Findings of the Study
All the participants experienced the prenatal-coaching mobile app, but only the intervention group experienced the VR images. The scores measuring maternal interaction with the fetus showed a significantly greater increase in the intervention group than the control group at follow-up (0.4 vs 0.1). The proportion of participants with increased scores at the follow-up evaluation was over 3 times greater in the intervention group than the control group ($43\%$ vs $13\%$). The intervention group seemed to show improvement in depressive-symptom test scores at the follow-up evaluation, although the difference was not statistically significant ($53\%$ vs $38\%$).
## The Effect of Digital Technologies in Pregnancy
In this study, all participants experienced the mobile prenatal care app, which provided information about what the pregnant women should do and eat. Commercially available apps also provide information on symptoms or signs that pregnant women might feel in each gestational period. The participants were instructed to use the specific prenatal-coaching app that was developed for the trial, but most were already using one or more mobile apps to obtain information about their pregnancy and to record their own diaries or pictures of the fetus. During the pandemic era, the role of digital technology has grown due to the decrease in direct contact, and this has inspired the development of numerous mobile apps for pregnant women to take care of themselves without direct visits to a clinic. Marko et al [48] performed a prospective controlled trial and demonstrated that a mobile prenatal-care app reduced the number of in-person visits without decreasing patient satisfaction. Many prenatal mobile apps contain similar information about routine prenatal tests and currently have additional functions, such as coaching for blood sugar control in patients with gestational diabetes, thereby contributing to improving society by improving the lives of pregnant women and even helping their mental health [49-55].
Taking things further, the digital economy in the postpandemic era has focused on the application of VR to medical fields to produce consistent and easily accessible environments for the management of many disease entities [56,57]. There have been a few intriguing studies involving VR in obstetrics. Wong et al [58] reported that VR was effective in reducing labor pain in a randomized controlled trial. Williams et al [59] demonstrated the application of VR to train midwifery students in emergency skills such as neonatal resuscitation. One pilot study reported that immersive VR reduced anxiety in patients undergoing first-trimester surgical termination [60]. Since ultrasound is the most common tool in obstetrics, VR has been developed based on fetal ultrasounds, and the effectiveness for diagnosing fetal structural anomalies has been tested by several groups [61,62]. Grayscale ultrasound images of the fetus give important information to doctors, but pregnant women and their family members might have difficulty recognizing the face or body parts of the fetus. 3D ultrasounds help people understand the features of the fetus much better than grayscale images. Indeed, when 3D images are combined with VR, the images are far more realistic. Because VR images come from measured ultrasonography data, the fetus can be seen to grow and change in shape at each ultrasonographic examination, and the pregnant woman can easily recognize the differences.
## Clinical Implications
We wanted to determine how fetal images generated in VR can affect maternal-fetal bonding in pregnant women and investigated the possibility that they might lower maternal anxiety or depressive symptoms. This randomized controlled trial showed that there was a significantly greater increase in measured scores of maternal interaction with the fetus in the VR group than the control group. Additionally, although the difference did not reach statistical significance, about half the VR group showed an improvement in depressive-symptom test scores. Cranley [37] defined MFA as “the extent to which women engage in behaviors that represent an affiliation and interaction with their unborn child”; thus, the concept could be considered slightly obscure from a medical perspective. Yarcheski et al [63] performed a meta-analytic study of the predictors of MFA and suggested that gestational age was positively related to increased MFA. Other predicting or associated factors that have been studied are social support for the mother; the mother’s own anxiety, self-esteem, and underlying depression; prenatal test results; whether the pregnancy was planned; maternal age; parity; income; education; and marital status [64-66].
Although MFA could be considered an abstract concept, the association between MFA and postpartum depression has been previously studied. Rollè et al [67] reviewed the literature on the relationship between MFA and perinatal depression and found that lower MFA was related to higher rates of postnatal depressive symptoms, although some of the studies in that review reported controversial or nonsignificant results. Rollè emphasized that the development of strong MFA should be encouraged during pregnancy to reduce postpartum depression and increase the psychological well-being of expectant parents. Modalities to increase healthy and positive MFA should be identified if MFA is to help prevent maternal depressive symptoms, since postpartum depression is a serious psychiatric condition after childbirth that can result in abnormal parenting behavior, negative maternal bonding, child abuse, adverse mental development in childhood, and even maternal suicide [68]. Seimyr et al [69] reported that physical contact with the fetus and sensitivity to fetal movements decreased maternal depression. In fact, an experimental study revealed that watching the fetus in 3D ultrasound images and attempts to touch the fetus in VR decreased maternal stress, as measured by salivary cortisol concentration [70]. Much research has revealed that maternal-fetal bonding increases when pregnant women experience better and more realistic images [71]. Pulliainen et al [72] performed a qualitative pilot study with pregnant women at high risk for preterm birth and showed that interactive 3D ultrasounds shown at the request of the pregnant women increased MFA. A randomized controlled trial of a 4D ultrasound intervention among pregnant women with substance use demonstrated a higher retention rate, as well as enhanced MFA, in the intervention group compared to the control group [73].
## Conclusion
To our knowledge, there are no prior studies demonstrating the effect of VR on maternal-fetal interaction and other aspects of maternal mental status. Fetal images generated in VR seem to promote positive MFA and might reduce maternal depressive symptoms. In this study, we used a prenatal-coaching mobile app to maximize access to fetal images produced with a combination of VR and ultrasound. Since we examined very short-term changes after experiencing VR, future studies are essential to determine the long-term perinatal outcomes of using VR as a modality to increase MFA and to promote mental health during pregnancy.
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|
---
title: Antiphotoaging and Skin-Protective Activities of Ardisia silvestris Ethanol
Extract in Human Keratinocytes
authors:
- Lei Huang
- Long You
- Nur Aziz
- Seung Hui Yu
- Jong Sub Lee
- Eui Su Choung
- Van Dung Luong
- Mi-Jeong Jeon
- Moonsuk Hur
- Sarah Lee
- Byoung-Hee Lee
- Han Gyung Kim
- Jae Youl Cho
journal: Plants
year: 2023
pmcid: PMC10007040
doi: 10.3390/plants12051167
license: CC BY 4.0
---
# Antiphotoaging and Skin-Protective Activities of Ardisia silvestris Ethanol Extract in Human Keratinocytes
## Abstract
Ardisia silvestris is a traditional medicinal herb used in Vietnam and several other countries. However, the skin-protective properties of A. silvestris ethanol extract (As-EE) have not been evaluated. Human keratinocytes form the outermost barrier of the skin and are the main target of ultraviolet (UV) radiation. UV exposure causes skin photoaging via the production of reactive oxygen species. Protection from photoaging is thus a key component of dermatological and cosmetic products. In this research, we found that As-EE can prevent UV-induced skin aging and cell death as well as enhance the barrier effect of the skin. First, the radical-scavenging ability of As-EE was checked using DPPH, ABTS, TPC, CUPRAC, and FRAP assays, and a 3-(4-5-dimethylthiazol-2-yl)-2-5-diphenyltetrazolium bromide assay was used to examine cytotoxicity. *Reporter* gene assays were used to determine the doses that affect skin-barrier-related genes. A luciferase assay was used to identify possible transcription factors. The anti-photoaging mechanism of As-EE was investigated by determining correlated signaling pathways using immunoblotting analyses. As-EE had no harmful effects on HaCaT cells, according to our findings, and As-EE revealed moderate radical-scavenging ability. With high-performance liquid chromatography (HPLC) analysis, rutin was found to be one of the major components. In addition, As-EE enhanced the expression levels of hyaluronic acid synthase-1 and occludin in HaCaT cells. Moreover, As-EE dose-dependently up-regulated the production of occludin and transglutaminase-1 after suppression caused by UVB blocking the activator protein-1 signaling pathway, in particular, the extracellular response kinase and c-Jun N-terminal kinase. Our findings suggest that As-EE may have anti-photoaging effects by regulating mitogen-activated protein kinase, which is good news for the cosmetics and dermatology sectors.
## 1. Introduction
The greatest organ in the human body and the interaction with the outside world is the skin. The skin serves as a barrier that protects against infections, physical and chemical harm, and uncontrolled water loss [1]. It is made up of the epidermis, dermis, and subcutis, three separate layers [2]. Ninety percent of the epidermis, which is the skin’s outermost layer, is made up of keratinocytes [3]. The stratum corneum (SC), which is the epidermis’ outermost layer and the first line of defense, plays a crucial role in maintaining the integrity of the skin barrier as well as the skin’s suppleness [4,5]. Several studies have reported increased hydration of the SC and improved epidermal barrier function, and thus have potential applications in moisturizing, protective, and anti-aging cosmetics [6,7,8].
Skin aging is a multifactorial process that causes functional and cosmetic changes in the skin. Unlike other organs of the human body, the skin is impacted not only by the intrinsic aging process, but also by several extrinsic environmental elements that accelerate aging, particularly ultraviolet (UV) radiation. The primary environmental element causing early skin aging is UV exposure (photoaging). Based on the amount of sun exposure and the quantity of skin pigment, UV irradiation causes human skin to age over time [9,10]. UV energy can be subdivided into UVA, -B, and -C components based on electrophysical properties, with UVC photons having the shortest wavelengths (100–280 nm) and the greatest amount of energy, UVB falling between the two, and UVA having the longest wavelengths (315–400 nm) but the least-energetic photons. Each UV component has different potential impacts on molecules, cells, and tissues [11]. Because UVB radiation is directly absorbed by DNA and is known to produce cyclobutane pyrimidine dimers and 6-4 pyrimidine pyrimidone dimers, it is predominantly a DNA-damaging agent [12,13,14]. Unrepaired DNA lesions result in DNA mutation during cell division, which may initiate carcinogenesis [15]. UV rays from the sun damage keratinocytes and fibroblasts at the molecular level, activating cell surface receptors that start signal transduction cascades [16]. Solar UV radiation activates a protein from the serine/threonine protein kinase family that is linked to cellular signaling, namely the mitogen-activated protein kinase (MAPK) pathway. *In* general, the MAPK pathways are divided into three distinct pathways: c-Jun NH2-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and p38 MAPK (p38 kinase). The ERK cascade promotes cell proliferation and survival, whereas the other two pathways (JNK and p38 kinase) protect and promote apoptosis, respectively [17,18,19]. By targeting distinct intracellular proteins, each member of the serine/threonine protein kinase family generates a different stimulus or cellular stress. ERK activation is normally caused by UVA-mediated reactive oxygen species (ROS), whereas JNK is mostly activated by UVC, and p38 kinases may be activated by all UV wavelengths (including UVA, UVB, and UVC) to modulate DNA damage response [20].
Nearly all eukaryotic cells produce ROS and regulate various physiological processes. However, excessive ROS can cause tissue malfunction and oxidative damage by changing the structural and functional characteristics of cellular constituents such proteins, lipids, and nucleic acids [21,22,23]. Our skin is shielded from harm by the antioxidant defense system, such as UV radiation. ROS, which are brought on by oxidative stress or UV light, accelerate the aging and wrinkle-causing processes in the skin. ROS overproduction initiates internal cellular apoptosis or programmed cell death [24,25,26]. Apoptosis is supposed to accelerate aging or diseases associated with aging. For these reasons, consuming antioxidants to eradicate free radicals is one strategy to maintain healthy skin or stop aging [27,28,29,30]. According to the theory of free radical aging (FRTA) [31], one of the primary causes of aging and diseases associated with aging is the buildup of oxidative stress brought on by ROS [32,33,34]. Even at relatively low concentrations compared to the levels of oxidizable substrates, antioxidants are chemicals that can significantly slow down or block the oxidation of oxidizable substrates [32,35,36]. Although organisms contain defense mechanisms to counteract the oxidative effects of ROS, these mechanisms must be supported by exogenous antioxidants when the balance between ROS formation and antioxidant systems is aberrant [37,38,39].
As the quality of life improves, photoaging has been treated through diet, hormone therapy, and probiotics. However, in recent years, there has been a growing interest in natural herbal cosmetics to combat photoaging, and various botanical extracts have been launched that claim to reduce skin aging and enhance skin health [40,41,42,43,44]. Ardisia silvestris was received from the National Institute of Biological Resources (Ministry of Environment, Incheon, Korea). The leaves of As-EE were collected from Vietnam on 30 August 2016. As-EE is a common plant with a wide distribution, especially in Vietnam. However, its properties have yet to be validated scientifically. Until now no studies have shown that As-EE has skin-protective functions. The goal of this study was to assess the anti-aging activity of As-EE and to investigate its skin-protective functions in terms of anti-oxidant capacity, anti-apoptosis, and moisturizing effects under UVB irradiation. This study used cosmetological and pharmacological procedures such as antioxidant assays, mRNA preparation, reverse transcription polymerase chain reaction, immunoblotting analysis, and PI/Annexin V staining with FACS to investigate the potential usefulness of As-EE in skin care. Since we have very promising results on As-EE’s beneficial role in skin, this extract can be applied to the development of cosmeceutical preparations.
## 2.1. Effects of As-EE on Antioxidative Capacity
The anti-oxidation activity of As-EE was examined using the 2,2-di(4-tert-octylphenyl)-1-picrylhydrazyl (DPPH) assay, 2,2′-Azinobis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assay, cupric ion reducing antioxidant capacity (CUPRAC) assay, and ferric-reducing antioxidant power (FRAP) assay. Its inhibitory concentration of $50\%$ (IC50) values for DPPH and ABTS were 46 µg/mL and 13 µg/mL. First, DPPH tests were utilized to assess the ability of natural components to scavenge free radicals [45]. Stable DPPH• free radicals in the DPPH assay lose their purple hue following reduction. At concentrations of 3.125–200 µg/mL, As-EE scavenged DPPH• radicals in a dose-dependent manner, and it began to exhibit considerable DPPH radical-scavenging action at 12.5 µg/mL (Figure 1a). The ABTS assay was examined to ascertain the anti-oxidative-stress effect of As-EE. As potassium persulfate or manganese dioxide oxidize ABTS, they produce bluish-green ABTS•+ radicals that become less pigmented when they are reduced by antioxidants [46]. The ABTS-radical-scavenging activity was inhibited dose-dependently by As-EE at a concentration of 3.125~200 µg/mL. The antiradical activity of As-EE at a concentration of 12.5 µg/mL was similar to that of ascorbic acid used as a positive control (Figure 1b). Similar to the FRAP assay, the CUPRAC assay uses metal ions, except *Cu is* used in place of Fe. At 450 nm, the hue changes from light blue to orange-yellow when Cu (II) is reduced to Cu (I) by a reducing agent [46]. Cu ions were dose-dependently decreased by As-EE at concentrations from 50–200 µg/mL, which is readable at 450 nm, as shown in Figure 1c; however, the effect was significantly less than that of trolox 0.4 mM. The FRAP assay is based on the idea that antioxidants cause colorless Fe3+-TPTZ to be converted to intensely blue Fe2+-TPTZ, which can be read at 593 nm. Trolox (Sigma, St. Louis, MO, USA) was employed as the antioxidant standard at a total concentration of 0.4 mM [46]. We found that the ferric-reducing antioxidative capacity was reduced by As-EE dose-dependently (Figure 1d). The components of As-EE were analyzed with high-performance liquid chromatography (HPLC) (Figure 1e–m). Rutin, quercetin, hesperidin, and kaempferol were used as standard compounds for HPLC analysis (Figure 1e,h,k). As shown in Figure 1f, rutin and quercetin were detected in As-EE, while hesperidin and kaempferol were not identified. The contents of rutin and quercetin were calculated to be 0.53 and 0.03 mg/g, respectively, using standard area curves of these compounds (data not shown). The conducive pharmacological activities of these components, such as anti-inflammatory and anti-oxidative effects, have been reported previously [47,48,49,50], implying that As-EE has potential in preventing UVB-induced skin damage. We also determined the As-EE’s potential as an antioxidant by checking its total phenolic content (TPC), then we evaluated the As-EE’s antioxidative activity. In terms of gallic acid equivalenst, the TPC is 140 ± 0.02 mg per g of As-EE [51].
## 2.2. Effects of As-EE on Cell Viability and Skin Moisture Protection Activity
Before measuring the skin-protective activity of As-EE in HaCaT cells (human keratinocytes), we evaluated the cytotoxicity of As-EE using the MTT assay. The cell viability results showed that As-EE did not induce the cell death of HaCaT cells (Figure 2a,b). To determine the skin-protective and skin-hydration efficacies of As-EE, the mRNA expression levels of occludin and HAS-3 were detected with reverse transcription-polymerase chain reaction (RT-PCR). As shown in Figure 2c,d, the mRNA expression levels of occludin and HAS-3 were significantly increased by 100 µg/mL of As-EE. To identify the As-EE-dependent signaling pathway, RT-PCR was used to detect the mRNA expression of occludin, transglutaminase (TGM)-1, and HAS-1 in cells treated with inhibitors of MAPK such as SB203580 (a p38 inhibitor), SP600125 (a JNK inhibitor), U0126 (an ERK inhibitor), and Bay117082 (an inhibitor of kappa B kinase (IKK) inhibitor). The mRNA expression of occludin, which was increased by As-EE, was suppressed by SP600125. However, the mRNA expression of TGM-1 and HAS-1 was not reduced by SP600125 in As-EE-treated HaCaT cells (Figure 2e). As-EE increased the expression of epidermal barrier and hydration genes through modulating the expression of occludin via the JNK-dependent signaling pathway.
## 2.3. Effects of As-EE on MAPK-Mediated AP-1 and CREB Signaling Pathway
Occludin is known to be regulated by transcription factors [52]. To investigate the regulator of HAS1 and HAS3, we evaluated the effect of As-EE on the transcriptional activity of CREB (Figure 3a). The results showed that As-EE increased CREB-mediated luciferase activity and the phosphorylation of CREB (Figure 3b). The phosphorylated levels of c-Jun, c-Fos, and JNK were also increased in HaCaT cells treated with As-EE (Figure 3c). Vitamin E (tocopherol) is widely recognized as a potent antioxidant commonly used in topical skin care products [53,54]. Despite its widespread use as a lipid-soluble antioxidant, few studies have examined the moisturizing effects of vitamin E on the skin. We checked the gene expression levels of hydration factors and the skin barrier using RT-PCR with different concentrations of vitamin E. As shown in Figure 3d, we found that mRNA levels of these factors were significantly increased by treatment with 12.5 μM vitamin E. Moreover, CREB-mediated luciferase activity was activated by As-EE in a dose-dependent manner (Figure 3e. In addition, the level of p-JNK was also enhanced in a dose-dependent manner from 0 to 12.5 µM, with the JNK total form invariant (Figure 3f). Thus, As-EE induced the transcriptional factor CREB through the c-Jun, c-Fos, and JNK-dependent signaling pathways.
## 2.4. Moisturizing and Anti-Apoptotic Effect of As-EE in UVB-Irradiated Human Keratinocytes
Previous reports suggest that the ability of UVB radiation to impair the skin immune system has been widely documented, and UVB-induced damage is a key factor in the development of sun-induced skin cancer [55,56]. To explore the potential ability of As-EE in protecting keratinocytes against UVB irradiation, the morphological changes in As-EE-treated HaCaT cells stimulated with UVB were detected using phase-contrast microscopy. Expectedly, the number of floating dead cells was reduced in the As-EE-pre-treated group (Figure 4a). To identify whether the viability of cells is reduced by UVB, cell viability was analyzed with an MTT assay. The cell death that is induced by UVB was inhibited by treatment with As-EE (50~100 µg/mL) (Figure 4b). Apoptosis is a well-known mode of programmed cell death that occurs in multicellular organisms and is used not only to control tissue homeostasis but also to remove severely damaged cells and to protect from the excess growth of abnormal cells in cancer in the epidermis of human skin, which consists mainly of keratinocytes and is constantly renewed. Thus, keratinocyte apoptosis plays a crucial role in the maintenance of epidermal structure and function. However, regulated cell death may be perturbed by environmental factors, particularly UVB, leading to sunburn (keratinocytes undergo UVB-induced apoptosis) and impairing skin integrity. In this study, we suggested the potential of As-EE to modulate UVB-induced apoptosis in human keratinocytes. To investigate the effects of As-EE on cell death in HaCaT cells further, propidium iodide (PI)–annexin V staining and FACS were used. Although UVB radiation caused cell death in HaCaT cells, pre-treatment with As-EE reduced cell death from $58\%$ to $45\%$, as seen in Figure 4c. To examine whether As-EE also played a role in preserving moisture levels in human keratinocytes under UVB irradiation, the mRNA levels of skin barrier factors were determined using RT-PCR. The results showed that As-EE heightened the expression of occludin and TGM-1 in a dose-dependent manner. Especially when treated with 75 µg/mL and 100 µg/mL of As-EE, the gene expression levels recovered remarkably (Figure 4d,e). Moreover, we investigated the protein levels of UVB-mediated conditions in HaCaT cells using immunoblotting. We found that As-EE down-regulated the phosphorylation of ERK and CREB (Figure 4f–i). This indicated that As-EE repaired cell damage from UVB exposure. Finally, we treated human keratinocyte cells with 20 μM of three inhibitors, SB203580 (p38 inhibitor), SP600125 (JNK inhibitor), and U0126 (ERK inhibitor), related to the AP-1 pathway under UVB irradiation. We found that inhibitors of ERK and JNK notably extricated the expression of occludin and TGM-1, which was inhibited by UVB irradiation (Figure 4j,k). To sum up, these findings indicated that the JNK and ERK signaling pathways predominantly contribute to the moisture-retaining capacity). This implies that As-EE ameliorates the damage to skin after exposure to UVB by increasing the expression of JNK and CREB.
Here, we have identified that As-EE can lead to changes in biomarkers linked to skin hydration and prevent the photodamage of keratinocytes from UVB irradiation by restraining the JNK/ERK/AP-1 and CREB pathways, as well as by inhibiting apoptosis (Figure 4).
## 3. Discussion
The skin not only defends the body from environmental change and pathogenic infection, but it also inhibits moisture loss, allowing homeostasis to be maintained. Skin aging is induced by internal and external factors. The loss of moisture represents an internal factor of skin aging and follows a natural course through a reduction in the regulation of hyaluronan synthesis or the loss of keratinocyte tight junctions [57,58,59]. Here, we have identified factors that increase moisturization, including occludin, which is a regulator of tight junctions, and HAS-1, which regulates hyaluronan synthase. Vitamin E is used heavily worldwide to protect cells from oxidative stress and aging [60] and is an essential nutrient and a powerful antioxidant. It is a fat-soluble vitamin that occurs naturally in eight forms. Vitamin E can be divided into two principal classes: tocopherols and tocotrienols. These can be further categorized into slightly different compounds, known as alpha, beta, delta, and gamma [61]. Despite its widespread use as a lipid-soluble antioxidant, few studies have examined the moisturizing effects of vitamin E on the skin. Therefore, we also determined the protective activity of vitamin E on skin moisturization.
The evidence indicates that UVB induces acute and chronic skin problems, such as dehydration, and generates reactive oxygen species that progress skin aging [62]. Currently, there is an increased interest in skin health and natural products that prevent photoaging or are involved in skin protection [54]. In fact, clinical researchers are also trying to find new treatments for anti-oxidation and anti-photoaging. In previous reports, flavonoids or phenolics that are abundant in extracts of plants are related to antioxidant ability, and they are even considered an indispensable ingredient in various nutritional, pharmaceutical, and cosmetic applications [63,64,65,66]. In this study, we investigated the potential skin-protective functions of As-EE by evaluating the expression of genes related to antioxidant and moisturizing capacity. As shown in Figure 1, the ability of As-EE to reduce free radical levels in cell-free systems was confirmed using ABTS, DPPH, FRAP, and CUPRAC assays. Based on the IC50 values (46 µg/mL and 13 µg/mL) of As-EE for DPPH and ABTS, it is assumed that this plant can have a higher antioxidative activity than other plants such as Malus baccata, Canarium subulatum, Licania macrocarpa, Atriplex halimus, and Euphorbia resinifera, with IC50 values of 50 to 200 μg/mL [67,68,69,70,71,72,73,74,75,76]. The contents of rutin and quercetin were calculated to be 0.53 and $0.03\%$, respectively, using standard area curves of these compounds (data not shown). These results suggest that the antioxidative property of As-EE can be beneficial as major pharmacological activities and rutin and quercetin can be considered as active components in As-EE.
An emerging study has shown that MAPK was activated in UV-induced signal transduction [77]. We found that As-EE has restorative effects on UVB-induced skin damage by reducing ERK$\frac{1}{2}$, not just in antioxidant effects. Furthermore, ERK$\frac{1}{2}$ directly interacts with occludin and can activate TGMs [78,79]. Occludin has a critical role in protecting the skin barrier by maintaining tight junctions in cell–cell junctions from the irradiation of UVB [80]. Our data have demonstrated that As-EE improved skin-hydrating effects by elevating moisturizing factors, including occludin and TGM-1, that were inhibited by UVB. After HaCaT cells were treated with As-EE, we found that the expression of occludin and TGM-1 was enhanced in a dose-dependent manner. In addition, As-EE can dramatically restore the levels of occludin and TGM-1 under UVB irradiation, showing that As-EE can recover skin water loss caused by UVB. These results suggest that As-EE might be a feasible anti-aging ingredient in future cosmetics.
Moreover, previous research has demonstrated that oxidative stress also induces apoptotic cell death [81]. Owing to As-EE antioxidant effects in UVB-exposed human keratinocytes, we determined the anti-apoptotic ability of As-EE in the same cell line. We analyzed cell death using propidium iodide (PI)-Annexin V staining. As expected, As-EE markedly reduced the level of apoptosis in UVB-irradiated HaCaT cells. These results forcefully suggest that As-EE has a protective activity against UVB-induced apoptotic death in human keratinocytes. These results suggest that As-EE can be a feasible anti-aging ingredient in cosmetics.
Which compounds can mediate the anti-photoaging activity of As-EE was not elucidated yet. However, based on previously published papers, rutin seems to work as a major active ingredient in As-EE. Numerous papers regarding the role of rutin in photoaging effects have been published so far. For example, rutin was reported to be cytoprotective against oxidative stress in skin fibroblasts and cytotoxic conditions under UVB exposure [82,83]. Solid lipid nanoparticles containing rutin showed an efficient protective activity against UVB-induced cell death, lipid peroxidation, and metalloproteinase formation [84]. Similar photoprotective activities of apple, Satureja hortensis, and *Aronia melanocarpa* were also found to be mediated by its ingredient rutin [85,86,87]. Therefore, these reports raise a possibility that the anti-photoaging activity of As-EE could be in part mediated by rutin. To verify this possibility, further study will be continued with rutin.
## 4.1. Materials
Ethanol extract [$70\%$ (w/v)] of the leaves of A. silvestris (Ac-EE) was obtained from the National Institute of Biological Resources (Ministry of Environment, Incheon, Korea). Gallic acid, anhydrous sodium acetate, glacial acetic acid, Folin and Ciocalteu’s phenol reagent, 1,1-diphenyl-2 picrylhydrazyl radical (DPPH), ethanol, L-ascorbic acid, 2,20-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), potassium persulfate, acetic acid buffer, CuCl2·2H2O, NH4Ac, neocuproine, 2,4,6-tri(2-pyridyl)-s-triazine (TPTZ), FeCl3·6H2O, dimethyl sulfoxide (DMSO), trolox, LiChrosolv® water for chromatography (LC-MS Grade), DL-α-tocopherol acetate, the four inhibitors [SB203580 (p38 inhibitor), SP600125 (JNK inhibitor), U0126 (ERK inhibitor) and Bay117082 (inhibitor of κB kinase)], polyethylenimine (PEI), and bovine serum albumin (BSA), were obtained from Sigma (St. Louis, MO, USA). ( 3-4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) was obtained from Amresco (Brisbane, Australia). HaCaT cells (human keratinocyte cell line) and HEK293T (human embryonic kidney cell line) cells were purchased from the American Type Culture Collection (Rockville, MD, USA). Dulbecco’s Modified Eagle’s Medium (DMEM), antibiotics (Penicillin–Streptomycin Solution), and trypsin $0.25\%$ (1X) solution were purchased from Cytiva HyClone (Logan, UT, USA). Fetal bovine serum (FBS) was purchased from Gibco (Grand Island, NY, USA). The 1X phosphate buffered saline (PBS) was procured from Samchun Pure Chemical Co. (Gyeonggi-do, Korea). TRIzol reagent was bought from Molecular Research Center, Inc. (Cincinnati, OH, USA). The cDNA synthesis kits were obtained from Thermo Fisher Scientific (Waltham, MA, USA). The primers for polymerase chain reaction (PCR) were synthesized by Macrogen (Seoul, Korea) and reverse transcription polymerase chain reaction (RT-PCR) premix was purchased from Bop-D Inc. (Seoul, Korea). The luciferase reporter assay system kit was bought from Promega (Madison, WI, USA). The 3 MM CHR was bought from Whatman GE Healthcare Life Sciences. Polyvinylidene fluoride (PVDF) membranes were purchased from Merck Millipore (Burlington, MA, USA) and the Western blot detection kit was bought from ATTO CORPORATION. Some total and phosphor-forms of antibodies for Western blotting specific for each target protein were purchased from either Cell Signaling Technology (Beverly, MA, USA) or Santa Cruz Biotechnology (Santa Cruz, CA, USA). The UVB lamp Bio-link crosslinker BLX-312 was purchased from Vilber Lourmat, Collegien, France. The FITC-Annexin V Apoptosis Detection Kit I was obtained from BD Bioscience (San Jose, CA, USA).
## 4.2. Prepartion of As-EE and HPLC Analysis
The leaves of A. silvestris identified by Dr. Van Dung Luong (Dalat University, Vietnam) were collected from Vietnam on 30 August 2016. A voucher specimen (#501) was deposited in the herbarium of the National Institute of Biological Resources. The dried leaves of A. silvestris were pulverized and extracted with $70\%$ ethanol at room temperature. The ethanol could completely evaporate because the extract was filtered and concentrated in vacuo at 40 °C. The leftover aqueous solution was vacuum-concentrated and freeze-dried [88]. The phytochemical characteristics of As-EE were determined with HPLC as before [89]. For analysis, a system equipped with a KNAUER (Wellchrom) HPLC-pump K-1001, a Wellchrom high-speed scanning spectrophotometer K-2600, a four-channel deaerator K-500, and a Phenomenex Gemini C18 ODS (5 µm) column was used [90,91,92]. Solvent A ($0.1\%$ H3PO4 in H2O) and solvent B (acetonitrile) were used as elution solvents. Rutin, quercetin, hesperidin, and kaempferol were used as standard compounds for HPLC (Figure 1e–m).
## 4.3. Determination of Total Phenolic Content
The total phenolic content (TPC) of the As-EE was measured using Folin and Ciocalteu’s phenol (FC) reagent according to the method of Ali Ghasemzadeh et al. with some modification [93]. A 100 µL volume of As-EE (0–200 µg/mL, previously prepared) dissolved in DMSO or gallic acid (0–500 µg/mL) was dissolved in distilled or deionized water. Then 300 µL of distilled or deionized water and 100 µL of $10\%$ (v/v) Folin and Ciocalteu’s phenol (FC) reagent were added in E-tube. After 5 min of incubation at room temperature, 500 µL of distilled or deionized water and 500 µL of $8\%$ (w/v) sodium carbonate were mixed. After 30 min of incubation at room temperature, using a spectrophotometer, the absorbance of each fraction was measured at 765 nm after 200 µL of the mixture had been poured into a 96-well plate (BioTek Instruments Inc., Winooski, VT, USA). The total phenolic content (TPC) is given as mg of gallic acid equivalent/g of As-EE, and in this method, gallic acid was used as a reference standard ($y = 0.0032$x + 0.0481, R2 = 0.999).
## 4.4. DPPH Assay
DPPH is a method for predicting antioxidant activity. The DPPH-radical-scavenging ability can be used to identify the free-radical-scavenging ability. To determine the oxidant-scavenging capacity of As-EE, a DPPH decolorimetric assay was conducted [94]. First, DPPH (Sigma, St. Louis, MO, USA) was dissolved in methanol and configured into 3 mM stocks, L-ascorbic acid (100 mM) was dissolved in PBS (Samchun Pure Chemical Co. Gyeonggi-do, Korea), and As-EE (100 mg/mL) was dissolved in DMSO separately [95]. Next, the DPPH stock solution was diluted with methanol to 250 µM, L-ascorbic acid was diluted to 50 µM, and As-EE was serial diluted from 200 µg/mL to 0 µg/mL. These mixtures were incubated with foil at 37 °C for 30 min and then the absorbance was measured at 517 nm using a spectrophotometer (BioTek Instruments Inc., Winooski, VT, USA). The percentage of inhibition for DPPH scavenging was estimated as follows: DPPH scavenging effect (%) = [(A0 − A1)/A0] × 100 in which A0 is the absorbance of DPPH and A1 is the absorbance of the sample (As-EE or L-ascorbic acid).
## 4.5. ABTS Assay
Another technique for assessing antioxidant-scavenging properties is the ABTS-radical-scavenging assay. First, ABTS and potassium persulfate (K2S2O8) were taken using a chemical balance. ABTS was dissolved in PBS and potassium persulfate was dissolved in acetic acid buffer solution. Then, 7.4 mM ABTS and mixed with 2.4mM potassium persulfate in a ratio of 1:1. After incubating the solution for 30 min at 37 °C in the dark, we checked if the solution color changed to dark green. In a 96-well plate, the ABTS solution and As-EE (0–200 µg/mL) were mixed at a 1:1 ratio. A positive control was utilized, which was L-ascorbic acid (50 µM). The mixture was covered with foil and incubated once again at 37 °C for 30 min in an incubator (Thermo Fisher Scientific, Waltham, MA, USA) [76]. Using a spectrophotometer, the absorbance of each fraction was measured at 730 nm after 30 min of incubation at 37 °C (BioTek Instruments Inc., Winooski, VT, USA). The following percentage was computed for the ABTS0-scavenging effect: Assuming that A0 is the absorbance of ABTS and A1 is the absorbance of the sample, the ABTS-scavenging effect (%) is calculated as [(A0 − A1)/A0] × 100. ( As-EE or L-ascorbic acid).
## 4.6. Cupric Ion Reducing Antioxidant Capacity (CUPRAC) Assay
The CUPRAC assay is a redox reduction between the CUPRAC reagent and the antioxidants with a leading thiol group (for example, glutathione) present in the sample. In this process, the reagent reduces itself, forming a chelate complex of copper (I)-neocuproine, which provides a color measurable at 450 nm [96]. First, CuCl2⋅2H2O was dissolved with distilled or deionized water to make a copper (II) chloride solution at a concentration of 10 mM. Ammonium acetate (NH4Ac) was taken using a chemical balance and dissolved in distilled or deionized water to prepare NH4Ac buffer at pH 7.0. Neocuproine (Nc) was dissolved in pure EtOH to make a neocuproine solution at a concentration of 7.5 mM. Then, the copper(II) chloride solution, neocuproine solution, and NH4Ac buffer were mixed in a 15 mL conical tube at a ratio of 1:1:1 (v/v/v). Next, 600 µL of the mixture and 200 µL of As-EE (0–200 µg/mL, previously prepared) were put into 1.5 mL E-tubes. A 200 µL volume of the reaction solution was added into a 96-well plate. Trolox (0.4 mM) was used as a positive control. After incubation for 1 h, a spectrophotometer was used to measure the absorbance of each fraction at 450 nm (BioTek Instruments Inc., Winooski, VT, USA).
## 4.7. Ferric-Reducing Antioxidant Power (FRAP) Assay
First, anhydrous sodium acetate and glacial acetic acid were taken using a chemical balance and dissolved with distilled or deionized water to make acetate acid buffer (pH 3.6) at a concentration of 300 mM. TPTZ was dissolved in distilled or deionized water, and concentrated hydrochloric acid was added to make a TPTZ solution at a concentration of 10 mM. FeCl3·6H2O was dissolved with distilled or deionized water to make a FeCl3 solution at a concentration of 20 mM. Then, the acetic acid buffer, TPTZ solution, and FeCl3 solution were mixed according to the ratio 10:1:1 (v/v/v). A 100 µL volume of the As-EE solution (0–200 µg/mL) was added. In a 96-well plate, 100 µL of FRAP working solution was added and the plate was shaken well. Additionally employed as a positive control was trolox (0.4 mM). Using a spectrophotometer, the absorbance of each fraction was measured at 593 nm after 15 min of incubation in the dark at 37 °C (BioTek Instruments Inc., Winooski, VT, USA).
## 4.8. Cell Culture
HaCaT cells (human keratinocyte cell line) were cultured in DMEM supplemented with $1\%$ (v/v) penicillin–streptomycin and $10\%$ (v/v) FBS. HEK293T (human embryonic kidney cell line) cells were cultured in DMEM with $1\%$ (v/v) penicillin–streptomycin and $5\%$ v/v FBS. All cells were grown in an incubator with $5\%$ CO2 humidity (Thermo Fisher Scientific, Waltham, MA, USA) at 37 °C. To maintain fresh cells, the cells were divided and given fresh medium three times per week.
## 4.9. Cell Viability Test
To appraise the cytotoxicity of As-EE, HaCaT cells were seeded into 96-well plates at 5 × 105 cell/mL in DMEM supplemented with $1\%$ (v/v) penicillin–streptomycin and $10\%$ (v/v) FBS overnight. As-EE was applied to all cells in a dose-dependent manner for 24 h. To evaluate the cytotoxicity of As-EE, HaCaT cells were seeded into 24-well plates at 3 × 105 cell/mL in DMEM overnight. After pre-treating with As-EE for 30 min, the media was sucked out, and the cells were washed with PBS. Then, the cells were covered with PBS and irradiated with 30 mJ/cm2 UVB before the PBS was sucked out again and As-EE was added in a dose-dependent manner, with a subsequent 24 h incubation. Following the removal of 100 µL, 10 µL of MTT solution (5 mg/mL) was put into each well for 3–4 h. MTT stopping solution [$10\%$ (w/v) sodium dodecyl sulfate in 1 M HCl] was added when purple formazan appeared to stop the reaction [97]. After incubation for 16–20 h, using a microplate reader, the absorbance of each well was determined at 570 nm (BioTek Instruments Inc., Winooski, VT, USA).
## 4.10. mRNA Preparation and Reverse Transcription Polymerase Chain Reaction (PCR)
To ascertain the gene expression of occludin, HAS-3, TGM-1, HAS-1, claudin, HAS-2, and HAS-3, HaCaT cells were seeded in 6-well plates at a density of 5 × 105 cells/mL. The total RNA was isolated using TRIzol reagent, according to the operating manual. The cDNA synthesis was performed using a cDNA synthesis kit. RT-PCR was implemented using PCRBIO HS Taq PreMix (PCR Biosystems Ltd., Oxford, UK) after preparing cDNA with reverse transcriptase [97]. GAPDH was used as reference gene in RT-PCR. The primers for this study are listed in Table 1.
## 4.11. Reporter Gene Assays
HEK293T cells (1 × 105 cells/well) were plated in a 24-well plate using DMEM supplemented with $5\%$ (v/v) FBS overnight. After removing the cultural supernatant, fresh medium (400 µL) was added into each well. Then, the cells were transfected with 0.8 μg/mL of luciferase construct (eg., CREB-Luc) and β-gal (control) by adding the transfection reagent [polyethylenimine (PEI)]. After 24 h incubation, the media was changed to DMEM supplemented with $1\%$ (v/v) penicillin–streptomycin and $5\%$ (v/v) FBS first. Then cells were treated with 50, 75, and 100 µg/mL of As-EE or 6.25 and 12.5 µM of ascorbic acid, respectively, for a further 24 h. A luminometer was used to measure the luciferase activity (BioTek Instruments Inc., Winooski, VT, USA).
## 4.12. Total Cell Lysate Preparation
First, As-EE-treated HaCaT cells were collected with cold PBS. After adding lysis buffer, cells were incubated for 15 min on ice. The cell lysates were then kept at −70 °C after being centrifuged for 15 min at 12,000 rpm. To obtain protein samples for Western blotting, the protein concentrations were measured at 570 nm using the Bradford protein assay (Bio-Rad, Hercules, CA, USA) as described previously [98,99].
## 4.13. Immunoblotting Analysis
After making protein loading samples, all samples were subjected to SDS-polyacrylamide gel electrophoresis (SDS-PAGE), and transferred onto PVDF membranes (Millipore, Billerica, MA, USA). Every loading sample contained 20 µg of proteins. The membranes were blotted with $3\%$ (w/v) BSA at room temperature for 30 min and incubated with primary antibodies overnight at 4 °C. Following that, TBST was used to wash all membranes three times for a total of ten minutes each time. All membranes were then washed once again, followed by a second 2 h incubation with the secondary antibody at room temperature. Finally, all membranes were detected with chemiluminescence reagents [100,101]. In this method, β-actin was used as an immunoblotting loading control, as reported previously [102].
## 4.14. UVB Irradiation
In a 6-well pate, HaCaT cells were evenly plated at a density of 3 × 105 cells/well and incubated overnight. Before UVB irradiation, As-EE was used to pre-treat cells for 30 min. PBS was used to wash the cells and the 6-well pate was irradiated with 30 mJ/cm2 UVB [103]. After UVB irradiation and removing PBS, As-EE was treated again and incubated at 37 °C with $5\%$ CO2.
## 4.15. Morphological Changes
To determine morphological changes, HaCaT cells (3 × 105 cells/well) were seeded equably in a 6-well pate using DMEM supplemented with $10\%$ (v/v) FBS and $1\%$ (v/v) penicillin–streptomycin overnight. Cells were pre-treated with As-EE for 30 min and irradiated under UVB light (Bio-Link BLX-312; Vilber Lourmat, Collégien, France) with a strength of 30 mJ/cm2, as established previously [104,105,106,107,108,109]. The PBS was removed and the cells were treated again with As-EE for 6 h and 12 h [110]. Cells images were taken using an epifluorescence microscope (Olympus, Tokyo, Japan).
## 4.16. PI and Annexin V Staining (FACS)
To evaluate apoptosis, HaCaT cells were seeded into 6-well plates and incubated overnight. The cells were pre-treated with As-EE (from 0 µg/mL to 100 µg/mL) for 30 min. Cells were then removed and washed with PBS. The washed cells were irradiated with UVB (30 mJ/cm2). After incubation for 24 h, UVB-irradiated cells were harvested, washed with cold PBS, and centrifuged at a speed of 1200 rpm for 3 min at 4 °C. Next, all samples were prepared using an FITC-Annexin V Apoptosis Detection Kit I (BD Bioscience, San Jose, CA, USA). A 100 µL volume of 1 × binding buffer was added first. All cells were stained with two apoptotic markers (FITC and PI) following the manufacturer’s instructions [67]. After incubation, 400 µL of 1 × binding buffer was added and the fluorescence was measured using a Guava easy Cyte flow cytometer (Millipore, Burlington, MA, USA).
## 4.17. Statistical Analysis
All results presented are expressed as the mean ± standard deviation (SD) of experiments performed with six (Figure 2a and Figure 3a,e) or three samples (Figure 1a–d, Figure 2e and Figure 4b,c,e,g,i,k). IC50 values were determined using Graphpad Prism 7.0. The graphs were drawn in this study using SigmaPlot (Systat Software, San Jose, CA, USA). Western blot data are a representative of three. All data were analyzed using Mann–Whitney U tests. p-values less than 0.05 and less than 0.01 were regarded as statistically significant and very statistically significant, respectively. Similar experimental data were also observed using an additional independent set of in vitro experiments conducted using the same numbers of samples.
## 5. Conclusions
To sum up, we demonstrated that As-EE may be a potential drug to prevent UVB-induced skin photoaging because we did not see cytotoxicity in the results. At the same time, As-EE also has the ability to moisturize, as summarized in Figure 5. However, we need to study As-EE further in the future—for instance, whether it is difficult to collect As-EE and how stable it is. Moreover, here we did not further investigate inflammation and whitening effects; these functions also need to be explored further. In the end our results strongly suggest that As-EE has antioxidant and moisturizing capacities and that this fraction has potential applications in cosmeceutical preparations. Since GC-*Mass analysis* can only detect highly gas-flammable compounds, we will also employ LC-*Mass analysis* for identify the other compounds in the following project.
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|
---
title: Effect of Clutter Filter in High-Frame-Rate Ultrasonic Backscatter Coefficient
Analysis
authors:
- Masaaki Omura
- Kunimasa Yagi
- Ryo Nagaoka
- Kenji Yoshida
- Tadashi Yamaguchi
- Hideyuki Hasegawa
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10007061
doi: 10.3390/s23052639
license: CC BY 4.0
---
# Effect of Clutter Filter in High-Frame-Rate Ultrasonic Backscatter Coefficient Analysis
## Abstract
High-frame-rate imaging with a clutter filter can clearly visualize blood flow signals and provide more efficient discrimination with tissue signals. In vitro studies using clutter-less phantom and high-frequency ultrasound suggested a possibility of evaluating the red blood cell (RBC) aggregation by analyzing the frequency dependence of the backscatter coefficient (BSC). However, in in vivo applications, clutter filtering is required to visualize echoes from the RBC. This study initially evaluated the effect of the clutter filter for ultrasonic BSC analysis for in vitro and preliminary in vivo data to characterize hemorheology. Coherently compounded plane wave imaging at a frame rate of 2 kHz was carried out in high-frame-rate imaging. Two samples of RBCs suspended by saline and autologous plasma for in vitro data were circulated in two types of flow phantoms without or with clutter signals. The singular value decomposition was applied to suppress the clutter signal in the flow phantom. The BSC was calculated using the reference phantom method, and it was parametrized by spectral slope and mid-band fit (MBF) between 4–12 MHz. The velocity distribution was estimated by the block matching method, and the shear rate was estimated by the least squares approximation of the slope near the wall. Consequently, the spectral slope of the saline sample was always around four (Rayleigh scattering), independently of the shear rate, because the RBCs did not aggregate in the solution. Conversely, the spectral slope of the plasma sample was lower than four at low shear rates but approached four by increasing the shear rate, because the aggregations were presumably dissolved by the high shear rate. Moreover, the MBF of the plasma sample decreased from −36 to −49 dB in both flow phantoms with increasing shear rates, from approximately 10 to 100 s−1. The variation in the spectral slope and MBF in the saline sample was comparable to the results of in vivo cases in healthy human jugular veins when the tissue and blood flow signals could be separated.
## 1. Introduction
The viscoelasticity of blood has been noted as a hemodynamic factor for impaired vascular function [1]. The flow field of blood (velocity gradient, i.e., shear rate) is affected by the hemodynamic property of blood [2,3]. The most dominant component of blood is the red blood cell (RBC), and the flow pattern depends on its behavior. The backscattered signal from the RBC is related to the biochemical properties of the blood sample, e.g., the hematocrit and the level of aggregation. The interaction of the RBC with plasma proteins results in the formation of RBC aggregates, which increase the flow resistance and, thus, increase the viscosity of the blood at a low shear rate. Hence, the relationship between the physical properties and flow patterns of RBCs must be comprehended. The viscoelasticity of blood has been noted as a hemodynamic factor for impaired vascular function [1]. The flow field of blood (velocity gradient, i.e., shear rate) is affected by the hemodynamic property of blood [2,3]. The most dominant component of blood is the red blood cell (RBC), and the flow pattern depends on its behavior. The backscattered signal from the RBC is related to the biochemical properties of the blood sample, e.g., the hematocrit and the level of aggregation. The interaction of the RBC with plasma proteins results in the formation of RBC aggregates, which increase the flow resistance and, thus, increase the viscosity of the blood at a low shear rate. Hence, the relationship between the physical properties and flow patterns of RBCs must be comprehended.
High-frame-rate imaging with a clutter filter can clearly visualize blood flow signals and provide more efficient discrimination with tissue signals. The tissue signals are commonly removed using a high-pass finite or infinite impulse response filters [4]. The idea behind the clutter filter is that the low and high temporal frequency components are echoes from slowly moving tissues and blood flow signals, respectively. Singular value decomposition (SVD) is one of the advanced techniques for the clutter filter design [5,6,7,8]. An SVD-based clutter filter improves the extraction of tiny vessels with slow velocities, in addition to the sensitivity in clutter rejection between slow tissues and blood flow signals [9,10].
The degree of the RBC aggregation can be evaluated in high-frequency ultrasound by analyzing the frequency dependence of the ultrasonic backscatter signals, i.e., backscatter coefficient (BSC) [11,12,13,14]. The attenuation coefficient of blood has been measured through preliminary in vivo study in the frequency range of 10–45 MHz [15]. A pilot clinical study of quantitative BSC analysis has been demonstrated for the RBC aggregation measurement [16]. Conventionally focused line-by-line imaging with frame rates of several 10 s to 100 *Hz is* enough to observe moving RBCs at a low shear rate, and these studies have mainly focused on the hemorheology under peripheral circulation. An in vitro study has revealed the efficiency of high-frame-rate plane wave imaging with a 7.5-MHz ultrasound to characterize static tissue [17] and flowing RBCs in the agar phantom, i.e., in the clutter-less case [18]. Our previous study has also analyzed spatial features such as the contrast of flowing blood in in vitro experiments and in in vivo measurements of human jugular veins [19]. For in vivo imaging in the central vasomotor, such as a carotid artery and jugular vein, the clutter filter is required to visualize echoes from RBCs by suppressing tissue signals. However, the clutter filter has not been considered in the BSC analysis so far.
The novelty of this study is to develop non-invasive hemorheological imaging based on the BSC analysis for in vivo situations. In this study, the performance of the clutter filter was experimentally evaluated to indicate the hemorheological property dependently on a shear rate under physiological conditions. The spectral BSC analysis of porcine blood in a flow phantom without or with clutter signals was analyzed from low to high steady shear rates that were comparable with the human jugular vein [20]. Secondly, in vivo experiments in jugular veins of healthy subjects were carried out to confirm the feasibility under physiological flow conditions. The reproducibility and feasibility of high-frame-rate spectral analysis with clutter filters were compared among inter-subject variance.
## 2.1. In Vitro Porcine Blood Measurement
Whole porcine blood (within 24 h of its collection) anticoagulated by sodium citrate was centrifuged and separated into RBCs, plasma, and platelet at room temperature (24 °C), as followed in previous studies [18,21,22]. The RBCs were diluted in phosphate-buffered solution (PBS) or autologous plasma ($40\%$ hematocrit) to reproduce dispersed or aggregated RBCs. Each sample was circulated at a steady flow in the cylindrical lumen surrounded by $2\%$ agar (i.e., clutter-less) or $2\%$ agar-$7\%$ graphite (i.e., clutter-rich) flow phantoms as illustrated in Figure 1. Acoustic properties of clutter-less and clutter-rich phantoms were as follows: speed of sound (SoS)—1500 and 1520 m/s; attenuation coefficient—0.05 and 0.52 dB/cm/MHz. The blood of 0.8 L was passed through the wall-less lumen (8 mm diameter and 350 mm length) in the flow phantom using a peristaltic pump (Model 07528-10, Masterflex). The distance from the phantom surface to the center lumen was 15 mm. The steady flow was controlled with a flow regulator (CY-1208, ASOH) and monitored with a Coriolis flowmeter (FD-SF8, Keyence). For in vitro experiment, ultrasound data were obtained at selected flow rates from 10 to 600 mL/min.
## 2.2. In Vivo Jugular Vein Measurement
All procedures were carried out following the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration of 1964 and later versions. The Ethics Committee of Toyama University Hospital approved the study protocol (IRB# R2015150 and R2019135). Informed consent was acquired from all subjects. The jugular veins of healthy subjects ($$n = 5$$, 21–49 y.o., male) were scanned along the long axis in vivo. All subjects were in the supine position and breathed normally during the measurement.
## 2.3. Data Acquisition and Post-Processing
A 7.5-MHz linear array probe (UST-5412, Fujifilm) was placed over the middle of the flow phantom so that the lumen along the long axis of the human jugular vein reached its maximum. Multiple plane waves at 5 angles of −10, −5, 0, 5, and 10 degrees were transmitted in each frame at pulse repetition periods of 96 μs, i.e., frame rate of about 2 kHz for compounded images. Tukey apodization with a coefficient of 0.4 was used in transmission. The radio frequency (RF) channel data were acquired at a sampling frequency of 31.25 MHz for 0.1 s (porcine blood experiment) and 0.96 s (in vivo measurement) using a research platform scanner (RSYS0016, Microsonic). The delay-and-sum method with the dynamic aperture (F-number of 1) was applied to the RF channel data [23]. The beamformed RF data (axial 24 mm × lateral 24 mm, 893 × 242 pixels) was reconstructed on a pixel-by-pixel basis (axial 25 μm × lateral 100 μm) at an SoS of 1540 m/s. The coherently compounded RF data were used for the following analysis.
## 2.4. Clutter Filter
A spatiotemporal filter based on SVD was applied to the beamformed RF data to emphasize the blood flow signal [9]. Briefly, a spatiotemporal matrix (spatial 216,106 pixels × temporal 200 (porcine blood experiment) or 2000 (in vivo measurement) frames) composed of the coherently compounded beamformed signals in all frames was used for the SVD filter. The spatiotemporal matrix S is decomposed by SVD into a product of three matrices as [1]S=U^Σ^V^T, where U^ and V^ are matrices in terms of spatial and temporal singular vectors, respectively, Σ^ is a diagonal matrix of singular values with descending order, and T is the transpose operation. The clutter-filtered signal S′ is defined as [2]S′=U^Σ′^V^T, where Σ′^ is a diagonal matrix in which the components in the matrix of Σ^ at orders less than the lower threshold and those higher than the higher threshold are replaced with zeros. The low- and high-rank thresholds of singular values for clutter, blood flow, and system noise components were empirically set among the porcine blood experiments (from −67 to −27 dB normalized by the maximum singular value from a reference phantom in Section 2.5) and in vivo (from −67 to −37 dB similar to the normalization criteria in the porcine experiments) measurements, respectively, in reference to the power curve of singular values, as shown in Figure 2. The 1st inflection point was assumed to be the boundary between clutter and blood flow signals, and 2nd inflection point was the boundary between blood flow signal and system noise.
## 2.5. Backscatter Coefficient Analysis
The BSC of the RBCs was calculated using the reference phantom method [24,25,26] in the frequency domain expressed as [3]BSC(f,d)=P(f,d)¯Pref(f,d)¯BSCref(f)exp[4df(α−αref)8.686], where P¯ and Pref¯ are the mean of power spectra obtained from the RBCs and a reference medium, respectively; BSCref is the theoretical BSC, d is the distance between the transducer and central position of the analysis window; and α and αref are the attenuation coefficients of the RBCs and the reference medium, respectively. The window size for calculation of the mean of the power spectra was 128 × 30 pixels (3.2 × 3.0 mm2, with 10 wavelengths in axial × 5-point spread functions in lateral direction under the SoS of 1540 m/s and center frequency of 7.5 MHz), and then ensemble averaging was performed for 4 frames (1.92 ms). Adjacent analysis windows overlapped by $80\%$ in both the axial and lateral directions.
For in vitro measurement data, the reference medium consisted of a suspension of the RBC in saline at a low hematocrit [27]. In this study, a PBS sample at a hematocrit of $3\%$ flowing in the geometry, as in Figure 1, in the clutter-less and clutter-rich phantoms at a flow rate of 10 mL/min, was used for the reference medium. The reference phantom used for in vivo analysis was made from 96.6 wt% purified water, 2 wt% agar, 0.9 wt% dispersant, and 0.5 wt% polyamide particles with a diameter of 10 μm (Orgasol 2002 EXD NAT1, Arkema). The means of SoS and attenuation coefficient of the reference phantom were 1503 m/s and 0.10 dB/cm/MHz, respectively, in the reflector method [17,28] using a single element plane transducer (V312, Olympus), as well as the backscatter power comparable to human blood [29].
In the attenuation compensation function, the constants α and αref were obtained using a typical attenuation coefficient: 0.30 dB/MHz for in vitro study determined by the reflector measurement in addition to considering the literature value [30]; 0.50 dB/cm/MHz in the tissue [31]; 0.15 dB/cm/MHz in the blood [32] for in vivo study. BSCref for in vitro measurement was the theoretical backscatter of human blood as defined by 5 × 10−31 f4 m−1 sr−1 [33]. BSCref for in vivo reference phantom was calculated based on the Faran model [34]. Here, the BSCref was calculated using the known parameters of the Faran model: particle diameter = 10 μm; volume fractions = $0.5\%$; SoS in particle’s material = 2300 m/s; SoS in the surrounding medium = 1500 m/s; particle density = 1.03 g/cm3; medium density = 1.0 g/cm3; and Poisson ratio = 0.42.
The frequency dependence of the BSC was also evaluated by fitting a function to the calculated BSC in the frequency range from 4 to 12 MHz. The fitting function is expressed as [4]10log10[BSC(f,d)]≈nlog10f+10log10b Equation [4] could be modeled by a line (y=nx+B) having spectral slope n and intercept B in the least-square method, where $B = 10$log10b. It means that the curve fitting was carried out in log scale. Also, the ideal estimate of n of the RBC has been expected to be 4 dB/MHz. In addition, the mid-band fit (MBF) was computed as the magnitude of the BSC, i.e., 10log10[BSC(7.5 MHz,d)], at the frequency of 7.5 MHz.
## 2.6. Shear Rate Estimation
A block matching analysis was carried out to calculate the blood flow velocity. The input of the block matching analysis was the amplitude envelope of the coherently compounded beamformed signal. The block size was 60 × 60 pixels (1.4 × 6.0 mm2 for the axial and lateral directions), and the search distance in both directions was 40 pixels in reference to our previous numerical and experimental studies [29,35]. Adjacent macro blocks overlapped each other by 0.5 and 1.0 mm in the axial and lateral directions, respectively. The normalized cross-correlation function was up-sampled using the reconstructive interpolation method to estimate a sub-sample displacement [36,37]. Both the lateral and axial interpolation factors were 4. Also, the ensemble average of the correlation function was performed for 1.92 ms (4 frames).
The slope of flow velocity profile along the axial direction was calculated as the shear rate in the neighborhood around the proximal and distal boundaries between vein wall and lumen, which were manually traced. The gate length was 3 mm from each boundary in each beam line to estimate the slope by the least-squares method.
## 3. Results
Figure 3a–c show examples of coherently compounded B-mode images in the clutter-less phantom without the clutter filter and clutter-rich phantoms without or with filters in the case of low flow rate (50 mL/min). Ensemble averaging was applied among 4 frames. Note that mean velocity and shear rate were from 3 to 4 cm/s and from 10 to 20 s−1 at the minimum to maximum, respectively. In addition, Figure 3[1]–[2] compare PBS and plasma samples, respectively. The echogenicity of the plasma sample was higher; however, the PBS sample had the same echogenicity that was independent of the shear rate. Conversely, B-mode images in the case of high flow rate (450 mL/min, mean velocity of 12–13 cm/s, and shear rate of 90–100 s−1) were equivalent when compared between PBS and plasma samples, as is shown in Figure 4.
Figure 5 shows the typical BSC of the PBS and plasma samples in the clutter-less and clutter-rich phantoms at selected flow rates of 10, 50, 150, 250, 350, and 450 mL/min. The standard deviation is unmarked for clarity. The magnitude of the BSC in the plasma sample increased with a lower shear rate in both phantoms. In contrast, the magnitude of the BSC in the PBS sample was constant around the theoretical BSC of human blood [33], which was irrelevant to the flow rate. Additionally, as is seen in Figure 6a, the spectral slope of the PBS sample was always around 4 dB/MHz, i.e., Rayleigh scattering was independent of the shear rate, because the RBCs did not aggregate in the PBS solution. On the other hand, the spectral slope of the plasma sample, especially in the clutter-rich phantoms, was lower than four at low shear rates (2.2 dB/MHz at 25 s−1) but approached four by increasing the shear rate, because the aggregations were presumably dissolved by the high shear rate. Also, the MBF of the plasma sample decreased from −36 to −49 dB with an increasing shear rate, as is seen in Figure 6b. Conversely, the MBF of the PBS sample was constant around −52 to −49 dB and was unrelated to the shear rate.
Typical B-mode images of in vivo jugular veins at selected low and high shear rates within intra-subjects are displayed in Figure 7a,b. Ensemble averaging was also applied among four frames to emphasize the signal-to-noise ratio. B-mode images were observed as the horizontal blurring effect of the diffusive suspensions; however, the echogenicity of blood flow could be clearly confirmed. Temporal variations in the spectral slope, MBF, shear rate (mean and standard deviation), and mean velocity are illustrated in Figure 8. Standard deviations in the BSC features and velocity are unmarked for clarity. For five healthy subjects, the spectral slope and MBF in Figure 8a,b were distributed around 2.8 to 5.0 dB/MHz and −53 to −46 dB within the approximate accelerating phase of the shear rate (e.g., increasing from less than 50 s−1 to over 100 s−1) through 0.2 to 0.4 s and the hereafter phase until around 0.8 s. In contrast, the spectral slope rapidly increased to over 5.0 dB/MHz at around 0.1 and 0.9 s (constant phase of the low shear rate and velocity) and exhibited a low MBF of less than −55 dB.
## 4. Discussion
This study initially evaluated the effect of a clutter filter in high-frame-rate ultrasonic backscatter coefficient analysis for blood characterization. In the porcine blood experiment, the property of the RBC aggregation was visualized in a low shear rate (around < 50 s−1) by comparing PBS and plasma samples, as is shown in Figure 3. The RBC aggregation by force between the cell and protein in the overall plasma would induce increasing echogenicity in accordance with previous studies [18,21,38,39]. In contrast, the RBC disaggregation that was caused by the high shear was confirmed through the lower echogenicity of both samples in Figure 4. These findings in the clutter-rich phantom were consistent with the previous studies on the dependence of ultrasonic backscatter power on the shear rate and hemorheology in the case of a clutter-less geometry [18,21,38,39]. In addition, the BSC features, such as spectral slope and MBF, could be quantitatively evaluated in the different shear rates, as is shown in Figure 6.
For five in vivo subjects within the accelerating and deaccelerating phases of shear rate, such as from 0.2 to 0.8 s, the spectral slope and MBF were comparable to those in in vitro experiments of PBS sample, and low shear plasma sample as can be seen in Figure 6 and Figure 8. The variability in BSC features among inter-subjects would be induced by the assumption of same attenuation and by the level of clutter rejection. Further investigation will be necessary to conduct an extensive clinical study to clarify the findings more explicitly in this preliminary study. Although the spectral slope and MBF in 3 of 5 subjects (#1 to #3) were also steady temporally, those in 2 of 5 subjects (#4 and #5) were rapidly changed in the constant phase of shear rate, such as around 0.1 and 0.9 s. Figure 9 displays typical B-mode images and spatial distributions of the spectral slope and MBF with vector velocity and shear rate. The echogenicity inside the vein was lower in Figure 9a, because the blood signals were suppressed by the clutter filter due to the near velocity or echogenicity between tissue and blood flow.
We further investigated the relationship of frequencies (in the clutter filter and BSC feature) between temporal and axial directions. The main temporal frequency was calculated by the column vectors v^n in the temporal singular matrix V^=[v^0 v^1 v^2 ⋯ v^Nframe−1], which corresponds to the temporal basis functions rejected by the SVD filter as mentioned in our previous study [40]. Briefly, the main temporal frequency ω¯SVD was estimated as [5]ω¯SVD=∑ωω|R^(ω)|∑ω|R^(ω)|, where R^(ω) is the frequency spectrum of r^(t) along the temporal direction t. The vector r^ of the rejected components is obtained as [6]r^=∑$$n = 0$$Nlow−rankσnv^n, where σn is the n-th singular value. While the components of the vector r^ are temporal samples of the rejected components, they are redefined as r^≡r^(t).
Figure 10 shows the mean frequency ω¯SVD in the porcine blood data, as is shown in Figure 2a. The components with low frequency under several 100 Hz (in Figure 10) were suppressed by the clutter filter at the cut-off value of approximately −25 to −35 dB (the second and the following plots from a plot at around 0 dB). Furthermore, low frequency components around 4 to 8 MHz (in the axial direction) dominantly decreased with decreasing cut-off values of the SVD filter, as are shown in the frequency spectra P(f) of Figure 11. For that reason, a high spectral slope and low MBF occurred in in vivo jugular vein in the constant phase of the shear rate. Such features were also confirmed in the porcine blood data, as are shown in Figure 12, Figure 13 and Figure 14. Typical B-mode images at selected flow rates of 10 and 350 mL/min are visualized in Figure 12 and Figure 13. Some different cut-offs of high singular values were selected in reference to Figure 2. At the flow rate of 350 mL/min (mean velocity and shear rate around 12 cm/s and 100 s−1), the clutter filter presumably distinguished between blood flow and tissue signals. Hence, the spectral slope was constant around 4 dB/MHz, independent of the cut-off property of singular values, as is shown in Figure 14b. However, the change in echogenicity occurred at the flow rate of 10 mL/min (mean velocity and shear rate around 1 cm/s and 10 s−1) for the different singular cut-off values. Additionally, the spectral slopes of the PBS and plasma samples increased from 2.2 to 6.0 dB/MHz with decreasing singular cut-off values in Figure 14a. Hence, the limitation of the condition of the clutter filter should be calibrated if the level of clutter rejection is uncertain. The criteria based on mean velocity, shear rate, and contrast might be necessary to reject the evaluation frame affected by the clutter filter. Another scenario is to measure with more wide-band BSC toward high frequency. Although the relationship between frequency range and penetration depth for this vascular imaging must be proven, high frequency ultrasound should emphasize backscattering power from the RBCs.
While the plasma sample in the low shear case presented the changes in the spectral slope and MBF in Figure 6, the physiological states were not confirmed in healthy subjects in in vivo situations. Diabetes [2], coronary artery disease [41], thrombosis [42], and hyperlipidemia [43] are associated with abnormally high levels of RBC aggregation. Chronic lower shear rates and flow disturbance in aging patients with diabetes might be distinct from the change in the hemorheological property, such as for the RBC aggregation and thrombus [44,45]. The RBC aggregation has been successively measured using line-by-line focused imaging in vivo in diabetic patients [46]. One of the potentials of high-frame-rate backscatter coefficient analysis for clinical applications will be conducted on a non-invasive quantitative analysis for anticoagulant therapy for diabetes subjects in future works.
## 5. Conclusions
The effect of a clutter filter in high-frame-rate ultrasonic BSC analysis was confirmed in the porcine blood and in in vivo jugular vein measurements. For the porcine blood experiment, the characteristics of the RBCs, such as aggregation and disaggregation, were evaluated as the change in the spectral slope and MBF of the BSC through the surrounding tissues with different clutter levels. The feasibility and reproducibility of this analysis were also compared in in vivo jugular veins in healthy subjects when the tissue and blood flow signals could be separated. High-frame-rate imaging with the clutter filter has the potential to characterize blood by means of the BSC analysis. In future works, the criteria based on flow features and image quality, such as the contrast, will provide feedback regarding the condition of the clutter filter to develop a robust analysis of the BSC.
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|
---
title: Using Digital Human Modelling to Evaluate the Risk of Musculoskeletal Injury
for Workers in the Healthcare Industry
authors:
- Xiaoxu Ji
- Ranuki O. Hettiarachchige
- Alexa L. E. Littman
- Davide Piovesan
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10007127
doi: 10.3390/s23052781
license: CC BY 4.0
---
# Using Digital Human Modelling to Evaluate the Risk of Musculoskeletal Injury for Workers in the Healthcare Industry
## Abstract
Background: Hospital nurses and caregivers are reported to have the highest number of workplace injuries every year, which directly leads to missed days of work, a large amount of compensation costs, and staff shortage issues in the healthcare industry. Hence, this research study provides a new technique to evaluate the risk of injuries for healthcare workers using a combination of unobtrusive wearable devices and digital human technology. The seamless integration of JACK Siemens software and the Xsens motion tracking system was used to determine awkward postures adopted for patient transfer tasks. This technique allows for continuous monitoring of the healthcare worker’s movement which can be obtained in the field. Methods: Thirty-three participants underwent two common tasks: moving a patient manikin from a lying position to a sitting position in bed and transferring the manikin from a bed to a wheelchair. By identifying, in these daily repetitive patient-transfer tasks, potential inappropriate postures that can be conducive to excessive load on the lumbar spine, a real-time monitoring process can be devised to adjust them, accounting for the effect of fatigue. Experimental Result: From the results, we identified a significant difference in spinal forces exerted on the lower back between genders at different operational heights. Additionally, we revealed the main anthropometric variables (e.g., trunk and hip motions) that are having a large impact on potential lower back injury. Conclusions: These results will lead to implementation of training techniques and improvements in working environment design to effectively reduce the number of healthcare workers experiencing lower back pain, which can be conducive to fewer workers leaving the healthcare industry, better patient satisfaction and reduction of healthcare costs.
## 1. Introduction
Musculoskeletal disorders (MSDs) are one of the leading causes of disability in hospital nurses and caregivers as they have the highest number of reported occupational injury cases every year in the United States [1]. In 2021, overexertion as the major contributor caused $43.5\%$ to $64.5\%$ of back injuries in the healthcare industry [2]. In the same year, 300 nurses were involved in a self-administered survey in which $97.3\%$ reported work-related pains, and $86.7\%$ had the worst pain in their lower back [3]. In 2020, there were more than 175,000 cases of days away from work reported by nursing assistants and registered nurses [4]. In 2019, the Liberty Mutual Workplace Safety Index reported that the healthcare industry was one of the most severe workplaces with a high proportion of workplace injuries. An estimated cost of USD 1.77 billion was caused by overexertion and outside sources [5]. In the same year, out of 121 questionnaires distributed to nurses and caregivers, approximately half of the respondents reported upper and lower back problems [6]. Furthermore, the healthcare workers, including nursing assistants and registered nurses, reported that over 23,000 days were missed from work due to MSDs in 2018 [7].
Caregivers’ frequency and severity of injury are associated with certain types of patient-handling tasks. Among the riskiest tasks are moving patients from one bed to another, repositioning patients in bed, transferring patients from a wheelchair to a toilet/bathtub, or lifting patients from the floor [8]. Healthcare settings cause caregivers to adopt postures that are significantly different between individuals due to the lack of workstation customization that generally occurs in other industries. Patients’ weight is another factor affecting the forces on the caregivers’ lower back. The heavier the patients, the larger the load on the caregivers’ backs and shoulders, eventually leading to MSDs [9]. The lack of lifting equipment, shortages in staff [10], and prolonged exposure to a large trunk inclination angle [11] were other reasons leading to healthcare workers’ high risk of injury.
Numerous approaches have been implemented to reduce low back pain in healthcare workers. For example, manual handling training, stress management and stretching exercises were introduced as treatment options in [12]. Yet, there was no strong evidence for any intervention in preventing MSDs in nurses. According to a study questionnaire [10], training in the use of lifting aids has not been widely implemented. In addition, due to staff shortages, $72.6\%$ of nurses still worked 12 h shifts. These can all be conducive to a high prevalence of MSDs in the lower back. Some materials also inform healthcare workers to avoid postures and movements that can cause injury [13,14]. However, these methods seem ineffective since the number of MSD cases has been rising [4]. Finally, since more nurses and nursing assistants are leaving the healthcare industry, employees have been unable to reduce their long shift hours.
Currently, digital human modelling (DHM) technology is prevalently implemented to avoid injuries in the workplace. For example, DHM can be used to enhance productivity by reducing the risk of incurring MSDs [15], simulate a variety of tasks at workplaces to improve organizational ergonomics by analyzing physical fatigue [16], and evaluate safety in public places to make it compatible with the elderly [17]. Yet, there is a lack of DHM posture study in the healthcare sector. The previous studies [18,19,20,21] were limited to single static pose analysis, which cannot imitate realistic human motion to fully estimate the potential risk occurring in workplaces. Moreover, the analysis of spinal load and the load capability of each anatomical joint is sensitive to the DHM-adopted postures [22]. DHM software does not usually implement any principle or algorithm for accurately predicting human movement [23]. Hence, creating each task by positioning individual DHM postures and later merging all postures to form one dynamic simulation remains the most common strategy [24].
DHM technology can be integrated with advanced motion-tracking systems to evaluate the risk of injury. The known limitations of magnetic-based and camera-based motion tracking technologies can be overcome by wearable inertial measurement units (IMU) [25]. The operational range of IMUs is quite extensive as it only depends on the range of wireless links [26]. IMUs do not require any fixed apparatus in the proximity of the working area, such as cameras or magnetic emitters. Hence, the application of IMUs within the workplace includes surgery [27], sports practice [28], and use on the factory floor [29]. Wearable IMUs are ideal for limited workspaces and unstructured environments (such as hospitals), as the body-worn tracking sensors are unobtrusive [26]. These systems do not limit body movement while performing tasks involving full body motion, presenting a clear advantage against infrared markers [30]. Yet, IMUs are known for their inherent drift that can be linear or quadratic depending on the order of the integration. Wearable tracking systems are usually a combination of accelerometers (second order integration to obtain position), gyroscope (first order of integration) and magnetometers [26]. Sensor-fusion techniques can be used to alleviate the drift problem.
The Xsens system combines the signals from 3D gyroscopes, accelerometers and magnetometers to create accurate and drift-free orientation estimation for inertial sensors. Magnetic sensors are particularly important as they provide stability in the horizontal plane by sensing the Earth’s magnetic field’s direction like a compass. These complementary sensors combined with a Kalman filter reduce the drift by continuous correction of the orientation obtained by integrating sensor data.
This study combines the accuracy of wearable sensors in measuring full-body postures with the versatility of DHM to provide accurate ergonomics analysis. Although some common tasks in the healthcare sector have been assessed with this integrative approach in [8], more repetitive patient-transfer tasks need to be studied to help healthcare workers understand the awkward postures that must be avoided. This study focuses on lower back analysis and maximum hand force, which may lead to injuries when the lower back force reaches a safety threshold limit.
## 2.1. System Setup and Participants
This experimental study involved thirty-three participants in performing patient transfer tasks. The following data represent the average and standard deviation for each gender population (seventeen males: height 180.6 (10.6) cm, weight 83.4 (15.1) kg; sixteen females: height 165.7 (7.7) cm, weight 65.5 (10.8) kg). A total of seventeen Xsens MVN Awinda system wearable inertial sensors (Xsens 3D Motion Tracking Technology, Netherlands) were secured on each participant following the user manual. For each individual, we created a skeletal model (DHM_Xsens) in the Xsens software, which was used to record full-body movements. The sensors of the Xsens system were also used to establish the parameters for the digital model. The model accounts for [1] shoulder width, [2] hip widths, [3] arm span, [4] ankle height, and two values for the length of [5] upper arms, [6] lower arms, [7] hands, [8] thighs, [9] shanks and [10] feet.
JACK Siemens software integrates a wide range of population anthropometric data, human performance and motion prediction models [31]; it has been prevalently used in a variety of research areas [32,33,34] which extensively validated the force output. JACK also has the unique capability of integrating with the “Xsens MVN Analyze tool”; a piece of software used for ergonomic analysis. The skeletal roots providing the joint centroid positions in the cartesian space of the DHM_Xsens were imported into JACK software by setting up a unique port number in Network Streamer. By doing so, we created a second model for each participant in JACK (version C6.1), which we will indicate as DHM_JACK hereafter. The anatomical joint centers of DHM_JACK were properly aligned to the Xsens skeletal segments by using a scaling feature. This operation guarantees that the DHM_JACK will exactly copy any movement performed by the DHM_Xsens. This advanced integrative approach greatly eliminates the time required to create a full-body dynamic simulation by manually positioning each DHM anatomical joint.
## 2.2. Operational Tasks
Each participant performed two common patient transfer tasks that are repeated by healthcare workers daily. Participants followed the operational details determined by the UPMC Health System Nursing Assistant Orientation, to ensure basic consistency in task performance.
Task#1: A 25 kg patient manikin was located on the center of a hospital bed (Hill-Rom Advanta). Each participant was required to perform a lying-to-sitting task in bed, as shown in Figure 1. This task involved the following steps: [1] leaning the upper body forward, [2] extending both arms, [3] placing the right hand under the manikin’s head, [4] holding the manikin’s leg using the left hand, then applying force on both hands to move the manikin from a lying position to a sitting position on the edge of the bed.
Task#2: Each participant was required to transfer the patient manikin from the edge of a hospital bed to a wheelchair (Everest & Jennings Product), as shown in Figure 2. This task involved the following steps: [1] leaning the body forward, [2] lifting the manikin up to a standing position by applying an upward force on the underarms of the manikin, [3] firmly holding the manikin straight, [4] moving the manikin in front of the wheelchair, and [5] gently placing the manikin onto the chair.
Two different bed operational heights (higher height: 32.5 in; lower height: 25.5 in) were designed. Each participant was required to perform four cycles executing Task#1 and Task#2 consecutively at each operational height, to ensure the reliability of results while minimizing fatigue.
## 2.3. Data Analysis
The forces generated on the 4th/5th lumbar spine (L4/L5) were analyzed using the Task Analysis Toolkit (TAT) in JACK. The application can estimate the compressive and anterior/posterior (A/P) shear forces based on the posture assumed by the model and the force generated at the hands. The magnitude, application point and direction of the force applied by each hand was added to the Human Control Panel. In this study, we focused on three specific postures which placed the healthcare workers at a higher risk of injury due to the excessive estimated forces (compressive and A/P) acting on the lower back.
Pose#1: In the task of moving the patient manikin from a lying position to a sitting position (Task#1), the maximum spinal load occurred as the participants began to raise the head and upper trunk of the manikin in bed, as shown in Figure 3a. The force was measured by a digital force gauge (SF-500).
In the task of transferring the mock patient from the bed to the wheelchair (Task#2), excessive forces occurred at two poses.
Pose#2: The participants stood toward the patient manikin with extended arms, flexed elbows, hands placed under the patient’s arms and bent upper body slightly backward to support the body weight of the manikin, as shown in Figure 3b.
Pose#3: In the same task of transferring the patient from the bed to the wheelchair (Task#2), the second at-risk pose was detected as the participants bent their upper body forward and extended both arms to place the patient manikin into the wheelchair, as shown in Figure 3c.
Finally, each hand’s maximum force exerted to reach a 3400 N compressive lower-back load was estimated at each of the three aforementioned poses. The National Institute for Occupational Safety and Health (NIOSH) [35] suggested the aforementioned spinal compressive load as the at risk value for developing MSDs. Once the estimated hand force is determined using the biomechanical module in JACK, healthcare workers will understand the maximum force they can exert during their daily patient transfer tasks to avoid the risk of injury.
## 2.4. Statistical Analysis
The data distribution for each of the variables (compressive force, A/P shear force, trunk and hips) will be tested by using MATLB (MathWorks Inc., Monterey, CA, USA). At each pose, a two-way analysis of variance (ANOVA) was performed to determine the significant difference between genders and two operational heights for each of the aforementioned variables. To reduce the chance of failure, the Bonferroni correction was involved to adjust the p values when several independent or dependent variables are tested simultaneously on a single data set. Considering that the calculated p values for some significantly different variables were too small, even after the Bonferroni correction post hoc analysis, the conclusion to describe the significance was not changed. Additionally, to fully understand the impact of each variable, a t-test was performed to analyze the compressive force, A/P shear force and the joint angular displacement between genders at each of the operational heights. Hence, the p values for each independent t-test are presented in this study. The statistically significant level was set at 0.05. Moreover, we analyzed the cross-correlation (R) between the spinal forces and either body height, weight or joint angles. Considering the significance of the correlation coefficient is highly related to the sample size (thirty-three participants in this study) [36], the critical values are ±0.35, indicating that if the calculated R values are greater than +0.35 or less than −0.35, they are significant.
## 3. Results
The normality of the distribution for each of the variables (compressive force, A/P shear force, trunk and hips) was confirmed using a Kolmogorov–Smirnov test. All the p values were found to be less than 3.2 × 10−7, which rejected the null hypothesis at the $5\%$ significance level.
The settings at each of the specific postures to estimate the ergonomics results are listed in Table 1. The spinal forces acting on the lower back and the estimated anatomical joint angles are listed in Table 2 and Table 3. The correlation coefficients for both compressive and A/P loads are listed in Table 4. The explanation for the calculated results at each pose is described in the following Section 3.1, Section 3.2 and Section 3.3, respectively.
## 3.1.1. Force Analysis
In Task#1, the measured force magnitude applied on the right hand was approximately 70 N. The direction was vertically upward. At this time frame, the force applied by the left hand could be consider negligible.
The maximum forces exerted on the lower back occurred at Pose#1 when the participant bent over at the waist, the right hand supported the manikin’s head, the left hand held the legs, and the participant started raising the manikin’s head and trunk from the bed. There was no significant difference in the spinal load of the participants as they exerted the lifting effort at two different operational heights ($p \leq 0.05$, see Figure 4a). However, the spinal forces estimated on the male participants were statistically larger than the female participants for the compressive force analysis with $p \leq 0.0001$ (males: 3342.6 N; females: 2266.7 N), and for the A/P shear force with $$p \leq 0.0002$$ (males: 720.4 N; females: 510.9 N) at the higher operational height. Furthermore, the p values were less than 0.0001 for the analysis of compressive force (males: 3559.8 N; females: 2360.3 N) and A/P shear force (males: 812.2 N; females: 574.1 N) at the lower operational height.
The maximum applied hand force was estimated until the compressive force’s safety threshold limit reached 3400 N at each specific pose. The estimated hand force was not significantly different at both operational heights, but it was statistically different between genders ($p \leq 0.0001$). For the female population, the predicted average forces exerted by each hand were 168.2 N when the bed was at 32.5 in, and 159.9 N when the bed was at 25.5 in. The maximum forces in the female population were more than two times greater than for the male population which reached only 82.4 N at the higher height and 69.8 N at the lower height.
## 3.1.2. Joint Angle
There are two important joint centers defined in DHM_JACK. The Root joint is defined as the center of two greater trochanters. Spine#1 joint is defined as the center of two posterior superior iliac spine (PSIS). Hence, the trunk movement is based on the relatively angular displacement between Vector#1 (from the Acromion to Spine#1) and Vector#2 (from Spine#1 to the Root). The hip movement is based on the relatively angular displacement between Vector#1 (from Spine#1 to the Root) and Vector#2 (from the Root to the Knee).
The comparison of joint angular displacements is shown in Figure 4b,c. Assuming that the trunk angle and the hip angle are 0° at a neural erected pose, the positive and negative values for the trunk and hips indicate flexion and extension, respectively. The hip movement was significantly different at both operational heights (right hip: $$p \leq 0.0006$$; left hip: $$p \leq 0.0012$$). The average angular displacements of the right hip for the whole population were 44.8° and 60.6° at the higher and lower operational heights, respectively. For the left hip, the angular displacements were 34.1° and 47.9° for the higher and lower heights, respectively. Interestingly, despite the different size of the subjects, there was no significant difference between genders.
On the other hand, there was no significant difference between trunk angular displacement at both operational heights for all participants. Yet, the difference was statistically different between genders (higher height: $$p \leq 0.049$$; lower height: $$p \leq 0.024$$). At the higher operational height, the average trunk angles were 6.1° (flexion) for males, and −6.4° (extension) for females. At the lower operational height, the average trunk angles were 8.3° (flexion) for males, and −4.8° (extension) for females.
## 3.1.3. Cross-Correlation
There was a high correlation between body height and spinal forces. The R values were 0.93 and 0.86 for the compressive and A/P shear forces, respectively, at the higher height, and 0.94 and 0.91, respectively, at the lower height. There was also a high correlation between body weight and spinal forces. The R values were 0.78 and 0.72 for the compressive and A/P shear forces, respectively, at the higher height, and 0.79 and 0.80, respectively, at the lower height. The correlation between the trunk angular displacement and compressive force was moderate with the R values of 0.51 and 0.43 at the higher and lower heights, respectively. It was interesting to notice that there was a relatively high negative correlation between the trunk and hip movement with the average R values of −0.62 and −0.58 at the higher and lower operational heights, respectively.
## 3.2.1. Force Analysis
The direction of the force applied by the participant to the patient was assumed vertically upward. Considering that both feet of the patient barely contacted the floor, the force distributed to each hand was approximately 125 N (25 kg × 9.8/h). When assuming $h = 2$ the force was equally distributed between the two hands. Although there was slight contact between the patient and the edge of the bed, the 125 N estimated force exerted by each hand is a good approximation to reveal the effect of key anthropometric variables affecting the force generated at the lower back of each participant.
The maximum spinal forces occurred at the pose when the participants put their hands under the arms of the manikin to start lifting from the bed (Figure 5a). The estimated compressive forces exerted on the lower back of males (higher height: 3038.1 N; lower height: 3293.2 N) were significantly different than the forces on the females’ back (higher height: 2644.1 N; lower height: 2766.2 N) at both operational heights (higher height: $$p \leq 0.041$$; lower height: $$p \leq 0.005$$).
To reach the safety threshold limit of 3400 N when raising the patient manikin from the bed at two different heights, the females can exert approximately more than 30 N force per hand than the males. At the higher operational height, the estimated force for females was 176.2 N per hand, as compared to 150.8 N for males. At the lower height, the average force for females was 166.0 N, and the force for males was 136.6 N.
## 3.2.2. Joint Angle
When comparing the hip angular displacements, the right hip has 5° difference at the two operational heights (higher height: 11.4°; lower height: 16.4°). For the comparison between males and females, the trunk angular displacement was significantly different between the two groups with $$p \leq 0.008$$ at the higher height, and $$p \leq 0.015$$ at the lower height. In Figure 5b,c, the female population shows a greater trunk extension than the males in the patient transfer activity. At the higher operational height, the average trunk extension was −9.6° for the female group, and −1.7° for the male group. At the lower height, we find −5.6° (extension) for the females and 4.0° (flexion) for the males.
## 3.2.3. Cross-Correlation
The trunk and hip movements were still negatively correlated with the lumbar compression force at Pose#2 in Task#2. The estimated A/P shear force and the hip flexion were highly correlated at both heights (higher height: $R = 0.68$; lower height: $R = 0.60$). Additionally, the body weight was moderately correlated with the lumbar compressive force with $R = 0.41$ and $R = 0.48$ at the high and low operational height, respectively. The correlation between the compressive force with the body height were $R = 0.49$ at the higher height, and $R = 0.68$ at the lower height.
## 3.3.1. Force Analysis
Considering that the excessive spinal force was estimated when the patient manikin just barely touched the wheelchair, each participant still held most of the manikin’s body weight. While the support from the manikin’s feet could reduce the loads applied by both hands, the 125 N estimated force to each palm was an approximated value which represents a figure of merit for the load. Given that end-users can easily modify the force values to represent the lifting efforts, having an approximated magnitude of the force would still provide useful information for a sensitivity analysis.
The second pose to have an excessive spinal force in Task#2 occurred as the participants moved the patient manikin into the wheelchair with a large hip flexion, as well as arm extension and knee flexion. The estimated spinal loads on the males were significantly greater than the forces on the females ($p \leq 0.001$; Figure 6a). The average compressive forces in males and females were 5201.0 N and 4003.3 N, respectively. The A/P shear force was also dangerously higher, averaging 1116.6 N in the male population and 854.7 N in the female population. Nearly all participants (31 out of 33) had a spinal load larger than the safety threshold limit of 3400 N for the compressive force [35], and 700 N for the shear force [37].
The hand force exerted by females to reach the compression safety threshold of 3400 N was nearly twice the males at this specific pose ($p \leq 0.0001$). The average safe applicable hand force was approximately 100 N per hand for females, and 50 N for males.
## 3.3.2. Joint Angle
The trunk angular displacement was significantly different between genders with $$p \leq 0.047.$$ Females extended their trunk (−6.2°) while male flexed it (3.5°) at this specific pose. There was no significant difference between hip angles between genders at this specific pose, as shown in Figure 6b,c.
## 3.3.3. Cross-Correlation
Both body weight and height are highly correlated with the lower back compressive force with $R = 0.65$ and $R = 0.81$, respectively. Both anthropometric variables were also highly correlated with the A/P shear force with $R = 0.59$ and $R = 0.71$, respectively. Additionally, the A/P shear was highly correlated with the right hip flexion ($R = 0.66$). A high negative correlation between the movement of the trunk and hips was found with an average R = −0.58.
## 4. Discussion
In this study, the participants were required to perform two common patient transferring tasks. The spinal forces exerted on the lower back were assessed. During the entire task performance, three specific postures are assumed to provoke excessive force on the spinal column, which could put healthcare workers at risk of injury. At each of the aforementioned poses, we analyzed the effect of the bed operational height on the posture adoption for all participants and the effect of key anthropometric and biomechanical factors affecting the force on the lower back between genders.
As the operational bed height was changed from 32.5 in to 25.5 in at Pose#1, only the hip angular displacement was significantly different for all participants, which was consistent with the result in [8]. The spinal forces and the trunk movement were not noticeably different among participants between the two heights. The physical constraints of the adopted posture could be the cause. Participants needed to extend their arms to support the manikin’s head and leg to facilitate the transition of the patient from lying to sitting in bed. Meanwhile, the individual’s eyes concentrated on the manikin’s face. This action may involve the extension of the participant’s head, which may somewhat constrain their trunk flexion. This phenomenon is consistent with the result in [38].
When comparing the spinal forces between genders, the forces on the spinal column of males were significantly larger than their female counterparts. The difference in compressive force amounted to approximately 1000 N. Given the lifting force, the trunk and hip movement determined the load amplitude affecting the participants’ lower back [22,39]. Our statistical analysis revealed a moderate correlation between the trunk angular displacement and the compressive spinal force at two different operational heights. Moreover, a high negative correlation between the movement of hips and trunk existed at Pose#1, particularly among females. Due to their relatively short body height, females have to flex their hips, and extend their arms and trunk to reach the manikin. The difference in average body height (15 cm) allowed the males to simply flex their trunk instead of adequately extending their spine into the lifting task. Given this significant difference in posture adoption between genders, the force affecting the lower back of males quickly reached the safety threshold limit of 3400 N even if the force exerted by each hand is merely 70 N. We can foresee that healthcare workers lift larger loads daily, putting them at a high risk of injury during their daily activities. Accordingly, providing the proper training and ergonomic adjustability of the beds to avoid awkward postures (trunk flexion) are keys to preventing workers from incurring injury when transferring a patient from lying to sitting in bed.
At Pose#2, the movement of the right hip has 5° difference among the participants, as they started lifting the manikin from the bed at two operational heights. Considering the proper lifting techniques suggested in the patient transfer task [13,14], healthcare workers should stand closer to the patient to avoid large trunk flexion, as shown in Figure 2. In this case, the limited space between the participants and the manikin constrained the participants’ trunk movement, forcing them to flex their hips to start the transferring task. Additionally, due to the position of the wheelchair on the right side of the bed, each individual preferred to tilt their body to the right side while simultaneously extending the left lower limb. The aforementioned posture limited any significant difference of the left hip movement when the operational height was changed at Pose#2.
At Pose#2, the trunk angular displacement and the compressive spinal force were significantly different between genders, consistent with the results at Pose#1. Again, high correlation between the A/P shear force and the hip flexion was revealed. Although males and females have some difference in the anthropometric and biomechanics aspects (e.g., the width of hip span and the muscle strength), both genders have a similar range of motion of trunks [40]. Hence, to prevent back injuries, both females and males should keep the upper body straight, bend the knees, and engage lower body muscle groups to lift an object safely, as mentioned by the Mayo Clinic [41]. The estimated force exerted by each hand to reach the spinal force safety threshold was also quite different between genders. At the higher operational height, the average estimated force was 175 N per hand for females as compared to 150 N per hand for males. Hence, females would be exposed to the risk of injury when lifting a 35 kg load. On the other hand, for the male population, the risk threshold was reached by lifting a 30 kg load. As the operational height was changed to 25.5 in, the average hand force was reduced by 10 N for both genders. Accordingly, the lifting load should be commensurate to the operational height, and it should either be decreased at lower heights, or redistributed by recruiting more workers to help completing the task. These results could help healthcare workers avoid injury when the lifting load is approximately or greater than the estimated 30 kg load at this specific pose.
At Pose#3, the spinal forces exerted by the participants (31 out of 33) were greater than the safety threshold limits for both the compressive force (3400 N) and shear force (700 N). Moreover, the forces exerted by the male population were significantly greater than the females. One major reason was the notable discrepancy of trunk movement. While the height of the person plays a big role, the perceived muscular strength is another important factor influencing individual postures [42]. Male participants preferred to position the patient manikin in the wheelchair by flexing their trunk, while females preferred to extend trunk and flex their hips. Indeed, the A/P shear force was highly correlated with the right hip flexion at this specific pose, because participants needed to step forward with their right foot to place the patient manikin in the wheelchair. Based on the maximum hand force analysis, males would reach a safety threshold when only 50 N was exerted by each hand while females could exert up to 100 N to put the healthcare worker at risk of injury. Accordingly, an appropriate assistive device or more workers should be involved to reduce the lifting load for healthcare workers.
At all three poses, both anthropometric variables, body height and weight, indicated a high correlation with the spinal forces, which was consistent with the results in previous studies [43,44]. More body weight supported by the lower trunk may lead to an increased load on the lower back. Considering that the carried load and the trunk flexion are key factors in the risk of lower back injury, the heavier and taller the workers are, the higher the risk is for them to incur MSDs during patient transfer tasks.
## 5. Conclusions
In this work we highlighted two distinct patient transfer tasks. We found that even though trained in the proper lifting techniques, a large force can be exerted on the lower back of healthcare workers as they assist a patient from lying to sitting in bed and moving them from the bed to a wheelchair. When the operational height of the bed is changed, the hip movement has a significant influence on posture adoption. Additionally, given the difference of anthropometric and biomechanics variables between genders, the male population is more easily exposed to a high risk of injury than females. Hence, for safety considerations, the lifting load and the adopted posture must be extremely controlled.
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|
---
title: Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid
Content in Cannabis Sativa
authors:
- Jonathan Tran
- Simone Vassiliadis
- Aaron C. Elkins
- Noel O. I. Cogan
- Simone J. Rochfort
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10007171
doi: 10.3390/s23052607
license: CC BY 4.0
---
# Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa
## Abstract
Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography–mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II–Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4–$100\%$ accuracy prediction), the handheld device also performed well (83.1–$100\%$ accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.
## 1. Introduction
Cannabis sativa has been used as a herbal medicine for thousands of years, with its first documented use on Egyptian *Ebers papyrus* dating back to the sixteenth century BC [1]. Today, medicinal cannabis has been shown to be beneficial in the treatment of neurological conditions, such as multiple sclerosis and epilepsy, and for the treatment of chronic pain [2]. While many pharmaceuticals are synthesised using laboratory reagents, cannabinoids are extracted from the inflorescences of cannabis plants, *Cannabis sativa* [2]. The two major cannabinoids of interest for their therapeutic effects, found in C. sativa, are cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC) [3,4]. CBD has been used in the treatment of intractable epilepsy, particularly in children [3], with research into its analgesic and anti-anxiety effects progressing [5]. THC is used in the treatment of multiple sclerosis (MS), spasticity, chronic pain and posttraumatic stress disorder (PTSD) [4,6,7]. Cannabidiolic acid (CBDA) and tetrahydrocannabinolic acid (THCA) are naturally synthesised molecules in the plant that are converted into the more active CBD and THC through a process of decarboxylation, typically achieved by heating cannabis inflorescences. CBD and THC do occur naturally in the plant but generally in lower levels as breakdown products from acid precursors. CBDA and THCA are the major cannabinoids in most plants, although their therapeutic potential is not as well studied as CBD and THC.
Some minor cannabinoids have been studied for therapeutic benefits. These include cannabinol (CBN) as an anti-inflammatory [8]; cannabigerolic acid (CBGA), cannabidivarinic acid (CBDVA) and cannabidivarin (CBDV) as anti-convulsants [9,10]; cannabigerol (CBG) has indications of being a treatment for MS and Parkinson’s disease (PD) [11]; tetrahydrocannabidivarin (THCV) has evidence of being an appetite suppressant and type-2 diabetes treatment [12,13]; and cannabichromene (CBC) has been studied as an anti-convulsant and in antitumor treatment [14,15]. Despite this, most manufacturers focus on products with high THC and/or CBD, meaning cultivators select plants high in THCA and/or CBDA.
Traditionally, the analysis of cannabinoids is completed using gas chromatography (GC) or liquid chromatography (LC) coupled with mass spectrometry (MS) as a detector; alternatively, flame-ionization detection (FID) is used for GC or an ultra-violet diode array detector (UV-DAD) is used for LC [16]. This process, although accurate and selective, is costly and slow compared with other techniques such as near-infrared (NIR) spectroscopy. NIR spectroscopy is a rapid, nondestructive but non-selective technique that can be used to rapidly scan cannabis in reflectance mode to produce NIR spectra [17,18,19,20,21]. These spectra can be used to make cannabinoid content predictions or distinguish between cultivars with significantly different chemotypic profiles. This can be achieved by developing prediction models from NIR spectra paired with high-quality quantitation data and multivariate statistical software. Sanchez et al. [ 2018] [22] developed cannabinoid content prediction models of CBDV (R2 = 0.92), Δ9-THCV (R2 = 0.87), CBD (R2 = 0.98), CBC (R2 = 0.93), Δ8-THC (R2 = 0.85), Δ9-THC (R2 = 0.90), CBG (R2 = 0.54) and CBN (R2 = 0.87) in finely ground, heat-treated cannabis inflorescences using data from two lab-based NIR instruments and with GC-FID data as a reference [22]. Deidda et al. [ 2021] [21] made prediction models for Δ9-THC using two handheld NIR instruments on three sample types, fresh whole inflorescences (R2 = 0.93, R2 = 0.73); coarsely ground (R2 = 0.76, R2 = 0.74); and sieved cannabis (R2 = 0.77, R2 = 0.93) samples, with UHPLC-UV data as a reference. Jaren et al. [ 2022] [20] used a handheld NIR to make prediction models for Δ9-THC (R2 = 0.77) and CBD (R2 = 0.77), with HPLC-DAD data as reference data, on heat-treated, finely ground material [20]. Yao et al. [ 2022] [19] used an FT-NIR handheld instrument to build predictive models for Δ9-THC (R2 = 0.91) and CBD (R2 = 0.93) concentrations in finely ground cannabis hemp samples. This research has demonstrated that NIR spectroscopy can be successfully used as a tool to predict cannabinoid concentration when accompanied by high-quality reference data. Apart from Sanchez’s study, these studies did not attempt to quantify the presence of cannabinoids such as CBDA, THCA, CBC, CBGA, CBG, CBDVA, CBDV THCV, THCVA, CBN, CBNA and CBCA. It is important for cultivators to obtain as much detailed information on the cannabinoid profile of their cannabis plants as possible, and information on concentrations is desirable. In addition, these studies utilise small sample sizes and lack chemovar variation. In statistics, it is always important to have a varied dataset with many samples to reduce any possibility of population bias. Lastly, the aforementioned work only focused on the main cannabinoids, CBD and Δ9-THC, in heat-treated material; without the decarboxylation step, CBDA and THCA are far higher in abundance compared with their derivatives [23]. In previous research, cannabis or hemp material was heat-treated before analysis, or quantitation was conducted using GC, which decarboxylates the acids and detects THC or CBD [18,20,22].
Birenboim et al. [ 2022] [17] developed prediction models for CBDA (R2 = 0.97), THCA (R2 = 0.95), CBD (R2 = 0.83), THC (R2 = 0.86), CBGA (R2 = 0.99), CBG (R2 = 0.72) and CBCA (R2 = 0.87) using an FT-NIR analyser to scan finely ground cannabis inflorescences and distinguished chemovars by classifying them as high THCA, high CBDA, high CBGA or ‘hybrid’ [17]. Some studies have also combined THC and THCA measurements to predict ‘total THC’ [21]. However, it may be useful to determine these individually since levels of decarboxylation can be an indication of maturation; as further research into the bioactivity of other cannabis constituents develops, quantifying amounts of the acids post-processing will become important. In addition, THC and THCA exposed to light and temperature are expected to degrade into CBN and CBNA over time through oxidation, and this is considered undesirable, as THC is a primary therapeutic target. These two cannabinoids should be of interest, as they may also serve as indicators of growth in long-term storage conditions.
This current work describes the statistical analysis of inflorescences from 734 cannabis plants using NIR and LCMS data to develop different models using principal component analysis (PCA), partial least discriminant analysis (PLS-DA) and partial least squares regression (PLS-R). Prediction models for 14 cannabinoids (CBDA, THCA, CBD, THC, CBC, CBGA, CBG, CBDVA, CBDV THCV, THCVA, CBN, CBNA and CBCA) using 2 NIR systems and comparing finely and coarsely ground material is presented; finely ground material is more homogenous and reveals more surface area for the NIR to scan and, therefore, should provide better prediction models; conversely, coarsely ground material requires less effort but may produce weaker models, as coarsely ground is less homogenous. Ideally, if coarsely ground material can provide prediction models comparable to finely ground material, this would be a timesaving alternative. This work has applications in cultivation where the rapid assessment of cannabinoid profiles can be used to assess a harvest for regulatory compliance, product consistency or determining a market segment to provide the highest return on investment for the cultivator. Other potential uses include monitoring cannabinoid degradation over time and the impact of storage conditions and/or monitoring cannabinoid production during the vegetation and flowering stages. This technology and the associated prediction models could potentially be used by external quality assurance (QA) auditors as a rapid solution when inspecting cannabis plants to ensure cultivators adhere to their cultivation permits or other regulatory requirements during auditing.
## 2.1. Sample Preparation
All seeds were legally imported from Canada or generated by project activities, and all the work undertaken was performed under a Medicinal Cannabis Research Licence (RL$\frac{011}{18}$) and Permits (RL01118P6 and RLO1118P3) issued by the Department of Health (DoH), Office of Drug Control (ODC), Australia.
Plant growth and harvest conditions were as detailed in Naim-Feil et al. [ 2021] [24], where 734 cannabis plants consisting of 164 unique chemovars, with 4–5 biological repeats and were separated into 4 harvest groups (HG) according to harvest date: $$n = 479$$ for HG 1; $$n = 126$$ for HG 2; $$n = 78$$ for HG 3; and $$n = 51$$ for HG 4. All samples were freeze-dried for 48 h using a VirTis General Purpose Freeze Dryer (Scientific Products, Warminster, PA, USA) and then coarsely ground by placing the sample into a paper bag and hand crushing it until the material was approximately 3–5 mm in size prior to NIR scanning. Samples were then placed in liquid nitrogen for 1 min and ground to a fine powder using a SPEX SamplePrep 2010 Geno/Grinder (SPEX SamplePrep, Metuchen, NJ, USA) for 1 min at 1500 rpm. The fine powder was then subjected to the same NIR scanning methods as described below. A subsample of the ground powder was subjected to quantitation using LC-MS methods described by Elkins et al. [ 2019] [16]. A histogram plot of all cannabis samples detailing the cannabinoid range of each harvest group is provided in Figure S1.
## 2.2. Instrumentation and Parameters
Two near-infrared spectrometers were used to collect NIR scan data—a laboratory benchtop system and a handheld device.
## 2.2.1. FT-NIR Bruker MPA II
A laboratory benchtop NIR system was first used to acquire NIR spectral data: a Bruker MPA II–Multi Purpose FT-NIR Analyzer (Bruker Corporation, Billerica, MA, USA) equipped with TE-InGaAs detectors collected spectra in diffuse reflectance mode in a range of 11,500–4000 cm−1 (870–2500 nm). A 22 mm vial, 30-sample rotary scanner attachment was used to perform scans at a resolution of 16 cm−1 and 64 scans, with a measurement time of 25 s per individual sample. The ground cannabis samples were contained in 22 mm glass vials purchased from Bruker, ensuring that the sample saturated the bottom of the vial, and placed into the rotary scanner. Background was recorded automatically every hour with a gold-coated reference, and 4 QC samples were run every 30 samples to monitor any changes in signal in the instrument. The OPUS 8.2.21 software (Bruker Corporation, Billerica, MA, USA) was used for spectra data acquisition. To ensure reproducibility, scanning was performed in triplicate. The sample vials were agitated in between each scan to ensure homogeneity, and the data from the triplicate scans (total of 2202 individual scans) were averaged using Microsoft Excel 365 (Microsoft Corporation, Redmond, Washington, DC, USA) before data analysis. This wavenumber range contained complex spectral features exclusive to cannabinoids. In total, 734 individual cannabis samples were analysed.
## 2.2.2. VIAVI MicroNIR Onsite-W
For comparative purposes, a VIAVI MicroNIR Onsite-W Spectrometer (VIAVI Solutions Inc., Scottsdale, AZ, USA) is a handheld portable NIR device that uses an InGaAs detector. Samples were decanted into sample cups/onto nitrogen-free weighing papers (Sigma-Aldrich, St. Louis, MI, USA) and scanned by directly pressing the detector against the cannabis sample. The detector was thoroughly cleaned using $80\%$ methanol and a KimTech Kimwipe tissue (Kimberly-Clark, Irving, TA, USA) between each sample until there was no residue left. Scans were performed in triplicate, and the data were averaged as previously described. Data were collected between 10,526–6060 cm−1 (950–1650 nm). The device was configured in diffuse reflectance mode with an integration time of 12 milliseconds and 100 scan counts. Scanning was performed in standard laboratory conditions. Data acquisition was performed using MicroNIR Pro v3.0 (VIAVI Solutions Inc., Scottsdale, AZ, USA). In total, 730 cannabis samples were scanned.
## 2.3. Statistical Analysis
Bruker MPA II data were exported from the OPUS 8.2.21 software and imported into Microsoft Excel (Microsoft Corporation, Redmond, WA, USA), where the data from the triplicate scans were averaged. The averaged data were then imported into MATLAB 2022a (Mathworks, Natick, MA, USA) with PLS-Toolbox 9.0 (Eigenvector Research Inc, Manson, WA, USA) for analysis. The data were then trimmed to 9000–4000 cm−1 (1111–2500 nm). For statistical modelling, pre-processing was optimised using combinations of detrend, standard normal variate (SNV) and normalization, and 1st-order and 2nd-order derivatives were selected based on the best R2 prediction whilst maintaining low prediction bias. PCA was performed to check data quality and explore trends within the sample population. PLS-DA models were used to further predict specific class information such as CBDA and THCA ratios, and PLS-R models were used to create predictive models for cannabinoids: CBDA, THCA, CBD, THC, CBC, CBGA, CBG, CBDVA, CBDV THCV, THCVA, CBN, CBNA and CBCA. Both PLS techniques used LCMS quantitative data as independent variables and NIR data from both the Bruker MPA II and VIAVI MicroNIR Onsite-W as dependent variables. Separate regression models were developed for each compound, and the method with the highest R2 prediction value was selected. Venetian blinds cross-validation was used with 10 splits and blind thickness set to 1.
*When* generating calibration and validation sets for PLS-R and PLS-DA models, a 75:25 split of the data was performed on the sample set. The Kennard–Stone algorithm was used to ensure the data selected for the calibration set were uniform and representative of the whole dataset, while the validation set contained samples that were interior and exterior to the calibration set. The algorithm achieves this by randomly seeking two samples within the dataset with the largest distance measure using either Euclidean or Mahalanobis distance [25]. This eliminates extrapolation in the calibration model when applied to the validation test. To test for statistical significance, permutation tests ($$n = 50$$) were carried out on all PLS models (Wilcoxon, Sign Test and Rand t-test).
## 3.1. Principal Component Analysis of NIR Data
A PCA was performed on the dataset to identify trends and assess data quality. Initial analysis of the data showed that the triplicate scans clustered well, highlighting good reproducibility (refer to Figure S2), and no spectra had to be removed from the dataset (Figure 1a,b). The average spectra for each sample were thereby used for all future models.
The analysis of the Bruker MPA II NIR spectra from the 734 samples showed that there were 3 major clusters evident across principal component (PC) 1, accounting for $65.39\%$ of the variation within the dataset (Figure 1c). It was postulated that this clustering was related to cannabinoid content, particularly between high-CBDA, high-THCA and even-ratio chemovars (refer to Tables S3 and S4); this was proven to be correct once the NIR dataset was annotated according to the high CBDA, high THCA and even ratio. The PCA was repeated using the MicroNIR NIR data and the same high-CBDA and -THCA and even-ratio class information (Figure 1d). Although clustering in the chemovars was still apparent ($38.71\%$ of the variation within the dataset was distributed across PC1), even-ratio and high-CBDA chemovars had some overlap. PCA are typically performed to assist in building calibration and validation datasets by identifying outliers that may influence the performance of the model while exploring variations between samples [20,22]. PCA models performed by Sanchez et al. [ 22] on their cannabis dataset showed clustering; however, there was no elaboration on what the principal components or clustering represent, so it is important to investigate all trends and clusters that occur. Conversely, PCA models performed by other researchers have been used to identify distinct clustering within their datasets, and they have classified their samples as high CBDA, high THCA, high CBGA or ‘hybrid’. In addition, Birenboim et al. [ 17] [2022] were able to assign clusters to individual cultivars with high accuracy. Given the PCA showed clustering based on the chemotype, supervised PLS-DA models were investigated to determine how accurately the high-CBDA, high-THCA, and even-ratio chemovars could be predicted from the spectra.
## 3.2. Partial Least Squares Discriminant Analysis (PLS-DA) Modelling
PLS-DA models were able to divide the cannabis dataset into the three previously described categories: the even-ratio, high-CBDA and high-THCA classes (Figure 2a–c). High CBDA was defined as classes that had a concentration ratio of CBDA to THCA higher than 6:1; high THCA had a concentration ratio of less than 1:6; and even ratio had concentration ratios between 6:1 and 1:6 (CBDA:THCA) (refer to Table S3).
## 3.2.1. FT-NIR Bruker MPA II Data
The dataset scanned with the Bruker MPA II had a total of 734 finely ground samples. Sensitivity prediction and specificity prediction values were one and equal for the high-CBDA and high-THCA predictors (Table 1). Typically when sensitivity and specificity values are equal to each other, this is an indicator of a highly accurate PLS-DA model [26]. Our prediction models showed a classification accuracy of $100\%$ (Class Error Pred. = $0\%$) for both high-CBDA and high-THCA models and $99.4\%$ (Class Error Pred. = $0.7\%$) for even-ratio models. These models showed excellent results in predicting high-CBDA, THCA and even-ratio classes.
Birenboim et al. [ 17] [2022] created PLS-DA models to make classification models of high-THCA, high-CBDA, high-CBGA and ‘hybrid’ classes, and the results provided accurate classifications based on a cannabis dataset of 325 cannabis samples relating to 15 unique chemovars. As noted by Birenboim, there were only 10–30 biological repeats per chemovar, meaning there was little genetic variation [17]. However, the present study has a cannabis dataset consisting of 734 cannabis samples with 164 unique chemovars with 4–5 biological repeats, resulting in larger genetic variation and the elimination of population bias due to the high sample number and different chemovars available whilst retaining sample repeatability. This sample size is certainly the largest and most complex, leading to the most robust prediction model currently developed.
## 3.2.2. VIAVI MicroNIR OnSite-W Handheld Data
The PLS-DA model using the MicroNIR data had a total of 730 samples for each cannabinoid. PLS-DA modelling for MicroNIR used the same definitions for even ratio, high CBDA and high THCA (Figure 3a–c). High THCA had a classification accuracy of $100\%$ (Class Error Pred., $0\%$); high CBDA was at $94.5\%$ (Class Error Pred., $5.5\%$); and even ratio was at $83.1\%$ (Class Error Pred., $16.9\%$) (Table 2). Whilst the predictions for high-THCA chemovars were accurate, reduced accuracy for the high-CBDA and even-ratio predictions were possibly due to the limitation of the resolution and range of the Micro NIR. Duchateau et al. used handheld NIR devices to create prediction models and focused on discriminating between ‘illegal’ (THC content > $0.2\%$ w/w) and ‘legal’ cannabis. From a law enforcement perspective, this is useful, but this research is not able to truly define chemovars as high-THCA or high-CBDA classes [27]. The purpose of comparing the two instruments in the current study was to identify a rapid, field-deployable and inexpensive cultivar assessment tool for use in the cannabis industry in both commercial and research settings. Limited research has used MicroNIR and other handheld devices to develop prediction models, and researchers have typically built PLS-R models [20,21,22] over PLS-DA models. However, when considering the ability to rapidly quantify cannabinoid content, it largely rests on user requirements, for example, an exact concentration, which provides a numerical value or a classification on whether it is a high-THCA or high-CBDA chemovar [21]. Classification prediction models may be more useful than rapid cannabinoid assessments for cultivators where precise cannabinoid quantification values may not be the priority, as opposed to guaranteeing whether a cannabis plant is a high-THCA or high-CBDA chemovar.
## 3.3. Partial Least Squares Regression (PLS-R) Modelling
Creating prediction models using partial least squares regression will provide prediction values ranging from R2 0 to 1. This value determines the accuracy of a prediction model. According to Williams et al. [ 2019] [28], prediction models can be classified as having a poor correlation (R2 = 0.26–0.49); predictions adequate for rough screening (R2 = 0.50–0.64); predictions adequate for screening and approximate predictions (R2 = 0.65–0.81); good predictors for applications such as research but not quality assurance (QA) (R2 = 0.82–0.90); great predictions that can be used in QA settings (R2 = 0.91–0.97); and excellent predictors (R2 > 0.98), which can be used in any application [28]. The ratio of performance of deviation (RPD) is a value typically reported as a measure of the goodness of fit of a prediction model; this has not been reported in this paper’s findings, as Minasny et al. [ 2013] [29] mentions that RPD and R2 values are the same measures. The current literature echoes this sentiment, as Martin [2022] [30] deems the RPD measure an inadequate indicator of a precise prediction model, instead preferring to use high R2 values and a ratio between the standard error prediction (SEP) and the standard laboratory error (SEL) as a measure of a strong prediction model, where the closer SEP is to SEL, the greater the precision of the model. SEL values in this study were reported (refer to S5) when a majority of cannabinoid SEL values were close to SEP values, providing good–excellent precision.
## 3.3.1. FT-NIR Bruker MPA II Data
This dataset consisted of 734 samples that were prepared according to Section 2.1, Sample Preparation. The scanned samples were finely ground cannabis powders.
Different pre-processing parameters were applied to optimise the prediction of 14 cannabinoid concentrations (Table 3, Figure 4). The predictions for CBDA and THCA were highly accurate, with a regression value R2 of 0.98 for both cannabinoids. This was followed by THC, with an R2 value of 0.93, and CBD at 0.89. CBN and CBNA had R2 values of 0.80. The R2 values for the CBDVA, CBDV, CBGA and CBCA prediction models ranged from 0.53 to 0.61; according to Williams et al. [ 2019] [28], these values are still adequate for the rough screening of cannabinoids from dried finely ground cannabis inflorescences. The models for CBC, CBG, THCV and THCVA provided R2 values between 0.34 and 0.46, indicating a poor correlation between the predicted and measured groups.
Throughout this study, all PLS-R models benefited from a variety of different data pre-treatment parameters to explore the best prediction results. Applications included SNV, second derivative, detrend and normalisation. The Bruker MPA II FT-NIR spectrometer produced high-quality NIR spectral data with good resolution. The CBDA, THCA and THC models had high values (R2 > 0.91) and are ideal for QA applications. The CBD model is suitable for research applications (R2 = 0.82–0.90). The model for CBNA and CBN is suitable for approximate predictions and screening (R2 = 0.65–0.81), whilst the CBCA, CBDVA, CBDV and CBGA models are only strong enough for rough screening (R2 = 0.50–0.64). The CBC, CBG, THCV and THCVA models had poor correlation (R2 < 0.49), most likely due to a poor abundance of the given cannabinoid.
Recent research has developed cannabinoid prediction models using heat-treated cannabis inflorescences [20,21,22]. Because of this, the researchers were not able to quantify CBDA and THCA, which are the most abundant cannabinoids produced, and, therefore, did not analyse the original metabolic profile of the cannabis inflorescences prior to decarboxylation. Alternatively, Birenboim et al. analysed cannabis inflorescence samples without heat treatment and developed robust prediction models for CBDA (R2 = 0.97), THCA (R2 = 0.95), THC (R2 = 0.86) and CBD (R2 = 0.83) [17]. This is similar to the present study; however, improved R2 values were developed using the prediction models for CBDA (R2 = 0.98), THCA (R2 = 0.98), THC (R2 = 0.93) and CBD (R2 = 0.89). This is likely because our dataset is much larger, with 734 samples and 164 unique chemovars with 4–5 biological repeats, which provided a more robust model. In addition, prediction models were developed for CBN (R2 = 0.80), CBNA (R2 = 0.80), CBDVA (R2 = 0.60) and CBDV (R2 = 0.59) suitable for screening purposes [28]; this is novel for the cannabis industry, as these cannabinoids do not have developed PLS-R models for concentration prediction.
It is likely that higher R2 values can be achieved when there is a high abundance of the target cannabinoid present; typically, CBDA and THCA are the most abundant, followed by CBD and THC. Hence, their high R2 values are relative to the other cannabinoids present. A high cannabinoid concentration in a sample will enable the NIR to detect this cannabinoid with greater intensity; this intensity then is paired with quality LCMS data and processed using statistical software to apply machine learning algorithms identifying correlations and patterns. This is not ideal for low-concentration cannabinoids such as CBC, which are overshadowed by higher-concentration cannabinoids such as CBDA; because of this, their models may result in poor prediction.
It is also important to include samples that have varying concentrations of cannabinoids to ensure an unbiased population. Having a model that only has extremely high or low values of a cannabinoid may yield false high R2 prediction values, as the data will contain a population bias that will overfit the data.
## 3.3.2. VIAVI MicroNIR OnSite-W Handheld Data
This dataset consisted of 730 samples prepared according to Section 2.1, Sample Preparation. The scanned samples were finely ground cannabis powders.
The VIAVI MicroNIR was utilised in this study to explore the benefits of using a handheld device as opposed to a benchtop instrument. Although it has a shorter wavelength range of 950–1650 nm (10,526–6060 cm−1) and fewer NIR datapoints compared with the Bruker MPA II, comparable cannabinoid predictive results were achieved (Table 4, Figure 5). Prediction models for both CBDA and THCA returned an R2 prediction value of 0.98, excellent for any application including research and QA, followed by CBD (R2 = 0.80) and THC (R2 = 0.75), CBNA (R2 = 0.76); CBN (R2 = 0.71); and CBCA (R2 = 0.66), suitable for screening and some approximate calculations. Prediction models for CBDVA (R2 = 0.55) and CBDV (R2 = 0.51) are adequate for rough prediction, whilst the models developed for CBC, CBGA, CBG, THCV and THCVA perform poorly, ranging from R2 0.21 to 0.38.
Deidda et al. [ 21] [2021] used the MicroNIR to create prediction models for THC (R2 = 0.77) on 26 finely ground cannabis inflorescence samples. The study illustrated good preliminary findings but lacked a large dataset, and the source of the cannabis samples was vague. In addition, the focus on only THC is not sufficient, even from a law enforcement perspective, as not all THCA is decarboxylated into the active THC. Therefore, crucial information regarding cannabinoids that can be easily heated into its psychoactive derivative is lacking. Jaren et al. used a Luminar 5030 miniature handheld NIR to make prediction models for CBD (R2 = 0.77) and Δ9-THC (R2 = 0.77), coupled with HPLC-DAD data as reference data, on 35 heat-treated and finely ground cannabis hemp samples [20]. Yao et al. used an FT-NIR handheld instrument to build predictive models for Δ9-THC (R2 = 0.91) and CBD (R2 = 0.93) concentrations in 91 finely ground cannabis hemp samples [19]. Yao et al. and Jaren et al. only focused on CBD and THC and not their acidic forms; it is important to gather information about CBDA and THCA when determining cannabinoid profiles. In addition, these authors only looked at cannabis hemp samples that contained $0.3\%$ or less THC. In the present study, both CBD and THC, as well as their acidic forms, CBDA and THCA, prediction models were developed along with six additional cannabinoids (CBN, CBDVA, CBDV, CBGA, CBNA and CBCA), some of which can be used for general screening or QA purposes. In addition, a sample size of 730 was used that contained 164 unique chemovars, reducing population bias. *The* genetic variation in the sample size is a great advantage over the two papers that only focused on cannabis hemp samples.
It is interesting that CBDA and THCA prediction models performed well using the MicroNIR compared with the Bruker MPA II considering its limited range and resolution. The MicroNIR is more than adequate and still able to perform excellent predictions for CBDA and THCA, rivalling the Bruker MPA II whilst being a smaller, rapid and portable solution. Considering that the cannabis industry still largely focuses on CBDA and THCA when producing cannabis products, this device can be successfully deployed in regulatory affairs and QA audits, where rapid inspection can classify a cannabis plant as a high-CBD or high-THC chemovar based on the CBDA and THCA predictions.
The CBD, THC, CBN, CBNA and CBCA prediction models using the MicroNIR did not perform as well when compared with the Bruker MPA II but are still within ranges for screening and provide adequate insight into the chemotypic profile of the plant. Since CBNA and CBN are both oxidation products from THCA and THC and have R2 prediction values >0.70, this technique can provide insight into the quality of inflorescence material and the impact of storage conditions, as it slowly oxidises due to air, temperature and light, and it can be used as a rapid application for monitoring cannabinoid content for long-term storage [31,32].
## 3.4. Cannabinoid Correlation Matrix
Correlation coefficients were obtained for 14 cannabinoids (Table 5) using available LCMS data (refer to S6) to see the statistical relationship between each cannabinoid compound, where values nearing 1 indicate a positive correlation, values nearing −1 indicate a negative correlation and values nearing 0 indicate no correlation. High negative correlation coefficients were observed for CBDA and THCA (−0.80), where high levels of one cannabinoid indicate lower levels of the other. The same was seen for CBD and THC; however, the negative correlation is weaker at −0.50.
Minor cannabinoids may have strong prediction models due to strong positive correlations to more abundant cannabinoids that have strong R2 prediction models. In the case of CBDA and CBD, the CBDA R2 prediction is 0.98, and the CBD R2 prediction is 0.89, despite CBD being lower in concentration compared with CBDA, with a correlation coefficient of 0.79. This observation is repeated for THCA (R2 = 0.98) and THC (R2 = 0.93), with a correlation coefficient of 0.66, where THC is lower in concentration than THCA. CBDA-CBD (0.79), THCA-THC (0.66) and CBDVA-CBDV (0.82) have positive correlations, which can be explained by the decarboxylation pathway of their acid forms to their neutral forms. THC (R2 = 0.93) and CBN (R2 = 0.80) also have a high correlation coefficient of 0.78, and this may be related to the oxidation pathway, where, over time, THC degrades into CBN; this degradation pathway and correlation also apply to THCA and CBNA. CBDA also correlates positively with CBDVA, CBDV and CBCA. Our findings are consistent with correlation matrices of cannabinoids found in the previous literature, where CBDA, CBD and CBCA are positively correlated with each other, whilst THCA and THC are positively correlated [33]. As with the present study, the previous literature also reports a negative correlation between CBDA and THCA. Birenboim et al. created a correlation matrix, showing a positive correlation between certain abundant cannabinoids and terpenes [33]. Overall, it seems that minor cannabinoids that have a positive correlation with more abundant cannabinoids (due to sharing a chemical pathway) have a positive influence on the R2 prediction.
## 3.5. Finely Ground vs. Coarsely Ground Material Using Bruker MPA II FT-NIR
A comparison was made of finely ground (Table 6) and coarsely ground (Table 7) inflorescences using the Bruker MPA II and scanning 479 samples (HG1). The aim of this was to see if the results were comparable between the two sample forms, as coarsely ground inflorescences will require less effort and preparation time. Prediction values for THCA (0.98 vs. 0.94), CBD (0.88 vs. 0.80) and THC (0.91 vs. 0.83) were better in all instances for finely ground inflorescences when compared with coarsely ground inflorescences. This was expected, as finely ground inflorescences have less area between the particles, increasing the scannable surface error of the sample; the sample is also more homogenous than that of the coarsely ground one. However, CBDVA (0.44 vs. 0.53), CBGA (0.43 vs. 0.51) and CBG (0.28 vs. 0.51) prediction values performed better in coarsely ground inflorescences; CBC (0.28 vs. 0.34), THCV (0.14 vs. 0.43) and THCVA (0.09 vs. 0.37) all performed better in the coarsely ground material form but did not have R2 values above 0.50, which cannot produce a model adequate for rough screening. The reason for this anomaly is unclear, and further investigation is required. CBDA (0.99 vs. 0.98), CBN (0.71 vs. 0.69), CBCA (0.60 vs. 0.59) and CBNA (0.79 vs. 0.79) saw negligible differences in R2 prediction values. CBDV (0.93 vs. 0.95) was also negligible; however, the R2 prediction values were unusually high compared with CBDV in Table 3; it is to be noted that this cannabinoid model has a population bias of high and low concentrations only influencing the model accuracy. More datapoints are required to correct this and improve the accuracy of the model.
Deidda et al. [ 21] [2021] developed prediction models for THC and compared whole inflorescences, coarsely ground inflorescences and finely ground (sieved) inflorescences, with inconsistent results and found that when using the NIR-S-G1, finely ground inflorescences provided the most accurate prediction (R2 = 0.73, 0.74, 0.93); however, when compared with the MicroNIR, the whole inflorescence was the most accurate (R2 = 0.93, 0.76, 0.77). These results are contradictory to the current studies’ findings when comparing coarsely ground and finely ground inflorescences using the MicroNIR and Bruker MPA II, where the finely ground inflorescences provided the most accurate prediction.
Su et al. [ 18] [2022] compared non-decarboxylated whole hemp and ground hemp and developed prediction models using a Perten DA7250 NIR Analyzer for five cannabinoids: CBD (R2 = 0.89, 0.85); THC (R2 = 0.11, 0.25); CBN (R2 = 0.03, 0.38); CBG (R2 = 0.43, 0.03); and CBC (R2 = 0.79, whole inflorescence only). Although this study showed strong predictors for CBD and CBC, THC and CBN were weak, with almost no correlation, which is expected, as they were only looking at high-CBD cannabis hemp. When comparing physical sample states, whole hemp samples produced better R2 values than those of ground hemp samples, which is contradictory to Deidda’s NIR-S-G1 results.
Overall, the current work found that scanning coarsely ground material provided quite comparable results to finely ground material, with the added benefit of saving time on sample processing. This is ideal for screening purposes, as it is cost-effective and would require less manual labour; in an industry setting where cultivators grow and harvest cannabis plants are at a large scale, this technology would be greatly valued. Depending on the use case, it may be better to opt for finely ground material to achieve the highest R2 prediction values for QA scenarios (R2 > 0.91), such as CBDA, THCA and THC (Table 6); however, coarsely ground cannabis material can also achieve results suitable for QA purposes for CBDA and THCA predictions (Table 7). From an industry perspective, this would be most desirable, as minimum standards for QA are being met whilst reducing workload.
## 4. Conclusions
Predictive models for the quantitation of 14 cannabinoids (CBDA, THCA, CBD, THC, CBC, CBGA, CBG, CBDVA, CBDV THCV, THCVA, CBN, CBNA and CBCA) and the characterisation of high-CBDA, high-THCA and even-ratio chemovars in cannabis inflorescence samples were developed using a combination of NIR techniques and LCMS data to build PLS-R and PLS-DA models. Two different NIR instruments were used, and it was found that the Bruker MPA II produced better results overall. However, the MicroNIR provided high-quality predictions for the main cannabinoid precursors (CBDA and THCA) and is accurate for several other cannabinoids (CBD, THC, CBNA, CBN and CBCA), despite being outperformed by the benchtop Bruker MPA II. Although the Bruker MPA II has a 30-vial rotary attachment that improves high throughput (scanning 30 samples in 15 min), the MicroNIR has the benefits of portability, rapid acquisition and a lower cost. PLS models with high predictive ability for CBDA and THCA were developed for both techniques and indicated that the abundance of a compound is one of the main influences in producing prediction models with high R2 values, and minor cannabinoids that correlate strongly with these major molecules also have a higher predictive R2 even at low concentrations. Further research is required to investigate potential correlations between major and minor cannabinoids and terpenes, potentially providing prediction models for terpene content. The analysis of coarsely or finely ground material was consistent, with coarsely ground material producing slightly less accurate prediction models compared with finely ground material, making coarsely ground material a suitable alternative if rapid, high-throughput screening is preferred over high-quality predictive models made for quality assurance or research. This process can be improved further if non-ground whole inflorescences can produce similar results, as the timesaving would be greater.
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---
title: Polyphenol Iongel Patches with Antimicrobial, Antioxidant and Anti-Inflammatory
Properties
authors:
- Gisela C. Luque
- Melissa Moya
- Matias L. Picchio
- Vanessa Bagnarello
- Idalia Valerio
- José Bolaños
- María Vethencourt
- Sue-Hellen Gamboa
- Liliana C. Tomé
- Roque J. Minari
- David Mecerreyes
journal: Polymers
year: 2023
pmcid: PMC10007217
doi: 10.3390/polym15051076
license: CC BY 4.0
---
# Polyphenol Iongel Patches with Antimicrobial, Antioxidant and Anti-Inflammatory Properties
## Abstract
There is an actual need for developing materials for wound healing applications with anti-inflammatory, antioxidant, or antibacterial properties in order to improve the healing performance. In this work, we report the preparation and characterization of soft and bioactive iongel materials for patches, based on polymeric poly(vinyl alcohol) (PVA) and four ionic liquids containing the cholinium cation and different phenolic acid anions, namely cholinium salicylate ([Ch][Sal]), cholinium gallate ([Ch][Ga]), cholinium vanillate ([Ch][Van]), and cholinium caffeate ([Ch][Caff]). Within the iongels, the phenolic motif in the ionic liquids plays a dual role, acting as a PVA crosslinker and a bioactive compound. The obtained iongels are flexible, elastic, ionic conducting, and thermoreversible materials. Moreover, the iongels demonstrated high biocompatibility, non-hemolytic activity, and non-agglutination in mice blood, which are key-sought material specifications in wound healing applications. All the iongels have shown antibacterial properties, being PVA-[Ch][Sal], the one with higher inhibition halo for Escherichia Coli. The iongels also revealed high values of antioxidant activity due to the presence of the polyphenol, with the PVA-[Ch][Van] iongel having the highest activity. Finally, the iongels show a decrease in NO production in LPS-stimulated macrophages, with the PVA-[Ch][Sal] iongel displaying the best anti-inflammatory activity (>$63\%$ at 200 µg/mL).
## 1. Introduction
Nowadays, bioactive natural compounds are gaining interest in the biomedical field because of the possibility to obtain biocompatible, biodegradable, and non-toxic materials with therapeutic properties [1,2]. In particular, the design of original skin healthcare materials, like wound healing membranes, is increasing significantly [3,4]. The wound healing mechanism basically involves four steps: hemostasis, inflammation, proliferation, and remodeling [5]. For this reason, evaluating the anti-inflammatory and antioxidant activity of substances with potentiality for wound treatment is crucial because the upregulation of inflammatory mediators and radical oxygen species (ROS) may delay and impair the healing process. Indeed, it is reported that dysregulation of the inflammatory response may result in host tissue damage, rendering a chronic pathological inflammation [6,7]. At the same time, the presence of free radicals and oxidative reactions can accelerate inflammation, and oxidative stress can induce cellular damage, which are the main causes of delayed wound healing.
The use of bioactive materials with anti-inflammatory and antioxidant properties can offer an excellent opportunity to design novel patches for wound treatment and tissue regeneration [6,7]. In this context, polyphenols appear as good candidates because of their attractive properties like analgesic, anti-inflammatory [8], and antioxidant agents [9,10]. Moreover, polyphenols have acted as crosslinkers of different polymers due to their capability to form physical and chemical bindings [11,12]. There are some works reporting the use of phenolic compounds (PhCs) in the synthesis of hydrogels and iongels [11,13,14,15]. Iongels, in particular, are a new generation of soft-ionic materials that can be applied in several fields, such as drug delivery [16,17,18], sensors [14,19], energy [20,21,22], and bioelectronics [23,24]. These exciting materials are tridimensional polymeric networks that contain percolated an ionic liquid (IL) in their structure [25]. For bio-related applications, the polymer and ILs employed need to be biocompatible, biodegradable, and preferably bioactive.
Despite the valuable features of iongels, there are scarce reports regarding their use in wound healing applications, but ILs have been widely combined with biopolymers to develop new materials for this application. Morais et al. reported the preparation of bacterial nanocellulose membranes impregnated with cholinium-based phenolic ionic liquids for skin treatment [26]. They demonstrated by in vitro assays that these membranes present anti-inflammatory and analgesic properties due to the presence of phenolic acid anions in the ILs’ composition. Nevertheless, the main drawback of these materials is their rigidity and low deformation capability (reported elongation at break was around $2\%$), limiting the surface adaptability during application. In the same line Arruda Fernandez et al. summarizes the advantages to combine the properties of bacterian cellulose with phenolic compounds to prevent the UV-induced skin damage [27]. Moreover, particularly employing cholinium gallate in combination with silk fibroin Gomez et al. reported the preparation of sponges with antioxidant and anti-inflammatory features to be in tissue engineering strategies due to the benefits of the phenolic compound. They demonstrate the influence of the use of the PhCs to balance the pro- and anti-inflammatory cytokines and also no hemolysis effect was informed [28]. Taking advantage of the properties of polyphelons, Shengye and co-workers reported the use of poly(tannic acid) nanorods in a polysaccharide matrix composed by quaternary ammonium chitosan and oxidized β-glucan in the design of hydrogels for diabetic wound healing [29]. They reported of superior wound repair properties of the hydrogel by using diabetic rat model in comparison with a commercial wound dressing.
On the other hand, Fang et al. took advantage of the antibacterial properties of imidazolium-based ILs to synthesize hydrogels from 1-vinyl-3-butylimidazolium bromide poly(ionic liquid) and polyvinyl alcohol (PVA), finding that these obtained materials promoted epidermis reconstruction [30].
In this article, we present, with a different approach using iongels as soft ionic materials, the preparation of bioactive and soft-ionic materials bearing a biocompatible PVA network and percolated phenolic-based ILs. First, four different ILs were synthesized, by combining the cholinium cation and phenolic acids as anions, namely gallate, vanillate, caffeate, and salicylate. The ILs play a dual role in these featured iongels: (i) as a physical crosslinker by forming H-bonding with PVA and (ii) as bioactive compounds. The mechanical properties of the iongels were fully investigated depending on the material composition, looking for soft, flexible and elastic materials. Furthermore, the potential of the prepared iongel materials for wound healing was studied in terms of their biocompatibility, non-hemolytic properties, and antioxidant and anti-inflammatory activity.
## 2.1. Materials
Poly (vinyl alcohol) (PVA, Merck (Rahway, NJ, USA), degree of hydrolysis $99\%$, Mw 145 kDa), gallic acid (GA, Merck, ≥$99.0\%$), salicylic acid (SA, Alfa Aesar, Haverhill, MA, USA), caffeic acid (Cam TCI), vanillic acid (VA, Sigma Aldrich, St. Louis, MO, USA), choline hydroxide solution (Sigma Aldrich) were used as supplied. Distilled-deionized water was used for all experiments. The following reagents were used as received: RPMI 1640 (Thermofisher, Waltham, MA, USA), fetal bovine serum (Sigma Aldrich), PSN (penicillin, streptomycin, neomycin) (Gibco), Trypsin (MP biomedics). Trypan blue (Gibco), CyQuant XTT cell Viability assay (Invitrogen), phosphate buffer saline (Sigma Aldrich). 2,2-diphenyl-1-1picrylhydrazyl (Sigma Aldrich), ascorbic acid (Sigma Aldrich), methanol (Sigma Aldrich), lipopolysaccharide (LPS, Sigma Aldrich), N- 1-(naphthyl) ethylenediamine dihydrochloride (Sigma Aldrich), sulfanilamide (Sigma Aldrich), phosphoric acid (Sigma Aldrich), nitrite standard for IC (Sigma Aldrich), ethanol (Sigma Aldrich) and parthenolide (Sigma Aldrich).
## 2.2. Synthesis of Phenolic-Based Ionic Liquids
The ILs, namely cholinium salicylate ([Ch][Sal]), cholinium gallate ([Ch][Ga]), cholinium vanillate ([Ch][Van]), and cholinium caffeate ([Ch][Caff]) were synthesized using a procedure previously reported by Sintra et al. [ 31] The chemical structures and purities of the ILs were confirmed by 1H- and 13C-MR.
Cholinium gallate: 1H NMR (D2O, 400 MHz): δ/ppm = 6.98 (s, 2H, H-2 and H-6), 3.97–3.92 (m, 2H, NCH2CH2OH), 3.39–3.36 (m, 2H, NCH2CH2OH), 3.09 (s, 9H, N(CH3)3).
13C NMR (D2O, 101 MHz): δ/ppm = 174.83 (COO), 144.33 (C-3 and C-5), 135.72 (C-4), 128.19 (C-1), 109.33 (C-2 and C-6), 67.43 (t, JCN = 2.9 Hz, NCH2CH2OH), 55.54 (NCH2CH2OH), 53.57 (t, JCN = 3.9 Hz, N(CH3)3).
Cholinium vanillate: 1H NMR (D2O, 400 MHz): δ/ppm = 7.46 (d, 1H, H-2), 7.39 (dd, 1H, and H-6), 7.36 (d, 1H, H-5), 4.00- 3.95 (m, 2H, NCH2CH2OH), 3.84 (s, 3H, OCH3), 3.45–3.42 (m, 2H, NCH2CH2OH), 3.12 (s, 9H, N(CH3)3).
13C NMR (D2O, 101 MHz): δ/ppm = 175.02 (COO), 148.17 (COH-4), 146.78 (COCH3-3), 128.40 (CCOO−1), 123.08 (C-6), 114.81 (C-5), 113.04 (C-2), 67.30 (t, NCH2CH2OH), 55.77 (NCH2CH2OH), 55.51 (OCH3), 53.7, (t, N(CH3)3).
Cholinium salicylate: 1H NMR (D2O, 400 MHz): δ/ppm = 7.65 (d, 1H, H-6), 7.24 (t, 1H, H-4), 6.75 (m, 2H, H-3 and H-5), 3.80–3.75 (m, 2H, NCH2CH2OH), 3.20–3.17 (m, 2H, NCH2CH2OH), 2.89 (s, 9H, (N(CH3)3).
13C NMR (D2O, 101 MHz): δ/ppm = 175.02 (COO), 159.63 (COH-2), 134.02 (C-4), 130.46 (C-6), 119.44 (CCOO−1), 117.81 (C-5), 116.32 (C-3), 67.23 (t, JCN = 3.1 Hz, NCH2CH2OH), 55.56 (NCH2CH2OH), 53.66 (t, JCN = 3.9 Hz, N(CH3)3).
Cholinium caffeate: 1H NMR (d6-DMSO, 400 MHz): δ/ppm = 7.06 (d, 1H, CHCHCOO), 6.87 (d, 1H, H-2), 6.73 (dd, 1H, H-6), 6.66 (d, 1H, H-5), 6.09 (d, 1H, CHCHCOO), 3.87–3.82 (m, 2H, NCH2CH2OH), 3.43–3.39 (m, 2H, NCH2CH2OH), 3.11 (s, 9H, (N(CH3)3) 13C NMR (d6-DMSO, 101 MHz): δ/ppm = 171.55 (COO), 148.14 (CHCHCOO), 146.54 (COH-4), 138.27 (COH-3), 126.75 (CHCHCOO), 123.43 (C-1), 119.76 (C-6), 119.21 (C-5), 116.49 (C-2), 67.05 (t, JCN = 2.8 Hz, NCH2CH2OH), 55.05 (NCH2CH2OH), 53.14 (t, JCN = 3.8 Hz, N(CH3)3.
## 2.3. Preparation of Self-Assembly Iongel Materials
The iongel materials were formed by hydrogen bonding between PVA and the different phenolic anions of ILs. Iongels with 5, 10, and $20\%$ of polymer concentration (with respect to the IL) were synthesized. The synthesis involved the dissolution of the PVA in water at 90 °C and the addition of the corresponding IL with a ratio water/IL 1:1. For example, to prepare the PVA-[Ch][Sal] iongel with 10 wt% of polymer concentration, 0.05 g of PVA was first dissolved at 90 °C in 0.5 g of water under vigorous stirring. Then, 0.5 g of [Ch][Sal] IL was added. After complete dissolution of all the components, the mixed solution was poured into silicone molds and left at room temperature until gelation.
## 3.1. Thermal Analysis
Thermogravimetric analyses (TGA) were carried out on a TGA Q500 device from TA instruments. Samples of around 10 mg were heated at a constant rate of 10 °C min−1, under a nitrogen atmosphere, from 25 to 600 °C. The temperature at the maximal decomposition rate (Tmax) was determined at the main peak of the derivative weight loss curve.
## 3.2. FTIR Spectroscopy
A Bruker ALPHA spectrometer was used to collect the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra, from 400 to 4000 cm−1, with a resolution of 4 cm−1 after 32 scans. The samples were placed directly on the ATR crystal.
## 3.3. Rheological Behavior
In order to analyze the gel-sol transition temperatures (Tgel-sol) of the iongels, dynamic mechanical thermal analysis (DMTA) was performed using a parallel-plate geometry (8 mm in diameter), with a temperature sweep ranging from 20 to 120 °C and a heating rate of 2 °C min−1. The experiments were conducted at a frequency of 1.0 Hz and $0.1\%$ of strain. An Anton Paar Physica MCR 301 rheometer was used to measure the rheological behavior of the polyphenol iongel materials.
## 3.4. Mechanical Properties
The mechanical properties were tested on a universal testing machine (INSTRON 3344) at 23 °C and $55\%$ of relative humidity. Disks were prepared with around 1 mm in thickness and then subjected to compression, in which a 10 mm diameter plane-tip was moved down at a constant speed (1 mm·min−1) until compressing the samples $40\%$ of their height. Five consecutive cycles were performed for each iongel sample.
## 3.5. Ionic Conductivity
The ionic conductivity of the polyphenol iongel was measured by electrochemical impedance spectroscopy (EIS) using an Autolab 302N potentiostat-galvanostat coupled to a Microcell HC station, with temperature control during the measurements. Circular samples of 8 mm in diameter were used. The samples were sandwiched between two stainless steel electrodes and sealed in the Microcell. The temperature was set between 20 to 60 °C with a step of 10 °C and 20 min of equilibration. Frequency ranged from 1.10–5 Hz to 1 Hz, and the employed amplitude was 10 mV.
## 4.1. Cytotoxicity
To determine the possible cytotoxic effect of the polyphenol iongels, solutions of 100 µg/mL of PVA-[Ch][Van], PVA-[Ch][Sal], PVA-[Ch][Ga], and PVA-[Ch][Caff] were prepared in RPMI 1640 media without phenol red, and then, they were sterilized by filtration through Millipore filter membranes of 0.45 and 0.22 μm pore size. These solutions were kept sterile at 4 °C until their use.
Peritoneal macrophages were obtained by intraperitoneal puncture of CD1 mice (*Mus musculus* strain CD1), and the macrophage suspension was prepared in RPMI + fetal bovine serum $10\%$ + antibiotics (Penicillin-Streptomycin 100,000 IU/L). The macrophage suspension was placed in a sterile conical tube to perform cell counting in a Neubauer chamber and kept at 4 °C until culture.
The animals were kept under the conditions recommended by animal welfare standards and after IACUC (Institutional Animal Care and Use Committee) approval CICUA-044-2021.
Briefly, 100 µL of 1 × 105 cells were plated per well in a 96-well plate and left to stabilize for 2 hours before each experiment to allow cells to adhere to the plate surface. Cells were incubated at 37 °C in a humidified atmosphere of $95\%$ of air and $5\%$ of CO2.
Cell viability assay using XTT: To determine the cell viability of macrophages after exposure to each polyphenol iongel, cell viability was assessed using CyQuant XTT cell viability assay, which includes XTT reagent (2,3-Bis-(2-Methoxy-4-Nitro-5-Sulfophenyl)-2H-Tetrazolium-5-Carboxanilide) and an Electron Coupling Reagent. The XTT reagent, a tetrazolium-based compound, is sensitive to cellular redox potential, and therefore life cells reduce the compound and produce a colored formazan product that can be measured.
After 2 h of cell culture, 100 µL of each polyphenol iongel (100 µg/mL) was added to the cell culture and incubated for 20 h. Then, 100 µL of supernatant were carefully discarded and 70 µL of XTT reagent immediately prepared was added to each well and incubated for 4 h at 37 °C.
Finally, the absorbance (Abs) was measured at 450 nm using a 96-well plate reader, and the cell viability percentage was determined using a culture without any treatment or stimulus as control according to:[1]% cell viability=Abs sampleAbs control ×100 Cell viability assay using vital dye: The macrophages were cultivated in microscope slides. Briefly, 300 µL of 1 × 105 cells were seeded per slide and stabilized for 2 h at 37 °C in a humidified atmosphere of $95\%$ of air and $5\%$ of CO2 before the assay to allow their adhesion to the slide surface.
After the stabilization period, the macrophages culture and the polyphenol iongels were in contact for 24 h in the same incubation conditions, then the supernatant was removed, and phosphate buffer saline (PBS) was added to wash the slide. Immediately, 10 µL of the $0.4\%$ trypan blue solution was added, and living and dead cells were counted under a microscope, expressing the viability in terms of percentage.
## 4.2. In Vitro Hemolysis and Agglutination Test
Serial dilutions from 100 µg/mL of each iongel were prepared and mixed with 50 µL of mice blood, previously mixed with EDTA as an anticoagulant, in a 96-well plate and incubated for 24 h at 4 °C. After this period, a qualitative analysis of lysis and agglutination was done in the stereoscope.
## 4.3. In Chemico Antioxidant Activity
The antioxidant activity (AA) of the polyphenol iongels was determined by a radical scavenging assay using DPPH. An aqueous solution of ascorbic acid (5.7 µmol/L) was used as a reference to compare the results because of its well-known antioxidant activity [25].
A DPPH solution of 0.5 µmol/L was prepared in methanol and added to 100 µL of 100 µg/mL solution of each polyphenol iongel; then, it was incubated for 120 min, and the absorbance at 515 nm was read with a 96-well microplate reader.
DPPH radical scavenging activity was determined according to:[2]%AA=Abs control−Abs sampleAbs control×100
## 4.4. Anti-Inflammatory Assay
The potential anti-inflammatory activity of the polyphenol iongels was tested by analyzing their ability to inhibit or decrease nitric oxide production in LPS-stimulated macrophages using the Griess reagent. The Griess reaction is based on the formation of a chromophore by the reaction of sulfanilamide with nitrite in an acidic medium, followed by coupling with bicyclic amines such as N-1-(naphthyl) ethylenediamine dihydrochloride. Griess reagent was prepared by mixing a solution of N-1-(naphthyl) ethylenediamine dihydrochloride ($0.1\%$ w/v in $5\%$ H3PO4) with a solution of sulfanilamide ($1\%$ w/v in $5\%$ w/v of H3PO4). Parthenolide (10 mM) was used as a positive control because of its anti-inflammatory activity.
After stabilizing the cell culture for 2 h, different stimuli were added to the wells: 75 µL of 1 µg/mL LPS, 25 µL of 50, 100, and 200 µg/mL of each gel, and a mixture of LPS/iongels to evaluate if the phenolic-based materials reduce the nitric oxide produced by the LPS stimulus after 24 h. The colorimetric reaction was obtained by mixing 50 μL of the supernatant, and 100 μL of the Griess reagent, allowing the reaction to take place for 30 min in the dark at room temperature. A nitrite solution of known concentration was used as a standard, and a calibration curve was prepared in the corresponding range according to the behavior of the samples. Then, the absorbance was measured at 540 nm and reported as NO% of the control, where the control is the cell culture without stimulus. [ 3]NO% of control=Concentation of sampleConcentration of control×100
## 4.5. Antibacterial Activity
The antibacterial activity of the iongels was evaluated using a modification of the Kirby–Bauer disk diffusion susceptibility test. A bacterial suspension of *Escherichia coli* was prepared using a 0.5 McFarland standard in sterile saline. Mueller–Hinton plates were previously prepared and then inoculated with the bacterial suspension using a sterile swab by streaking the swab three times over the entire agar surface.
Then, the iongel disks (6 mm) were placed on the agar surface and incubated for 18 h at 35 °C ± 2 °C. After this period of time, the inhibition zone was measured from edge to edge across the zone of inhibition over the center of the disk.
## 5.1. Preparation and Characterization of Iongel Materials
The iongels were prepared using a simple hot dissolution/cooling self-assembly procedure. Using this method, soft solid iongel materials can be obtained in a fast way. Table S1 of the Supporting information (SI) summarizes the different polyphenol iongels obtained, while Figure 1A shows a schematic representation of the PVA-[Ch][Ga] iongel. The chemical structures of the investigated phenolic-based ILs are shown in Figure 1B. Both components of the iongel (IL and PVA) were combined to prepare iongels with $5\%$, $10\%$, and $20\%$ of polymer PVA concentrations. In the case of PVA-[Ch][Ga], only iongels with $10\%$ polymer concentration were obtained, while with $5\%$ did not occur, and for $20\%$, a non-homogenous mixture was obtained. Therefore, the four polyphenol iongels with a polymer concentration of $10\%$ were fully characterized.
ATR-FTIR was used to give more information about the interactions in the polymer network of the polyphenol iongels. Figure S1 shows the ATR-FTIR spectra of neat PVA, the four ILs, and the polyphenol iongels. In particular, Figure 1C displays the FTIR spectra of neat PVA, [Ch][Gal] IL, and PVA-[Ch][Gal] iongel, and it can be observed that the PVA and [Ch][Ga] ATR-FTIR spectra obtained are in agreement with those previously reported [13,14,26]. In the case of neat PVA, the peaks observed at 3014–3680 cm−1 can be attributed to the stretching vibration of OH groups. In addition, there are two bands, one at 2931 cm−1 due to the asymmetrical stretching of -CH groups and another at 2853 cm−1 related to the -CH symmetrical stretching.
Regarding the [Ch][Ga] IL, the band of the OH stretching appears at 3080 cm−1and the one related to the C=O stretching vibration can be seen at 1600 cm−1. On the other hand, the C-OH stretching vibrations, which are typical of phenol groups, appear in the region between 1200 and 1300 cm−1. A peak corresponding to the CN vibration from the cholinium group can also be observed at 1185 cm−1. Other characteristic signals of the IL can be found between 1100 and 750 cm−1, which correspond to the aromatic rings’ -CH out-of-plane bending vibrations. Although it is well known that the shift in the signal corresponding to the C=O stretching from 1710 to 1686 cm−1 is due to the formation of H bonding in PVA-phenolic-based hydrogel, no significant shifts in these bands were detected in the case of PVA-[Ch][Ga] iongel [11].
The thermal characteristics of polyphenol iongels were analyzed by TGA (Figure S2, SI). According to the decomposition pattern, the iongels displayed good thermal stability, with maximum decomposition temperatures (Tmax) between 206 and 242 °C. Additionally, the TGA analysis showed that the iongels bearing the [Ch][Van] IL showed the lowest degradation temperatures at $50\%$ of weight loss (T$50\%$), while the ones containing the [Ch][Caff] IL presented the highest stability (T$50\%$ ≈ 275°C). These results are consistent with the neat ILs’ decomposition profiles shown in Figure S2 and Table S2 of SI.
In wound healing applications, the mechanical and rheological properties of the materials are important aspects to be considered. The materials must be particularly soft, elastic, and flexible to form mechanically compliant interfaces with the skin. Figure 2A shows two typical consecutive compression curves for the axial compression until $40\%$ deformation of the PVA-[Ch][Sal] iongels with $10\%$ polymer concentration. The different cycles performed for each iongel started by the probe contacting the sample at the same point. In all cases, the iongels returned to their initial dimension after being compressed, as seen in Figure 2A. In summary, the material exhibits an elastic behavior, can resist high deformations, and recover its initial shape after load removal during consecutive cycles. All the polyphenol iongel materials revealed similar performances, which agrees with previous reports of similar systems [13,14,15].
Additionally, the rheological properties of the polyphenol iongels were evaluated using DMTA. As an example, Figure 2B shows the viscoelastic behavior of the PVA-[Ch][Sal] iongel as a function of the temperature. At temperatures below 120 °C, the elastic modulus (G′) is greater than the viscous modulus (G′′), indicating that polyphenol iongels exhibit solid-like characteristics. When the temperature increases, a transition from an elastic network to a viscoelastic liquid (G′′ > G′) occurs. The sol-gel transition temperature (Tgel-sol, determined as the temperature at which G′′ = G′) for the polyphenol iongels ranged from 78 to 123 °C (Table S3 of SI). A reversible transition confirms the formation of the iongel structure by H-bonding, which makes this material very interesting to be processed by 3D printing.
Several authors have recently reported an acceleration in the regeneration tissue rate when low currents are applied to the wound [32,33]. Considering the potential of these iongels for electrically-stimulated wound healing, their ionic conductivity was measured (Figure 2C) with obtained values ranging from 1.2 × 10−2 and 7.4 × 10−4 S cm−1 at room temperature. These high values of ionic conductivity, which are in agreement to what has been reported for other iongel systems, [34] indicate that this property could add a further stimulus for wound healing to those provided by the bio-functionality of the iongels components (analyzed below).
## 5.2. Biological Activity
Different assays were carried out to determine the viability of the polyphenol iongels for wound healing applications. Firstly, the cytotoxicity of the iongels was tested on peritoneal macrophages from *Mus musculus* CD1, because of their significant role in the immune response [35]. Additionally, in vitro hemolysis and agglutination tests were carried out with blood from the same mice to evaluate the action of the iongels in erythrocytes as an important effect on wound healing treatments.
As evidenced in Figure 3A, the PVA-[Ch][Van], PVA-[Ch][Sal], and PVA-[Ch][Ga] samples have viability percentages of $99\%$, $100\%$, and $90\%$, respectively, behaving very similar to the control without any stimulus. The PVA-[Ch][Caff] sample showed the lowest percentage of cell viability at around $74\%$.
Cell viability was also determined using the CyQUANT™ XTT Cell Viability Assay, which is suggested for detecting mammalian cell viability [36,37]. As shown in Figure 3B, the PVA-[Ch][Caff] sample showed again the lowest percentage of cell viability 35 ± $0.01\%$, with $65\%$ more cell mortality than the control. Moreover, in the case of PVA-[Ch][Van], PVA-[Ch][Sal], and PVA-[Ch][Ga] samples, they presented 161 ± $0.04\%$, 173 ± $0.09\%$ and 162 ± $0.08\%$, respectively, of cell viability analyzed by this method.
The cell viability percentages above $100\%$ in the CyQUANT™ XTT Cell Viability Assay may be due to three main reasons. The first is cell proliferation, which does not apply in this case because the primary culture used for peritoneal macrophages does not have the potential for proliferation; therefore, this factor is excluded. The second factor is due to the redox potential of the iongels that can reduce the reagent too, but based on the absorbance of the iongels alone with XTT reagent, no significant results were obtained; then, this is not the reason either [38]. Finally, the third possibility is that iongels stimulate macrophages and induce the production of ROS (reactive oxygen species) as a defense mechanism, which, based on the principle of the method used, can interfere in the reduction of the XTT used in this assay. However, this ROS production in a controlled way could have a great potential for wound healing applications because of the immune response stimulation [35,39]. In order to confirm if this stimulation and ROS production caused any damage, these results were correlated with those of cell viability with trypan blue, which do not suggest cell damage.
Additionally, as shown in Figure 4A, the cultures of the PVA-[Ch][Van], PVA-[Ch][Sal], and PVA-[Ch][Ga] samples under the microscope evidence viable cells adapted to cell culture with more elongated morphology unlike the culture exposed to PVA-[Ch][Caff] iongels, where only smaller and rounded cells are evidenced. This result correlates with the viability percentage obtained by both methods by suggesting that the increase in the percentage of cell viability may be due to cellular stimulation.
Regarding PVA-[Ch][Caff], which showed the lowest cell viability when evaluated by both techniques (Trypan blue and XTT) and different cell morphology, Chen et al. described that caffeate derivates have cytotoxic and apoptotic effects and can cause loss of mitochondria membrane potential [40].
This modification in the mitochondria is evidenced as low cell viability because the principle of the XTT assay is to evaluate cell viability through cellular respiration given in the mitochondria, and if there is damage in the membrane and morphology of the cell, the trypan blue dye can get into the cytoplasm confirming cell death.
Moreover, the in vitro hemolysis and agglutination tests help to identify if the iongels present hemolytic properties because they can harm patients’ health and interfere the wound healing process. As it can be observed in Figure 4B, there is no hemolytic effect and agglutination after 24 h of exposure of mice blood to each polyphenol iongel, which was repeated eight times with each material [41].
One of the most interesting properties of these polyphenol iongels are their potential anti-inflammatory properties due to the presence of the phenolic IL anions. The nitric oxide production was assessed via a Griess assay [26,42]. Data are presented as NO% of control (Figure 5) based on a calibration curve using a nitrite standard and as the concentration of NO (Figure S3 of SI) [43]. The assay allowed us to quantify if there was a decrease in NO production between cells treated with LPS and cells treated with LPS in the presence of PVA-[Ch][Van], PVA-[Ch][Sal], PVA-[Ch][Ga], and PVA-[Ch][Caff] materials. As shown in Figure 5, the polyphenol iongels decreased the LPS-induced NO production, indicating the anti-inflammatory activity of the materials. There is an evident inhibition when higher concentrations are used (≤100 µg/mL).
The PVA-[Ch][Caff] sample (200 µg/mL) showed the best anti-inflammatory activity by reducing the NO production more than $63\%$, as the positive control of anti-inflammatory activity used (parthenolide). The PVA-[Ch][Van], PVA-[Ch][Sal], and PVA-[Ch][Ga] iongels diminish in $30\%$, $27\%$, and $35\%$, respectively, the NO production at the same concentration. At lower concentrations (50 and 100 µg/mL), the inhibition percentage is lower, but there is always an inhibition when compared with the culture with LPS stimulus (Figure 5). These results demonstrate better anti-inflammatory activity than those reported for nanostructured cellulose membranes loaded with cholinium-based ILs bearing caffeate and gallate anions [26].
Another key property of polyphenol iongels is their potential antioxidant activity. This property depends on the bioactive phenolic compounds, mainly due to the presence of pyrogallol and catechol groups, which can act as radical scavengers through an electron donation or hydrogen donation mechanism. Antioxidants can react with a stable radical (DPPH) by providing an electron or a hydrogen atom, thus reducing it to 2,2-diphenyl-1-hydrazine (DPPH-H) or analogous substituted hydrazine (DPPH-R) characterized by a pale yellow color that could be easily monitored at 515 nm [26,44]. Antioxidant activity, using DPPH reagent (a widely used technique to evaluate the ability of compounds to operate as free radical scavengers and hydrogen suppliers) is presented as percentage in Figure 6. As a reference, the ascorbic acid solution tested revealed $51\%$ of antioxidant activity [45,46].
The antioxidant activities of the prepared polyphenol iongels varied between $3\%$ to $57\%$, and the PVA-[Ch][Caff] material showed the maximum antioxidant capacity ($57\%$ ± 0.009 and inhibition of the DPPH radical), more than ascorbic acid, followed by PVA-[Ch][Ga] ($32\%$ ± 0.029), PVA-[Ch][Van] ($31\%$ ± 0.011), and PVA-[Ch][Sal] ($3\%$ ± 0.026). These results are in agreement to what was previously reported for the neat ILs [31].
Due to the well know antioxidant properties of polyphenols, different efforts to develop biomaterials employing these compounds have been carried out. For example, Amain et al. have used polyphenols in polymer networks as antidiabetic agents to improve human health care. In this way, the results obtained in our research show that the synthesized iongels have the potential as antioxidant materials, which could be used for different biomedical applications, including wound healing and dermal treatments [47]. Analyzing the tendency and variability between the iongels, the lowest antioxidant activity is for [Ch][Sal], the relation between this reduction and its chemical structure could be due to the presence of other molecules that can be interfering in the reaction because of the chelation of metals by the hydroxyl groups [48]. In salicylates, the possible interactions with non-redox active metals such as calcium or magnesium have been reported and both are components of the culture media used (RPMI) in the biological assays. Studies by Zhao et al. conclude that calcium affects the antioxidant activity of simple phenols extracted from plants, such as hydroxybenzoates [49,50]. The chelation of the calcium ion could explain the decrease in the antioxidant activity of PVA-[Ch][Sal], which has the lowest antioxidant activity compared to the rest of the gels. However, given the potential of these materials for biological applications, this interference should always be considered because of the presence of these metals in blood, sweat, and other biological fluids that may be present in dermal wounds.
Based on the potential use of the proposed polyphenol iongels for wound healing applications and considering the previous reports of the antimicrobial properties of the ILs against antibiotic-resistant pathogens [51], the antibacterial activity of synthesized iongels was also tested. Regarding the antibacterial activity of ILs, Nickfarjam et al. reported the antibacterial properties of different ILs and they observed that this property could be attributed to the adsorption of the IL by the cell membrane through electrostatic interactions, and because of both, penetration of the IL that may produce leakage of the cytoplasm and cell lysis [51]. Furthermore, Ibsen et al. mentioned that ionic liquids have great antibacterial properties that have been previously demonstrated [52].
In order to determine if *Escherichia coli* is sensible to the polyphenol iongels, inhibition halos were compared with the standard pattern obtained with Gentamicin 10 µg/mL, which was reported as sensible with a halo ≥ 15 mm [53]. As shown in Figure 7A,B, the PVA-[Ch][Sal] presented the highest inhibitory activity (25 mm of inhibition), while the PVA-[Ch][Caff] had the lowest. Based on the reference of Gentamicine, we can conclude that *Escherichia coli* is more sensible to the PVA-[Ch][Sal] iongel. The other three iongels also show antibacterial activity but at a lower capacity when compared to that of the PVA-[Ch][Sal].
The highest antibacterial activity of PVA-[Ch][Sal] could be because salicylate and related compounds affect virulence factor production in some bacteria and are well-known as natural and safe antimicrobial agents. One of the mechanisms described for salicylic acid against *Escherichia coli* is the leakage of intracellular alkaline phosphatases and macromolecular substances (nucleic acids and proteins), which suggests the disruption of the bacterial cell wall [54].
## 6. Conclusions
In this work, we present new polyphenol iongel materials bearing PVA and phenolic-based ionic liquids. The iongels were prepared by an easy self-assembly process due to the H-bond interactions between the phenolic IL anion compounds and the PVA polymer matrix. Within the iongels, the phenolic IL anions play a dual role, acting as a crosslinker and as bioactive compounds. These iongels are flexible, elastic, and thermoreversible materials. Moreover, iongels with $10\%$ polymer concentration demonstrated high biocompatibility, non-hemolytic activity, and non-agglutination in mice blood, which are key-sought specifications for materials in wound healing applications. All the iongels showed antibacterial properties, and PVA-[Ch][Sal] showed the highest inhibition halo for Escherichia coli. The polyphenol iongels also revealed high values of antioxidant activity, with the PVA-[Ch][Van] showing the highest activity. Regarding the anti-inflammatory activity, the four iongels showed a decrease in NO production in LPS-stimulated macrophages, with the PVA-[Ch][Sal] unveiling the best anti-inflammatory activity of more than $63\%$ at 200 µg/mL.
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|
---
title: Novel and Facile Colorimetric Detection of Reducing Sugars in Foods via In
Situ Formed Gelatin-Capped Silver Nanoparticles
authors:
- Reda M. El-Shishtawy
- Yasser M. Al Angari
- Maha M. Alotaibi
- Yaaser Q. Almulaiky
journal: Polymers
year: 2023
pmcid: PMC10007220
doi: 10.3390/polym15051086
license: CC BY 4.0
---
# Novel and Facile Colorimetric Detection of Reducing Sugars in Foods via In Situ Formed Gelatin-Capped Silver Nanoparticles
## Abstract
The evolution of green technology for the simple and ecological formation of silver nanoparticles (AgNPs) inspired the present work for simple and efficient detection of reducing sugars (RS) in foods. The proposed method relies on gelatin as the capping and stabilizing agent and the analyte (RS) as the reducing agent. This work may attract significant attention, especially in the industry, for testing the sugar content using gelatin-capped silver nanoparticles as it not only detects the sugar in food, but also determines the content (%), which could be an alternative technique to the conventionally used DNS colorimetric method. For this purpose, a certain amount of maltose was mixed with a gelatin-silver nitrate. Different conditions that may affect the color changes at 434 nm owing to the in situ formed AgNPs, such as gelatin-silver nitrate ratio, PH, time, and temperature, have been investigated. The 1:3 mg/mg ratio of gelatin-silver nitrate dissolved in 10 mL distilled water was most effective in color formation. The development of AgNPs color increases within 8–10 min at PH 8.5 as the selected optimum value and at the optimum temperature of 90 °C for the evolution of the gelatin-silver reagent’s redox reaction. The gelatin-silver reagent showed a fast response (less than 10 min) with a detection limit for maltose at 46.67 µM. In addition, the selectivity of maltose was checked in the presence of starch and after its hydrolysis with α-amylase. Compared with the conventionally used dinitrosalicylic acid (DNS) colorimetric method, the proposed method could be applied to commercial fresh apple juice, watermelon, and honey to prove its viability for detecting RS in fruits; the total reducing sugar content was 287, 165, and 751 mg/g, respectively.
## 1. Introduction
Carbohydrases (glycoside hydrolases or O-glycosidases) are a significant class of enzymes that hydrolyze polysaccharides and low-molecular-weight glycosides. They belong to the hydrolase family of enzymes. Amylase, cellulases, xylanases, mannanases, pectinases, chitinases, and other carbohydrases are categorized according to their selectivity toward natural glycoside substrates. In biotechnology, many carbohydrates have found extensive applications [1]. The majority of carbohydrase activity measurement techniques depend on the examination of reducing sugars (RS) generated as a result of the enzymatic cleavage of a glycosidic bond between two carbohydrates or between a carbohydrate and a noncarbohydrate component [2,3]. In addition to performing particular roles in vital processes, carbohydrates are significant macronutrients that function as a primary source of energy in human nutrition [4]. Deoxyribose and ribose in nucleic acids structure, lactose in milk, and galactose in some oils are a few examples. The four categories of carbohydrates are monosaccharides, disaccharides, oligosaccharides, and polysaccharides. Free-form monosaccharides and disaccharides are commonly referred to as sugars, and depending on how they react chemically, they can be divided into reducing and non-reducing sugars [5]. Sugars are a marker for several nutritional qualities, including flavor, naturalness, and taste [6]. Reducing sugar is essential for biological samples, such as tissue, blood, plasma, and serum. European law regulates the sugar level of some products and beverages [7]. Since sugars are known to be crucial in developing severe diseases (such as obesity and diabetes), determining their identity is a complex analytical challenge. Sugar measurement is required in several intricate biological systems and food and beverage matrices. Soft drinks with added sugar should be given extra attention because they are the primary source of calories in the American diet and raise the risk of obesity [8]. Advanced analytical methods for determining carbohydrate concentrations have been developed as a result of the wide variety of carbohydrates present in these areas, including capillary electrophoresis [9], chromatography [10], infrared (IR) spectroscopy [11], nuclear magnetic resonance (NMR) spectroscopy [12], and light scattering detection [13]. These techniques demand a substantial financial investment, sophisticated analytical abilities, and effort. The colorimetric method, based on a redox reaction in which a reducing sugar acts as the reductant and the reagent as the oxidant leading to the production of color that could be measured by the spectrophotometric method, is one of the most flexible, reasonably simple, and affordable methods for determining reducing sugar. Different techniques for evaluating RS have been used in carbohydrate activity estimations. The Somogyi–Nelson [2], phenol-sulfuric acid [14], anthrone-sulfuric acid [15], Fehling [16], and dinitrosalicylic acid (DNS) [17] methods were evaluated for the determination of RS. The Fehling approach involves several analysis steps (including precipitation and titration). The phenol–sulfuric acid method has a variety of significant limitations. Multiple health risks are associated with phenol employed in this approach. The prolonged or repeated inhalation of phenol fumes brings on lung edema. Long-term phenol exposure seriously impacts the central nervous system [18]. The Nelson–Somogyi assay and the 3,5-dinitrosalicylic acid (DNS) assay described are the most popular methods used by many researchers. Although the DNS assay is known to be approximately 10 times less sensitive than the Nelson–Somogyi assay and it does not provide stoichiometric data with oligosaccharides, giving significantly higher values of RS than the actual number of hemiacetals reducing groups [19,20,21], it has been recommended by the International Union of Pure and Applied Chemistry (IUPAC) commission on biotechnology for measuring standard cellulase activities against filter paper and carboxymethylcellulose (CMC) [22]. Our goal was to develop a visible spectrophotometric method for quantifying RS with high sensitivity. We have developed a new and simple method for determining reducing sugar. Gelatin-silver reagent was found to be suitable for the determination of reducing sugar. In previous literature, gelatin-silver nanoparticles were utilized for several purposes. Gelatin-silver nanoparticles were used as antimicrobial composite films [23], and gelatin-silver nanoparticles coating for polycaprolactone was used for wound healing [24]. Composite films made of chitosan/gelatin-silver nanoparticles were used in biodegradable food packaging [25]. Gelatin, an abundant biopolymer, is both biocompatible and biodegradable. It can be extracted from animal tissues such as muscle, bone, and skin. Gelatin is widely employed in pharmaceutical, cosmetics, food, and medical applications due to its natural abundance and inherent biodegradability in physiological conditions [26]. The most common nanoparticles used in food and other industries are silver nanoparticles. A nanoparticle is described as a tiny item or particle that behaves as a whole unit in terms of its transport and properties. Nanotechnology makes use of the fact that when the size of a solid material shrinks, its surface area grows, enhancing its reactivity and quantum-related phenomena. Nanomaterials’ physical and chemical characteristics can change dramatically from those of the same substance in its bulk size [27]. Consumer products, food technology, textiles/fabrics, and the medical industries are all interested in silver NP due to its chemical and biological qualities. Silver NP also has special optical and physical characteristics that are not found in bulk silver and are said to offer a lot of potential in medicinal applications [27]. Generally, the formation of nanoparticles such as AgNPs has been made possible ecologically using reducing sugars such as glucose [28,29,30,31,32]. As a capping agent, gelatin in the presence of glucose was used for the green preparation of AgNPs [33]. The appearance of visible color due to the surface plasmon resonance (SPR) of nanoparticles has caught the attention of many researchers. The AgNPs SPR appear around 400 nm and, thus, could be applied as an analytical tool for detecting grallic acid [34], o-phenylenediamine [35], formaldehyde [36], and RS using Tollens’ reagent [37]. Gold nanoparticles were also used for the determination of RS [38].
Given the benefits that AgNPs provide, such as their high SPR across a broad spectrum [39], low cost, and environmentally friendly production, it was intended in this work to exploit the SPR of AgNPs that can be formed via a redox process with RS for its detection in the presence of gelatin as a capping agent for the in situ formed AgNPs.
## 2.1. Materials
Silver nitrate, gelatin, maltose, starch, tris(hydroxymethyl)aminomethane, nitric acid, α-amylase from porcine pancreas, and 3,5-dinitrosalicylic acid (DNS) were purchased from Sigma-Aldrich (Saint Louis, MO, USA) and then used as obtained. Watermelon, apple, and honey juice samples were purchased from local market, Jeddah, KSA.
## 2.2. Effect of Gelatin-Silver Reagent Ratio
To determine the gelatin-silver nitrate ratio for effective color formation, the gelatin-silver nitrate reagent was prepared in 10 mL distilled water at a ratio weight by weight (for example, 100 mg/100 mg for 1:1 ratio) 1:1, 2:1, 1:3, 1:2, and 2:2. The conditions at which the best chemical treatment yield obtained was 1:3 gelatin-silver nitrate. A 1 mL of reaction mixture contained 250 µL maltose (0.1 mM), 250 µL Tris–HNO3 buffer PH 8.5 (0.2 M) (prepared by 0.2 M of tris(hydroxymethyl)aminomethane then adjust PH by dilute HNO3 to the appropriate PH), and 500 µL gelatin-silver reagent. The reaction mixture was incubated for 10 min at 90 °C in a boiling water bath before cooling then the absorbance was recorded at 434 nm. The recorded optical density (OD) at 434 nm was used to determine the relative OD (%). Relative OD (%) = (ODx/ODmax) × 100[1] where ODmax is the maximum OD and ODx is OD for a sample with OD less than the maximum OD.
## 2.3. Effect of Time
To determine the effect of time on the evolution of color, a 1 mL of reaction mixture contained 250 µL maltose (0.1 mM), 250 µL of 0.2 M Tris–HNO3 buffer PH 8.5, and 500 µL gelatin-silver reagent (1:3). The reaction mixture was incubated for different times (2–20 min) at 90 °C in a boiling water bath then cooled to room temperature to measure the absorbance at 434 nm. The recorded OD was used to determine the relative OD (%).
## 2.4. Effect of PH on the Silver-Gelatin Reagent
The following buffers were used to determine the optimal PH, 50 mM Tris–HNO3 (PH 6.0–8.5). A 1 mL of reaction mixture contained 250 µL maltose (0.1 mM), 250 µL of different Tris–HNO3 buffers (0.2 M), and 500 µL gelatin-silver reagent (1:3). The reaction mixture was incubated for 10 min at 90 °C in a boiling water bath before cooling then the absorbance was recorded at 434 nm. The recorded OD at 434 nm was used to determine the relative OD (%).
## 2.5. Effect of Temperature
Different scales (30–90 °C) were applied to the reaction mixture containing 250 µL maltose (0.1 mM), temperature 250 µL of different Tris–HNO3 buffers, and 500 µL gelatin-silver reagent (1:3) to assess the impact of temperature on the gelatin-silver reagent. The reaction mixture was incubated for 10 min at different temperature scales in a boiling water bath before cooling then the absorbance was recorded at 434 nm. The recorded OD at 434 nm was used to determine the relative OD (%).
## 2.6. Maltose Selectivity
The gelatin-silver method was validated as selective toward reducing sugar as follows; in the Eppendorf tube, 185 µL of maltose (0.1 mM) was added to 185 µL of starch ($1\%$) and 125 µL of Tris-HNO3 buffer (0.2 M, PH 8.5). In another tube, 185 µL of maltose (0.1 mM) was added to 190 µL distilled water and 125 µL Tris-HNO3 buffer. The third tube contained 185 µL of starch ($1\%$), 190 µL distilled water, and 125 µL Tris-HNO3 buffer. A volume of 500 µL of gelatin-silver reagent (1:3) was added to all tubes. It was then incubated for 10 min at 90 °C in a boiling water bath and then cooled to room temperature to measure the absorbance at 434 nm.
## 2.7. Hydrolysis of Starch with α-Amylase
The hydrolysis of starch was carried out using α-amylase to ascertain the impact of gelatin-silver reagent on the reducing sugar content generated by carbohydrase enzymes. Thus, in the Eppendorf tube, 10 units of α-amylase were incubated for 30 min with 0.06, 1.25, and $1.9\%$ starch (1 mL prepared in 50 mM Tris–HNO3 buffers, PH 7.0) at 37 °C. After that, 1 mL of gelatin-silver reagent (1:3) was added and heat for 10 min at 90 °C to help develop the color. After cooling, the absorbance was recorded at 434 nm. One unit of α-amylase activity was defined as the amount of enzyme producing 1 μmol reducing sugar as maltose per min under the standard assay conditions [17].
## 2.8. Effect of Maltose Concentration and Detection Limit
Different concentrations of maltose (0.2–1.2 mM) were applied, and OD values were recorded following the reaction condition described above. A sample of different concentrations of maltose, 0.2 M Tris–HNO3 buffer PH 8.5, and 500 µL gelatin-silver reagent (1:3) was incubated for 10 min at 90 °C in a boiling water bath before cooling. Then the absorbance was recorded at 434 nm ten times to determine the standard deviation from which and the linear relation of maltose concentration versus OD, the limit of detection (LOD), was obtained.
## 2.9. Real Samples Analysis
Watermelon, apple, and honey juice samples were used as models to determine the effect of gelatin-silver reagent on reducing sugar content in some food samples. The watermelon, apple, and honey samples were supplied from a local market in Jeddah. The fruit of the watermelon and apple were rinsed, skinned, and eliminated waste. One gram of watermelon and apple flesh was crushed separately on a glass grater. The watermelon and apple juices were extracted from the pulps by centrifugation at 6000 rpm for 5 min and filtering using a PTFE filter with a pore size of 0.45 μm. The honey sample was generated by weighing 1.0 g of honey, homogenizing it with distilled water, and then diluting it to 100 mL with distilled water. Reducing sugar was determined as follows: 250 µL of each sample was mixed with 250 µL of different Tris–HNO3 buffers (0.2 M, PH 8.5) and 500 µL gelatin-silver reagent (1:3). The reaction mixture was incubated for 10 min at 90 °C in a boiling water bath before cooling then the absorbance was recorded at 434 nm. A standard curve of maltose concentrations was used to determine the total reducing sugar contents. The total reducing sugar contents were recorded as mg maltose Eq. g−1 of the studied sample.
DNS reagent was utilized to determine each sample’s total reducing sugar contents and compare it with our method. DNS reagent was prepared according to the Miller method [17] as follows: 20 g of potassium sodium tartrate was dissolved in 20 mL distilled water and stirred to totally dissolved, then sodium hydroxide (1 g, 20 mL) was added, followed by 1 g of 3,5-dinitrosalicylic acid prepared in 60 mL of distilled water. While the solution was mixed by magnetic stirrer with a hot plate at 90–95 °C, 50 mg of sodium sulfide was added followed 200 mg of phenol. After the components were dissolved entirely, the final solution was filtered with filter paper, then transferred the solution in dark glass bottles and stored at ambient temperature. Reducing sugar was determined according to DNS reagent: 250 µL of each sample was mixed with 250 µL of different Tris–HCl buffers (0.2 M, PH 7.0) and 500 µL DNS reagent. The reaction mixture was incubated for 10 min at 95 °C in a boiling water bath before cooling then the absorbance was recorded at 560 nm. A standard maltose concentration curve was used to determine the total reducing sugar contents.
## 3.1. Gelatin-Silver Method Optimization
The proposed method for RS detection was optimized, considering some crucial factors, including gelatin-silver nitrate ratio, time, PH, temperature, and color formation. Maltose was selected as a representative RS for the method optimization. Figure 1 shows the effect of the gelatin-silver ratio on the developed optical density (OD) value that was measured at the SPR of the in situ formed AgNPs. As shown in the figure, the ratio 1:3 w/w of gelatin-silver nitrate ratio was most effective in color formation owing to the in situ AgNPs. The inset color image reveals the intensity of the SPR color of AgNPs.
The UV-visible spectrum of the in situ-formed AgNPs is shown in Figure 2. The SPR peak is displayed at 434 nm to confirm the formation of AgNPs due to the occurrence of a redox reaction between silver nitrate and maltose-reducing sugar. The successful appearance of stable color due to gelatin-capped AgNPs confirms their formation in agreement with other similar green syntheses of AgNPs [33].
The effect of time on the evolution of color was monitored spectrophotometrically at the SPR peak. As shown in Figure 3, the development of AgNPs color increases by time up to 8–10 min above, after which there was no further increase, indicating fast response for RS detection in about 10 min. In addition, the OD values remain the same over the studied time, indicating the stability of the in situ-formed AgNPs. Therefore, compared with the conventional DNS method [17], the short-time response and the stability of AgNPs suggest the suitability of the gelatin-silver method for detecting RS.
The PH of the reaction mixture was varied to obtain the optimum PH. As shown in Figure 4, the formation of AgNPs needs a slightly alkaline medium to form the intensive color of AgNPs. The inset color image reveals the color-PH dependent on the SPR color of AgNPs. The results agree with our previous studies and those found in literature [28,29,37]. Subsequently, PH 8.5 was selected as the optimum value of the gelatin-silver reagent. This alkaline PH is favorable for reducing silver nitrate with RS by enhancing the addition of water molecules on the carbonyl groups, as shown in Scheme 1.
The impact of temperature on AgNPs’ color is depicted in Figure 5. Therefore, the detection of RS works well at a temperature close to 90 °C. As a result, 90 °C was used to conduct the gelatin-silver reagent’s redox reaction.
## 3.2. Possible Mechanism for Gelatin-Capped AgNPs
The presence of the aldehyde-containing compound in an aqueous alkaline medium may lead to the addition of water molecules on the carbonyl group and, subsequently, the formation of an oxysilver complex that ultimately converted to silver nanoparticles and carboxylic-containing compound via a redox reaction. The presence of gelatin help stabilizes the nanoparticles, as shown in Scheme 1.
## 3.3. Maltose Selectivity and Starch Hydrolysis
Three samples were tested to validate whether the gelatin-silver method is selective toward RS. The first sample was maltose, the second was a mixture of maltose and starch, and the third was starch only. To these samples, gelatin-silver reagent, as described in the experimental section, was mixed, and the evolution of the color was tracked. Figure 6 shows the OD values of the samples, and the inset shows an image of their colors. It is indicated that the reagent is selective toward maltose-reducing sugar but not starch.
Furthermore, starch hydrolysis was made using α-amylase, and the RS obtained was detected by gelatin-silver reagent, as shown in Figure 7. It is shown that the higher the content of starch, the higher the activity of the α-amylase in increasing the production of RS, as evidenced spectrophotometrically by the gelatin-silver reagent.
## 3.4. Limit of Detection of Maltose
The following equation calculated the limit of detection (LOD); LOD = 3.3 σ/S (40 and elsewhere), where σ is the standard deviation of 10 repeated readings of the measured optical density at 434 nm for a selected sample and S is the slope of the calibration curve (Figure 8) of maltose concentration versus OD values. The LOD obtained is LOD = 46.67 µM. Table 1 shows a comparative LOD limit of reported reagents with the present work. Gelatin-silver reagent reveals higher sensitivity than the conventional time-consuming DNS method. As we know, DNS methods are multistep and complicated processes that take more than 1 h. [17].
## 3.5. Real Samples Analysis
Real sample analysis was made for selected commercial samples, namely, honey, watermelon, and fresh apple juice. The total reducing sugars were determined by the conventional DNS method and the developed gelatin-silver method. Table 2 shows that the gelatin-silver method produced similar analysis data made by the DNS method to suggest its viability for food industries.
## 4. Conclusions
This work is devoted to developing a method for measuring reducing sugars using nanoparticles based on silver nitrate and gelatin. The developed approach was improved by considering many essential aspects, including the amount of reagent, reaction interval, PH, temperature, and the gelatin-silver reagent’s selectivity for starch and maltose. For optimization studies, maltose was selected as the representative reducing sugar. The gelatin-silver reagent showed a fast response (less than 10 min) with a detection limit for maltose at 46.67 µM more sensitive than DNS conventional method. Gelatin-silver nitrate in a ratio of 1:3 w/w produced the best results for color formation. The development of AgNPs color increases within 8–10 min at PH 8.5 as the selected optimum value and at the optimum temperature of 90 °C for the evolution of the gelatin-silver reagent’s redox reaction. In addition, the selectivity of maltose was checked in the presence of starch and after its hydrolysis with α-amylase. Compared with the conventionally used DNS colorimetric method, the proposed method could be applied to commercial fresh apple juice, watermelon, and honey to prove its viability for detecting reducing sugar in food products. The present work explored a viable method for determining the reducing sugar in food industries. Furthermore, the color-based AgNPs would inspire future success in exploiting other colorful nanomaterials to detect reducing sugars.
## Figures, Scheme and Tables
**Figure 1:** *Effect of gelatin- silver reagent ratio on the color evolution. Conditions: A 1 mL of the reaction mixture contained 250 µL maltose (0.1 mM), 250 µL Tris–HNO3 buffer PH 8.5 (0.2 M), 500 µL gelatin-silver reagent (1:1, 2:1, 1:3, 1:2, and 2:2), incubated for 10 min at 90 °C, cooling read absorbance at 434 nm. Each point represents the mean of three experiments ± SE.* **Figure 2:** *Wavelength of AgNPs produced after gelatin-silver reagent (1:3) react with maltose. Conditions: A 1 mL of the reaction mixture contained 250 µL maltose (0.1 mM), 250 µL Tris–HNO3 buffer PH (0.2 M), 500 µL gelatin-silver reagent (1:3), incubated for 10 min at 90 °C.* **Figure 3:** *Effect of time on the color evolution of gelatin-silver reagent. Conditions: A 1 mL of the reaction mixture contained 250 µL maltose (0.1 mM), 250 µL of 0.2 M Tris–HNO3 buffer PH 8.5, 500 µL gelatin-silver reagent (1:3), incubated for different times at 90 °C in a boiling water bath, cooling, recorded the absorbance at 434 nm. Each point represents the mean of three experiments ± SE.* **Figure 4:** *Effect of PH on the color evolution of the gelatin-silver reagent. Conditions: A 1 mL of the reaction mixture contained 250 µL maltose (0.1 mM), 250 µL Tris–HNO3 buffer with deferent PH (0.2 M), 500 µL gelatin-silver reagent (1:3), incubated for 10 min at 90 °C, cooling read absorbance at 434 nm. Each point represents the mean of three experiments ± SE.* **Scheme 1:** *Possible mechanism for the formation of gelatin (Gl) stabilized AgNPs.* **Figure 5:** *Effect of temperature on the color evolution of gelatin-silver reagent. Conditions: A 1 mL of the reaction mixture contained 250 µL maltose (0.1 mM), 250 µL of 0.2 M Tris–HNO3 buffer PH 8.5, 500 µL gelatin-silver reagent (1:3), incubated at different temperature, cooling, recorded the absorbance at 434 nm. Each point represents the mean of three experiments ± SE.* **Figure 6:** *Selectivity for gelatin-silver reagent (1) maltose; (2) maltose: starch; (3) starch. Conditions: A 1 mL of the reaction mixture in sample 1 contained 185 µL of maltose (0.1 mM),190 µL distilled water, 125 µL of Tris-HNO3 (0.2 M, PH 8.5); sample 2 contained 185 µL of maltose (0.1 mM), 185 µL of starch ($1\%$), 125 µL of Tris-HNO3 (0.2 M, PH 8.5); sample 3 contained 185 µL of starch ($1\%$), 190 µL distilled water, 125 µL Tris-HNO3 (0.2 M, PH 8.5); all samples incubated for 10 min at 90 °C, cooling read absorbance at 434 nm. Each point represents the mean of three experiments ± SE.* **Figure 7:** *Reduced sugar produced after starch hydrolysis by α-amylase. Conditions: A 1 mL of reaction mixture contained different concentrations of starch (0.06, 1.25, and $1.9\%$), Tris–HNO3 buffers, PH 7.0, incubates at 37 °C, 1 mL of gelatin-silver reagent (1:3), heated for 10 min at 90 °C, recorded absorbance at 434 nm. Each point represents the mean of three experiments ± SE.* **Figure 8:** *Calibration curve of maltose concentration versus OD. Conditions: See the conditions under Figure 5 using different concentrations of maltose.* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2
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|
---
title: 'Biodegradable Polylactic Acid-Polyhydroxyalkanoate-Based Nanocomposites with
Bio-Hydroxyapatite: Preparation and Characterization'
authors:
- Preeyaporn Injorhor
- Tatiya Trongsatitkul
- Jatuporn Wittayakun
- Chaiwat Ruksakulpiwat
- Yupaporn Ruksakulpiwat
journal: Polymers
year: 2023
pmcid: PMC10007227
doi: 10.3390/polym15051261
license: CC BY 4.0
---
# Biodegradable Polylactic Acid-Polyhydroxyalkanoate-Based Nanocomposites with Bio-Hydroxyapatite: Preparation and Characterization
## Abstract
Biodegradable polymers play a significant role in medical applications, especially internal devices because they can be broken down and absorbed into the body without producing harmful degradation products. In this study, biodegradable polylactic acid (PLA)-polyhydroxyalkanoate (PHA)-based nanocomposites with various PHA and nano-hydroxyapatite (nHAp) contents were prepared using solution casting method. Mechanical properties, microstructure, thermal stability, thermal properties, and in vitro degradation of the PLA-PHA-based composites were investigated. PLA-20PHA/5nHAp was shown to give the desired properties so it was selected to investigate electrospinnability at different applied high voltages. PLA-20PHA/5nHAp composite shows the highest improvement of tensile strength at 36.6 ± 0.7 MPa, while PLA-20PHA/10nHAp composite shows the highest thermal stability and in vitro degradation at $7.55\%$ of weight loss after 56 days of immersion in PBS solution. The addition of PHA in PLA-PHA-based nanocomposites improved elongation at break, compared to the composite without PHA. PLA-20PHA/5nHAp solution was successfully fabricated into fibers by electrospinning. All obtained fibers showed smooth and continuous fibers without beads with diameters of 3.7 ± 0.9, 3.5 ± 1.2, and 2.1 ± 0.7 µm at applied high voltages of 15, 20, and 25 kV, respectively.
## 1. Introduction
The circular economy (CE) has gained attention worldwide. The focus is on balance between the economy, environment, and society, especially recycling and the use of renewable technologies and materials [1]. Nowadays, composite materials are widely used in a variety of industrial sectors, resulting in a significant accumulation of plastic waste in the environment. Plastic composites require end-of-life (EOL) treatments because they cannot be easily disposed of. So, there are various study works attempting to investigate recycling and reusing techniques for plastics and their composite materials [2,3]. On the other hand, the use of biopolymer composites does not necessitate the disposal process because they can be decomposed of by themselves. Therefore, the development of materials from renewable resources is receiving attention from many researchers [4,5,6,7,8,9].
Biopolymers derived from renewable resources such as polylactic acid (PLA), polyhydroxyalkanoate (PHA), and thermoplastic starch (TPS) are a popular alternative to traditional petroleum-based plastics in several applications [10]. Since their advantageous characteristics are renewability, biocompatibility, and biodegradability, they have been mostly used for packaging [11,12].
PLA is made from agricultural raw materials such as fermented plant starch. PLA degrades environmentally in two stages. First, high molecular weight polyester chains are hydrolyzed into oligomers, and then the oligomers are degraded into water, carbon dioxide, and humus [13]. However, PLA has limitations such as its brittleness and low heat resistance [14]. Various PLA composites with different fillers have been developed to overcome these drawbacks [15].
PHA is synthesized from bacteria (both Gram-positive and Gram-negative) that have more than 75 different genera [13]. PHA has biocompatibility, renewability, and biodegradability, but their homopolymers are brittle, with a high degree of crystallinity and narrow processability window. However, the improved PHA was used in biomedical, packaging, and agricultural plastic applications [16].
Although PLA and PHA are often used in biodegradable ap plications to ease environmental problems, some weak properties such as low mechanical properties, low thermal resistance, fragility, and low processability limit their performance. Thus, they have to blend with each other or/and be incorporated with nanoparticles for composite materials to improve their properties and performance [4,11,17,18,19,20,21,22,23]. Zhang and Thomas [17] reported that PLA blended with polyhydroxybutyrate (PHB) can improve tensile strength due to the reinforcement effect of PHB small particles.
In the last decade, nanofillers were one of the choices to overcome the drawbacks of PLA and PHA. Inorganic nanofiller such as nano-hydroxyapatite (nHAp) has received attention from many researchers. Nejati et al. [ 24] have synthesized nHAp/PLA composite scaffolds for bone tissue engineering. They found that nHAp can improve the elastic modulus and compressive strength of their composites. The elastic modulus and compressive strength of composites increased up to 14.9 and 8.67 MPa while that of pure PLLA increased up to 2.40 and 1.79 MPa, respectively. Liu et al. [ 22] have prepared PLA/nHAp composite scaffold by phase separation method. They found that nHAp can improve the thermal decomposition temperature of the composite and its hydrophilicity.
Tissue engineering refers to the attempt to create functional human tissue from cells in a laboratory. Its ultimate goal is to be a cure by repairing or replacing tissues and organs due to disease, traumatic injury, genetic errors, etc. There are four important factors that tissue engineering relies on: 1. the right cells to do the jobs, 2. the right environment such as a scaffold to support the cells, 3. the right biomolecules (growth factors), and 4. mechanical environment to influence the development of the cells [25]. Scaffolds play an important role in the cell growth. Generally, they are biocompatible and biodegradable porous structures. The scaffold requirements are biocompatibility, biodegradability, mechanical properties, scaffold architecture, and manufacturing technology [26]. So, the characteristics of biopolymers meet the requirement for scaffold development and suitable for human use. There are many research studies which developed scaffolds and medical devices from biopolymer composites for tissue engineering and biomedical applications. Fang et al. [ 27] developed electrospun fiber scaffolds from polycaprolactone (PCL), PLA, and HAp for osteoblast-like cells. Kara et al. [ 28] developed nanofibrous composite scaffolds from fish scale/poly(3-hydroxybutyrate-co-3hydroxyvalerate) (PHBV) for bone regeneration. Senatov et al. [ 29] developed highly porous scaffolds from PHB and HAp for small bone defects replacement in the non-load-bearing parts. Prasad et al. [ 30] developed biofilms from PLA incorporated with HAp for use as internal fixation. Promnil et al. [ 31] developed nanofibrous scaffold from PLA and silk fibroin for meniscus tissue engineering.
The functional biomaterials that are entirely derived from natural resources were expected to study and fabricate. In our previous work [9], nHAp powder was successfully prepared from fish scales. In this study, we focused on the development of PLA-PHA-based nanocomposites filled with nHAp. Effects of PHA and nHAp contents on the mechanical properties, thermal properties, and biodegradability of the composites were studied. To study the effect of nHAp contents and PHA contents on PLA-PHA/nHAp composites, PLA-20PHA-based nanocomposite filled with 0, 2.5, 5, and 10 phr of nHAp and PLA-PHA/5nHAp-based nanocomposite filled with 5, 10, and 20 phr of PHA were prepared by solution casting method. The mechanical properties of the composites including Young’s modulus, tensile strength, and % elongation at break were investigated by tensile testing. The thermal stability and thermal properties of the composites were investigated by thermogravimetric analysis (TGA) and differential scanning calorimetry analysis (DSC). Furthermore, the biodegradability of the composites was studied using in vitro degradation, which determines biodegradability by soaking the sample in phosphate buffered solution (PBS). In addition, the composites that gave the desired properties was selected to investigate electrospinnability at different applied high voltages.
## 2.1. Preparation of PLA-PHA/nHAp Composite Films
Nano-hydroxyapatite (nHAp) powder from fish scales with crystallite size of 19.41 nm was prepared in-house, as described in [9]. Briefly, fish scales were deproteinized by hydrochloric acid and treated by alkali heat treatment. Polylactic acid (PLA, Ingeo™ Biopolymer 4043D, General Purpose Grade) was supplied by NatureWorks LLC (Minnetonka, MN, USA). Polyhydroxyalkanoate (PHA, Commercial Grade) was purchased from Cuckoo Trading Hebei Co., Ltd. (Shijiazhuang, Hebei, China). Dichloromethane (DCM, Analytical grade Reagents) was purchased from Carlo Erba (Milano, Italy).
PLA-PHA/nHAp composite films were prepared by solution casting method. First, nHAp powder was dispersed in DCM using a magnetic stirrer for 24 h. PLA and PHA solutions were prepared by dissolving their pellets in DCM at a concentration of 10 wt%. Table 1 shows the formulations of PLA-PHA/nHAp composite films. Each mixture from the formulations was mixed for 72 h using a magnetic stirrer until homogenous. The PLA-PHA/nHAp solutions were poured into a Petri dish for film casting. Then, they were air-dried at room temperature for 24 h and oven-dried at 40 °C for 72 h. After that, the composite films were stored in a desiccator for further characterization and measured for their thickness. The thickness of each sample is approximately 0.50–0.70 mm.
## 2.2. Characterization of PLA-PHA/nHAp Composite Films
The mechanical properties of PLA-20PHA and PLA-PHA/nHAp composites such as tensile strength, Young’s modulus, and % elongation at break were investigated by tensile test (according to ASTM D882-10) using a universal testing machine (INSTRON/5565, Norwood, MA, USA) with a load cell of 5 kN and a crosshead speed of 250 mm/min at room temperature. Five specimens with 1 cm width and 10 cm length from each composite were performed.
The cross-section microstructure of pure PLA, PLA-20PHA, PLA/5nHAp composites, and PLA-PHA/nHAp composites were observed using a scanning electron microscope (SEM, JEOL, JSM-6010LV, Tokyo, Japan). Before testing, the cross-section of the composites after tensile testing were coated with gold sputtering.
The thermal stability of the neat PLA and PHA, PLA-20PHA, PLA/5nHAp composite, and PLA-PHA/nHAp composites was characterized using a thermal gravimetric analyzer (TGA, TGA/DSC1, Mettler Toledo, Schwerzenbach, Switzerland) under a nitrogen atmosphere from 30 °C to 500 °C with a flow rate 50 mL/min and a heating rate 10 °C/min. The TGA curves and the first derivative of TGA curves (DTG) were obtained from the analysis STARe software (version: 16.30).
Thermal properties of neat PHA, PLA-20PHA, and PLA-PHA/nHAp composites were investigated using a differential scanning calorimeter (DSC, DSC 3+ STARe System, Mettler Toledo, Schwerzenbach, Switzerland). The samples were heated from −50 to 200 °C with a heating rate of 10 °C/min, under nitrogen at flow rate of 50 mL/min followed by a cooling process down to −50 °C and second heating with the same procedure. The DSC thermograms provide the thermal properties such as enthalpy of melting (ΔHm), enthalpy of crystallization and cold crystallization (ΔHc, ΔHcc), glass transition temperature (Tg), crystallization and cold crystallization temperature (Tc, Tcc), and melting temperature (Tm). The degree of crystallinity was calculated according to Equation [1] [18]:Χc (%) = [(ΔHm − ΔHc)/(ΔHm0 × w)] ×100[1] where ΔHm0 is the heat of melting of purely crystalline PLA (93 J·g–1) [32] and PHA (146 J·g–1) [33], and w is the weight fraction of PLA in the sample.
In vitro hydrolytic degradation of neat PLA, PLA-20PHA, PLA/5nHAp composite, and PLA-PHA/nHAp composites was determined by soaking in phosphate buffered solution (PBS) at a concentration of 0.1 M and pH 7.4. PBS solution was prepared by dissolving 8.58 g of PBS powder (PBS powder, HiMedia, Maharashtra, India) in 1000 mL distilled water, sterilized by an autoclave at a pressure of 15 lbs at 121 °C for 15 min. The soaked specimens (10 × 10 mm) were incubated at 37 °C for 0 to 56 days. The PBS solution in all test tubes was weekly replaced by fresh PBS. The specimens were removed from PBS and wiped with a filter paper to remove surface water. Then, these specimens were rinsed by distilled water for 3 times and oven-dried at a temperature of 40 °C to a constant weight (Wd). The percentage of weight loss of the specimen during immersion in PBS solution was calculated by Equation [2] [34]:Weight loss (%) = [(W0 − Wd)/W0] × 100[2] where W0 is an initial weight of the specimen and *Wd is* the weight of the specimen after removing from PBS and oven-dried at 40 °C.
## 2.3. Preparation of PLA-20PHA/5nHAp Fibers by Electrospinning Technique and Their Electrospinnability at Various Applied High Voltages
To determine electrospinnability, PLA-20PHA/5nHAp solution at a concentration of 15 wt% was fabricated to be PLA-20PHA/5nHAp electrospun fibers with an electrospinning machine (Nanon, MECC, Fukuoka, Japan). Nanofibers were spun at 90 mm distance to a drum collector, which was covered with aluminum foil. The collector rotation speed was set at 200 rpm. The high voltage between the needle tip and the drum collector was set to 15, 20, and 25 kV. The PLA-20PHA/5nHAp solution was fed at a constant flow rate of 1.0 mL/h. The electrospinning fabrication was carried out until the thickness was sufficient to be measured via diameter. The morphology of electrospun fibers were observed by SEM (JSM-6010LV, JOEL, Akishima, Tokyo, Japan). The fiber diameter was measured from SEM images using image analysis software (Image J 1.53k, Wayne Rasband and contributors, National Institutes of Health, Bethesda, MD, USA).
## Characterization of PLA-PHA/nHAp Composites
Stress-strain curves of neat PLA, PLA-20PHA, and PLA-20PHA/nHAp at various nHAp contents are shown in Figure 1a. All samples show a plastic deformation region that is attributed to a ductile fracture behavior. The mechanical properties of neat PLA, PLA-20PHA, and PLA-20PHA/nHAp at various nHAp contents are presented in Table 2. The results of neat PLA and PLA/5nHAp were obtained from previous work [9]. An increase in nHAp up to 5 phr shows the highest tensile strength (36.6 ± 0.7 MPa) and Young’s modulus (2.1 ± 0.1 GPa) which have nearly the same elongation at break as the other PLA-PHA/nHAp composites. Since nHAp is the nanoparticle filler, its large surface area has a significant impact on the physical interaction between filler and matrix. The reinforcing mechanism of nHAp in PLA matrix has been explained in previous work [9]. So, it could be assumed that the addition of nHAp, especially at 5 phr, is the optimum content that provided a well filler dispersed in the composites, resulting in enhancing the mechanical properties of the PLA-20PHA/5nHAp composite. Figure 1b shows the stress-strain curves of the PLA/5nHAp composite and PLA-PHA/5nHAp composites filled with PHA at various contents. The curves of PLA-PHA/5nHAp composites show plastic deformation region that is attributed to a ductile fracture behavior as already mentioned while the PLA/5nHAp composite shows no plastic deformation that is attributed to a brittle fracture behavior. All of the PLA-PHA/5nHAp composites show poor Young’s modulus and tensile strength compared with PLA/5nHAp composites, as shown in Table 2. It indicated immiscibility between PLA and PHA according to previous works which were observed in the literature [13,17,18,35,36]. However, their elongation at break was enhanced by the addition of PHA. In addition, the effect of PHA contents on the Young’s modulus of PLA-PHA/5nHAp composites shows that they were improved by 5 and 20 phr of PHA compared with neat PLA. It indicated that the PHA act as a reinforcement filler showing the crystalline from PHA’s reinforcement effect [13].
The fractured surfaces of neat PLA, PLA-20PHA and PLA-20PHA/nHAp composites with various nHAp contents were observed by SEM. Figure 2 shows the SEM micrographs of their fracture surfaces. The neat PLA showed a smooth surface and large ligaments. On the contrary, the fractured surface of the PLA-20PHA showed roughness surface with empty cavities of spherical PHA particles which were pulled out during the fracturing [37]. Similar to the PLA-20PHA, the PLA-20PHA/nHAp composite filled with 2.5, 5, and 10 phr of nHAp also showed roughness surface with empty cavities. It could be concluded that PHA-added samples showed a lack of interfacial adhesion between PLA and PHA, inducing poor mechanical properties. This finding corresponds to tensile properties and D’Anna et al. ’s report [37].
Figure 3 shows the fractured surfaces of PLA/5nHAp composite and PLA-PHA/5nHAp composites with PHA at various contents. The presence of empty cavities and ductile ligaments was found in PHA-added samples, especially the samples with 5 and 10 PHA. These findings may be assumed that the addition of PHA induces ductile deformation resulting in the improvement of elongation at break, similar to what El-hadi [38] has reported.
TGA and DTG curves of neat PLA and PHA, PLA-20PHA, and PLA-20PHA/nHAp composites with various nHAp contents are shown in Figure 4. The neat PLA and PHA show one-step degradation. In contrast, the PLA-20PHA and PLA-20PHA/nHAp with all contents of nHAp show degradation in two steps: the first one is degradation of PHA and the second one is degradation of PLA. First mass loss of the PLA-20PHA and PLA-20PHA/nHAp composites is around 17–$24\%$. Their thermal stability was evaluated from DTG curves that are summarized in Table 3. Tonset of the PLA-20PHA/nHAp composites slightly shift to the higher temperature when nHAp content increases. Due to the extremely high thermal stability of nHAp, as shown in our previous work [9], the thermal stability of the composites in this study was improved by the high thermal stability of nHAp. According to Rakmae et al. [ 39], the higher thermal stability of filler acts as a barrier preventing heat transfer to the matrix. It indicated that thermal stability of the composites was improved by the addition of nHAp that has better thermal stability than PLA and PHA.
Figure 5 shows the TGA and DTG curves of neat PLA and PHA, PLA/5nHAp composite, and PLA-PHA/5nHAp composites with various PHA contents. As shown in Table 3, PLA-PHA/5nHAp composites showed the decreasing of their Tonset when increasing PHA contents. This phenomenon was attributed to the lower thermal stability of PHA corresponding to Jimenez et al. [ 18]. From the results of TGA analysis, it could be concluded that the thermal stability of the composites was improved by the addition of nHAp, whereas their thermal stability was dropped by the addition of PHA.
DSC thermograms of neat PLA and PHA, PLA-20PHA, and PLA-20PHA/nHAp composites with various nHAp contents (Figure 6a), and PLA-PHA/5nHAp composites with various PHA contents are shown (Figure 6b). DSC data of all samples are summarized in Table 4. Neat PHA was observed with Tg and Tm at −18.00 and 159.95 °C without Tcc, which corresponds to [40]. Tg of PLA-20PHA/nHAp composites increased with increasing nHAp. The interfaces between organic and inorganic restricted the polymer chain motions, raising the Tg [41]. Tcc of PLA-20PHA/nHAp composites also increased with increasing nHAp. It suggested that nHAp particles inhibited the arrangement of the PLA-20PHA chains in a crystalline structure, resulting in a increase in Tcc [42]. On the other hand, Tg of PLA-PHA/5nHAp composites decreased with increasing PHA contents. It exhibited that PHA act as plasticizer in PLA-PHA/5nHAp composites, which is similar to the results of Olejnik et al. [ 43]. Similar to Tg, Tcc of PLA-PHA/5nHAp composites decreased with increasing PHA contents. In this study, the addition of PHA can promote the crystallization of the PLA-PHA/5nHAp composites, especially at 20 phr of PHA. The addition of PHA increased the crystal phase since it crystallizes as small spherulites that act as nucleating agents for PLA, causing lower Tcc and higher crystallinity [17]. There are two visible melting peaks in the samples which were added PHA. The first peak is PLA crystal melting, while the second one is PHA crystal melting [17,43]. This phenomenon suggested that PLA and PHA are no complete miscibility [44]. However, the addition of nHAp can improve the Tm of PHA in PLA-20PHA/nHAp composites. Although the PLA and the PHA are immiscible, the thermal properties were improved by this combination.
In vitro degradation of neat PLA, PLA-20PHA, PLA-5nHAp, and PLA-PHA/nHAp composites with various nHAp and PHA contents were investigated by the percentage of weight loss of the specimens during immersion in PBS solution for 56 days as shown in Figure 7. The weight loss of neat PLA, PLA-20PHA, and PLA/5nHAp composite after 56 days is $2.46\%$, $3.18\%$, and $2.52\%$, respectively. The weight loss of PLA-20PHA/nHAp composite with 2.5, 5, and 10 phr of nHAp is $3.46\%$, $5.18\%$, and $7.55\%$, respectively. It indicated the in vitro degradation increased with increasing nHAp contents. This was due to the dissolution of nHAp and its hydrophilic properties. Moreover, the agglomeration of nHAp particles makes the liquid medium easy to access, resulting in accelerates the degradation [34,41]. The weight loss of PLA-PHA/5nHAp composites with 5, 10, and 20 phr of PHA is $4.11\%$, $4.91\%$, and $5.18\%$, respectively. Since higher surface area from the surface roughness is a factor influencing the biodegradation rates [16]. In this study, addition of PHA induced surface roughness of the composites. In vitro degradation increased with increasing PHA contents. However, PLA-20PHA/10nHAp showed the highest in vitro degradation. The weight loss results correspond to SEM micrographs in Figure 8. The surface of the samples have changed in morphology after immersion in PBS solution. The surface of the composites were eroded and the rough surface, crack, and hole were created, especially the composites with 20PHA. However, the blend and the composites showed more morphological change than neat PLA.
SEM images of PLA-20PHA/5nHAp composite fibers with applied high voltage at 15, 20, and 25 kV are shown in Figure 9. The continuous PLA-20PHA/5nHAp composite fibers without the formation of beads and phase separation between PLA and PHA were successfully fabricated. The average diameter of the fibers obtained from the high voltage of 15, 20, and 25 kV is 3.7 ± 0.9, 3.5 ± 1.2, and 2.1 ± 0.7 µm, respectively. Since the higher applied high voltage up to the optimum value led to a decrease in the size of the Taylor cone and an increase in the jet velocity resulting in the stretching of the polymer chains, a smaller size fiber was formed [45]. It can be assumed that the fiber diameter decreased with increasing the applied high voltage as long as the applied high voltage was not adjusted over the stable stage, according to Liu et al. [ 46].
## 4. Conclusions
PLA-PHA-based nanocomposites filled with nHAp from fish scales were successfully prepared by the solution casting method. The dispersion of filler in the matrix and compatibility between two or more phases are significant factors in the mechanical properties of the composites. PLA-PHA-based nanocomposite with 20 phr of PHA and 5 phr of nHAp is the optimum content that gave the best mechanical performance with electrospinnability, compared to the other PLA-PHA-based nanocomposites. The addition of PHA induced thermal degradation and promoted in vitro degradation. However, the tensile strength and the thermal stability of the PLA-PHA-based composites were enhanced by the addition of nHAp. Since the composites in this study were prepared from biomaterials with medically interesting properties such as osteoconductivity from nHAp, biodegradation, and biocompatibility from PLA and PHA, they may be used as medical devices in bone tissue engineering. Additionally, PLA-20PHA/5nHAp electrospun fibers from optimal electrospinning conditions will be selected for scaffold fabrication and will be studied in vitro cell culture later.
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|
---
title: Numerical Simulation and Analysis of Turbulent Characteristics near Wake Area
of Vacuum Tube EMU
authors:
- Hongjiang Cui
- Guanxin Chen
- Ying Guan
- Huimin Zhao
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10007246
doi: 10.3390/s23052461
license: CC BY 4.0
---
# Numerical Simulation and Analysis of Turbulent Characteristics near Wake Area of Vacuum Tube EMU
## Abstract
Due to aerodynamic resistance, aerodynamic noise, and other problems, the further development of traditional high-speed electric multiple units (EMUs) on the open line has been seriously restricted, and the construction of a vacuum pipeline high-speed train system has become a new solution. In this paper, the Improved Detached Eddy Simulation (IDDES) is used to analyze the turbulent characteristics of the near wake region of EMU in vacuum pipes, so as to establish the important relationship between the turbulent boundary layer, wake, and aerodynamic drag energy consumption. The results show that there is a strong vortex in the wake near the tail, which is concentrated at the lower end of the nose near the ground and falls off from the tail. In the process of downstream propagation, it shows symmetrical distribution and develops laterally on both sides. The vortex structure far from the tail car is increasing gradually, but the strength of the vortex is decreasing gradually from the speed characterization. This study can provide guidance for the aerodynamic shape optimization design of the rear of the vacuum EMU train in the future and provide certain reference significance for improving the comfort of passengers and saving the energy consumption caused by the speed increase and length of the train.
## 1. Introduction
As a symbol of the development of railway transportation, increasing the speed of rail trains has always been a goal, an objective demand, and a dream that related researchers have been pursuing [1,2,3]. However, an increase in speed inevitably brings many problems that can be ignored at low speeds, such as aerodynamic drag, aerodynamic noise, and aerothermal effect [4,5,6,7]. At present, hundreds of millions of tons of oil are consumed in transportation on the earth every year, which wastes resources and damages the environment [8,9,10]. Building a high-speed transportation network with low energy consumption and environmental protection has become a very urgent need. China’s high-speed rail development is now at the forefront of the world. It is necessary to look farther to build an ultra-high-speed vacuum pipeline train network with a speed of more than 1000 km per hour [11,12,13,14].
A series of aerodynamic problems, such as train passing, crosswind effect, tunnel effect, aerodynamic drag, and wake, etc. will occur when trains are running at high speeds in the open air. Many scholars have conducted in-depth studies on these issues. Baker [15,16] drew lessons from a wide range of train model size and full-scale experiments and calculations, trying to establish a comprehensive flow field diagram. Additionally, for still air conditions and crosswind conditions, the researchers are limited to the train in the open air. Hemida et al. [ 17] analyzed the flow in the near-wake region under transient conditions and believed that the three-dimensional turbulence in the near-wake region has a complex turbulence structure. Choi et al. [ 18] verified the feasibility of dynamic control (active control or adaptive control) in the numerical simulation of channel turbulence. Huang et al. [ 19] compared the four algorithms and obtained the setting method for the calculation of the external flow field of the vacuum pipeline train, which provides a theoretical basis for the simulation calculation. Meanwhile, the characteristics of the distribution of turbulent kinetic energy in the wake area and the correlation between train wind and vortex shedding were discussed. Pan [20] applied the flat-plate boundary layer theory to a detailed comparative analysis of the thickness of the surface boundary layer of the high-speed train body and the distribution characteristics of the surface friction resistance, deriving the three-dimensional average motion equation. Meanwhile, the characteristics of the distribution of turbulent kinetic energy in the wake area and the correlation between train wind and vortex shedding were discussed. Bell et al. [ 21,22,23,24] found periodic vortex shedding in the near wake region of high-speed trains in the 1:10 wind tunnel test and discussed the flow changes of the topological structure of wake vortices and the relationship between train wind and wake vortex structure. Some researchers use the Proper Orthogonal Decomposition (POD) method to study the flow characteristics of the vortex in the wake of a high-speed train. Liu et al. [ 25] studied the dynamic characteristics of a high-speed train wake vortex in the speed range of 200~450 km/h and analyzed the order reduction of strong unsteady flow in the train wake region. Muld et al. [ 26,27,28] extracted the flow field structure near the wake region of the train model, identified a pair of counter-rotating flow vortices, obtained three dominant frequencies of vortex shedding, and found the bending motion of vortex pairs in the evolution process. Xia et al. [ 29,30,31] clarified the dynamic characteristics of the near wake. In addition, the IDDES method is used to study the effects of ground structure and ground effect on the slip flow and near wake of a high-speed train. Wang [32] studied the vortex structure in the wake region of a high-speed train under different Reynolds numbers. With the increase of the Reynolds number, some small-scale vortices appeared in the wake region of a high-speed train, but the large-scale vortex structure did not change significantly. In Yao’s research on the wake characteristics of high-speed trains, it was concluded that the optimization of the aerodynamic performance of the tail car should aim at reducing the strength of the tail vortex system [33]. Sterling et al. [ 34] obtained some conclusions on the characteristics of train slipstreams by comparing all available data sets of high-speed passenger trains and container freight trains. Kim et al. [ 35] selected a high-speed train in actual operation and a tunnel in the service section and evaluated the pressure characteristics of a single train through numerical analysis and experiments. Shin et al. [ 36] used the three-dimensional unsteady Navier-Stokes equation solver to explore the changes in aerodynamic force and the generation of compression waves when high-speed trains enter the tunnel. Gallani et al. [ 37] studied the aerodynamic performance of vacuum pipeline trains. The result shows that under appropriate vacuum pressure, different shapes of train heads and tails have a significant impact on the drag force of vacuum trains in the tunnel. Zhong et al. [ 38] used an improved delayed separation eddy simulation (IDDES) method to study two typical flow fields under different blockage rates and found that as the vacuum degree increases, the thickness of the wake boundary layer and the width of the vortex group increase slightly. Liang et al. [ 39] set the ballast height to 1.825 m to obtain the lowest slipstream velocity in the wake area of the high-speed train, which greatly limits the outward and downward movement of the vortex and improves the flow structure in the wake area. Li et al. [ 40] took the ICE-2 train model as the research object and studied the relationship between aerodynamic resistance and flow structure caused by train operation in time average and time-dependent views. Jia et al. [ 41,42] studied how train length affects the boundary layer, wake, surface pressure, aerodynamic resistance, and friction resistance. Zhou et al. [ 43,44,45] studied the fluctuation phenomenon produced by a vacuum pipeline maglev train at ultra-high speed, deduced the relationship between the critical blocking ratio and the critical incoming Mach Number in the vacuum pipe, and revealed the distribution characteristics of the flow field in the pipe. Kwon et al. [ 46,47] used the numerical calculation method to explore the basic characteristics of the flow field around the vacuum pipe train. Tan et al. [ 48] conducted a transient numerical simulation of maglev trains with different marshaling lengths under the condition of no wind in the open air and analyzed the characteristics of the wake structure. Sui et al. [ 49] studied the effect of vacuum on the flow field around the train cabin in a circular section vacuum tube. Dong et al. [ 50] studied the influence of ground clearance on simplified high-speed train flow, proposing and describing different topologies of train wake. Tian et al. [ 51,52] found that the eddy current around the train is mainly caused by the structure with complex mutation and large curvature change on the train surface, and put forward a series of drag reduction measures. In addition, some other methods have also been proposed in recent years [53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72].
At present, there is no three-dimensional numerical simulation of the turbulence characteristics in the wake of high-speed trains with vacuum ducts. Based on the research and analysis of the turbulence characteristics of the high-speed electric multiple unit (EMU) under the open line condition in the paper, a three-dimensional compressible model is used to numerically calculate the turbulent kinetic energy in the wake region of high-speed trains in the vacuum pipeline. Under the pressure of 0.01 atm and the speed of 800 km/h of a train in the vacuum pipeline, different train formation forms are used as variables to analyze the turbulence characteristics of the rear and wake regions of the EMU.
## 2.1. Fluid Model
In the vacuum tube, when the temperature is constant, the gas density changes with the pressure in the pipe. As the pressure decreases and the density decreases, the gas thinning effect in the tube will become more obvious. A dimensionless parameter that characterizes the rarefied degree of gas in fluid mechanics is abbreviated as:[1]Kn=λ/L where Kn represents the Knudsen Number, λ represents the mean free path of air molecules, and L represents the flow characteristic size.
It is equal to the ratio of the mean free path of gas molecules to the characteristic length of the flow field (such as the linear scale of a spacecraft or satellite or the diameter of aerosol particles), and the larger the Knudsen Number, the thinner the gas. Therefore, the flow can be divided into the following types [73,74,75]: when the Knudsen *Number is* less than 0.01, the gas flow belongs to the category of continuous media. When the Knudsen *Number is* between 0.01 and 0.1, the Navier–Stokes equation with slip boundary conditions can be used to describe the fluid, which is called slip flow. When it is between 0.1 and 10, it belongs to the transition zone; when the Knudsen *Number is* greater than 10, the molecular assumption is adopted, and the Boltzmann equation is used to describe the fluid directly [76].
For gas molecules, the average distance between two adjacent collisions is called the mean free path of the molecule, and its expression is:[2]λ=kBT2πd2P where P represents the pressure in the pipeline, kB represents Boltzmann’s constant, which is 1.3805 × 10−23, d represents molecular effective diameter, and T represents the temperature in the pipeline.
When the temperature is 298 K and the pressure is 104 Pa, the mean free path of a molecule with an effective diameter of 3 × 10−10 is 1.028 × 10−6. For the flow around the high-speed train in the vacuum pipeline, the characteristic length of the flow can be taken as the minimum dimension of the space, that is, the distance between the high-speed train and the ground, which is set to 0.2 m [77,78,79]. According to formula 2, the corresponding value is 5.14 × 10−6, which is less than 0.01, belonging to the continuum model.
## 2.2. High-Speed Train Model and Calculation Domain
In this paper, a certain type of high-speed EMU is used as a prototype to establish a simulation model. The real EMU is usually composed of 8 cars with a total length of about 200 m, including pantographs, bogies, and other structures. If the numerical simulation calculation is performed on the whole vehicle, the calculation amount will be too large to achieve. Therefore, it is necessary to appropriately simplify the EMU model. [ 1]Three length models (denoted as atm1, atm2, atm3) are adopted: head-1 carriage-tail train, head-2 carriages-tail train and head-3 carriages-tail train. The height and width of the model are set in proportion to the actual EMU. According to the experience of dealing with related problems, the simplified model has little change in the basic distribution law of the flow field compared with the complete EMU model.[2]Due to the complex structures of pantographs, door handles, bogies, etc., the structures that affect the grid division are ignored as much as possible, and smooth surfaces are simplified to replace these structures when processing.
Different from the semicircular tunnel used in other documents, the external flow field of the cuboid is directly selected as the calculation domain. It can be compared with the existing literature: whether different tunnel models will have different effects on the tail flow of multiple units. Considering that the front of the vehicle should be a certain distance from the entrance boundary, the proportional coefficients in the three directions are set to 4, 2, and 2, respectively, which simulate the operation of the EMU in the vacuum pipeline. Set the entrance of the flow domain as the velocity inlet and the speed as 800 km/h; then, the corresponding Mach *Number is* greater than 0.3. The air should be regarded as a compressible fluid. The outlet of the flow domain is set as a pressure outlet, and the relative pressure is 0. This article discusses the operation in the vacuum pipeline, and the operating pressure is set to 0.01 atm, which is 1000 Pa. In order to facilitate the calculation and reduce the occupation of computer resources, the surface of the EMU model, the vacuum pipeline, and the ground are set as fixed walls, and the calculation domain is shown in Figure 1.
## 2.3. Meshing
In order to simulate and obtain as much as possible the air flow outside the EMU model and the air flow near the wake area, the boundary layer grid of the EMU model car body and the grid near the wake area need to be encrypted. The settings are as follows:[1]Set the global grid size and scale factor.[2]Set the prism for the car body. The boundary layer grid is a triangular prism grid. Set the height, height ratio, and number of layers. A schematic diagram of the local grid of the front of the car is shown in Figure 2.[3]Set the grid size of the encryption zone. The schematic diagram of the grid around the car body is shown in Figure 3. The minimum mesh quality is 0.305, which is an ideal mesh quality. The mesh quality is shown in Figure 4.
## 2.4. Simulation Method
In this paper, the area of the vacuum tube high-speed train wake flow field is carefully explored, that is, it is necessary to capture sufficient flow information around the high-speed train flow field. Considering the computing power and efficiency of the computer, the IDDES simulation method is used to simulate the turbulent flow of the model. The IDDES model is developed from the DES model, which can realize the transformation of the processing grid between LES and RANS during model solving and simulation. Its expression is as follows:[3]LDES=f˜d(1+fe)Lt+(1−f˜d)CDESLg [4]f˜d=max((1−fd),fB) where f˜d is the conversion function; fd is the conversion function of the DDES model; fB is the conversion function of wall modeled LES (WMLES); fe is the control function of wall simulation as follows:[5]fe=max((fe1−1),0)fe2 During grid size calculation, the influence of wall distance was taken into account:[6]Lg=min(max(Cwdw,Cwhmax,hwn),hmax) where hwn is the step length of grids; hmax=max(Δx,Δy,Δz); *Cw is* a coefficient, and normally set to 0.15 in the IDDES model.
In addition, the flow field is numerically solved by an implicit coupling solver. The convection term adopts the second-order upwind scheme; the second-order implicit scheme is used for time discretization accuracy; Gauss’ least squares method is used for calculation.
## 2.5. Reliability Analysis
The total number of three grid models of atm1 is 3.2 × 106, 5.8 × 106 and 8.3 × 106, as shown in Table 1. The coarse grid, medium grid, and fine grid (CM, MM, XM) of atm1 are selected for grid independence verification. According to the position of the model in the calculation domain, the flow direction position and transverse position of the tail car nose are set as $x = 0$ and $z = 0.$ Figure 5 shows the speed of the high-speed train near the wake area along the flow direction. The results of the monitoring points in the wake area of the high-speed train of the three schemes show that XM and MM are in good agreement, the change trend is basically the same within the flow direction distance, and the speed trend gradually decreases. Although the deviation occurs at 3 h, the overall error is small. The change trend of CM and the former two are roughly similar, but the curve deviates significantly. Figure 6 shows the variation of turbulent kinetic energy in the near wake region of a high-speed train along the flow direction. The three schemes can obviously capture the turbulence information in the wake flow field of a high-speed train. Among them, the simulation results of monitoring points of MM and XM schemes are similar. Considering the comprehensive calculation efficiency, the MM grid encryption scheme is selected as the simulation calculation model of the vacuum pipeline working condition of a high-speed train.
In order to verify the accuracy of the numerical method and the car body model, the MM scheme calculation model is used to simulate the aerodynamic drag characteristics and other related parameters of the high-speed train running in the vacuum pipeline. The pressure in the vacuum pipeline is set to 0.01 atm, the speed of the EMU is 600 km/h, the ambient temperature is 300 K, and the remaining boundary conditions are consistent with the above. The drag coefficient of the EMU is expressed by the dimensionless coefficient as follows:[7]Cd=Fd0.5ρU2S where *Fd is* the drag of the EMU in operation, ρ is air density, U is the speed of the EMU, and S is the cross-sectional area of the EMU.
Shown in Figure 7 is the scatter diagram of the aerodynamic drag coefficient Cd of the vacuum tube EMU in 1 s. The Cd coefficient is mostly distributed near a straight line with an intercept of 14.079 and a slope of −2.039. More than $80\%$ of the *Cd is* between 12.1 and 13.7. The maximum error of the Cd value during the operation of the vacuum tube in the literature [79] is within $8\%$, indicating that the algorithm and model in this paper are correct and reliable and can meet the calculation requirements of the turbulence characteristics in the wake region.
## 3.1. Wake Vortex Structure
The Q-criterion is a classical criterion based on the Galilean invariant vortex definition. With the lowest pressure as the additional condition, the vortex is defined by the positive value of the second invariant Q of the velocity gradient. The theoretical expression is:[8]Q=−12((∂u∂x)2+(∂v∂y)2+(∂w∂z)2)−∂u∂y∂v∂x−∂u∂z∂w∂x−∂v∂z∂w∂y It can be seen from Figure 8 that this pair of vortices is near the symmetrical position $z = 0$ near the wake area of the rear of the vehicle at 800 km/h. With the extension of the longitudinal (x direction), this pair of vortices moves horizontally to both sides. An obvious negative pressure zone appears at the vortex core.
Moreover, Figure 8 is the wake vortex of atm1 model of high-speed train obtained by using Q-criterion and velocity as characteristic scale. According to the location of the train model, the flow position and transverse position of the tail car nose are set to $x = 0$ and $z = 0.$ From the figure, it can be seen that there is a strong vortex in the near wake region of the tail of the car, and it continues to fall off from the tail end. In the process of downstream propagation, it presents a symmetrical distribution, in which the vortex away from the tail car is gradually increasing, but from the characterization of speed, the strength of the vortex is gradually decreasing.
## 3.2. Train Wind
The development of train wind is closely related to the vortex structure. Since the EMU model used in this article is stationary, the train wind speed is defined as follows:[9]us=(U∞−u)2+w2 where u is the instantaneous flow velocity, ω is the instantaneous flow velocity in the span direction. Figure 9 and Table 2 show the comparison between the numerical simulation results and the experimental train wind speed under atm1 conditions, and the speed is dimensionless.
It can be seen from Figure 9 that when the train is in the vacuum pipeline, it will be affected by the tube wall, the speed curve is constantly oscillating, and the velocity peak appears at the flow distance of 1 h, 3 h, and 5 h. Comparing the literature [80], we can see that the two have similar regularity. The simulation data of the open line shows that at $x = 0$, that is, a small oscillation peak appears in the wind speed of the train at the nose of the trailing car, and the speed peak appears again around the downstream position of $x = 5$ h. In this paper, the dimensionless train wind speed value is higher than the speed value in the literature [80]. The vortex away from the chaser is gradually increasing, but from the speed characterization point of view, the strength of the vortex is gradually decreasing.
## 3.3.1. Aerodynamic Characteristics of atm1
Figure 10 shows the pressure field of the front and rear of the EMU model at 800 km/h and 0.01 atm. It can be seen from the figure that there is a maximum pressure near the nose tip, and the pressure gradually decreases backward along the body. Negative pressure appears at the intersection of the front and the body, indicating that vortex shedding occurs here. The pressure distribution of the tail car is similar to that of the head car. The pressure gradually decreases along the car body, and negative pressure appears in the transition area where the uniform section body and the variable section tail intersect.
Figure 11 shows the local velocity vector at the rear space of the tail car. It can be seen when the train is running at high speed, because the air is viscous, the air at the rear of the train will move with the train, thus forming a wake area, which is far affected along the flow direction. In Figure 12, five sections are cut perpendicular to the ground in the wake area of the train to show the pressure distribution at the section. These five sections are arranged in the wake area according to a certain growth ratio. The effect of pressure fluctuations of the vortex on the ground can be observed. During the downstream propagation of the streamwise vortex, due to the interaction between the ground effect and the vortex, it will move vertically downward and simultaneously horizontally outward, approximately symmetrically distributed, and exhibit periodic shedding similar to the Karman vortex street.
## 3.3.2. Drag Analysis of Three Different Length Models
Table 3 shows the aerodynamic drag of three train models with different marshalling lengths when the pressure of the pipeline and the speed of the train are constant. Because the train model is simplified, the calculated resistance value will be smaller than the real value, but the obtained law is accurate. With the increase of the characteristic length of the EMU model in a vacuum pipeline, the aerodynamic drag, including the pressure drag and viscous drag, increases. Compared with atm1, the aerodynamic drag, pressure drag, and friction drag of atm2 increase by $67.71\%$, $65.22\%$, and $73.08\%$, respectively. Compared with atm2, the aerodynamic drag, pressure drag, and friction drag of atm3 increased by $17.91\%$, $13.75\%$, and $26.48\%$. This shows that on the basis of long-grouped vacuum pipeline trains, when the same length of carriages is added, the increment of aerodynamic resistance is smaller than that of short-grouped trains. Additionally, this increment will decrease with the increase in train length.
Figure 13 shows the proportion of pressure drag and friction drag in aerodynamic drag of three different train models. Overall, the pressure drag of the three models accounts for 60~$70\%$ of the aerodynamic drag, and the friction drag accounts for 30~$40\%$ of the aerodynamic drag. When the length of the train model increases, the surface area of the train body also increases, so the proportion of friction drag becomes larger, and the contribution of friction drag to aerodynamic drag is gradually increasing.
## 3.4.1. Under atm1 Conditions
When the vertical position y is 0.18 h, 0.36 h, and 0.5 h, and z is at the positions of 0, 0.13 h, 0.39 h and 0.64 h, respectively, the turbulent kinetic energy along the flow direction changes curve as shown in Figure 14. In Figure 14a, the flow direction range 0 < x < h and the vertical position $y = 0.18$ h, the spanwise position $z = 0$ shows an overall decreasing trend in which the curve rises first and then drops after a peak at $x = 2$; the position of $z = 0.64$ h is relatively flat, but it can be seen from the partial enlarged view of Figure 13a that the curve first drops and then rises, and then drops and rises after reaching the peak repeatedly. The overall trend shows a decreasing trend at the same position as $z = 0.$ The curve of $z = 0.13$ h gradually rises first and starts to rise gently at $x = 25$, and the turbulent kinetic energy gradually decays after reaching a peak of about 840 J; the curve of $z = 0.39$ h shows an upward trend and continues to fluctuate and rise after reaching a peak. Among them, the turbulent kinetic energy at $z = 0$ before $x = 10$ is more active than the other three, and the turbulent kinetic energy at $z = 0.13$ h between 10 < x < 40 is more active than the other three. However, the turbulent kinetic energy curve of $z = 0.39$ h is much higher than the other three at $x = 40.$
Figure 14b shows the turbulent kinetic energy flow direction change curve at different spanwise positions when the vertical position is at $y = 0.36$ h. It can be clearly seen from the figure that there is an obvious regular change in the curve at the spanwise position $z = 0.13$ h: after the curve rises abruptly and peaks, it gradually drops, repeating in sequence, but it can be seen that the peak is constantly decreasing, and the curve as a whole shows a decline. The trend indicates that the turbulent kinetic energy is considerable, and the turbulent kinetic energy is continuously dissipated and decayed during the development of the flow direction; the curve $z = 0$ is similar to the curve $z = 0.13$ h. After a certain distance along the flow direction, the turbulent kinetic energy becomes active, and the curve gradually rises and begins to attenuate after the peak appears. In combination with Figure 14b, it can be seen that the curve at the spanwise position $z = 0.64$ h also has a similar rule to the former; the curve at the spanwise position $z = 0.39$ h gradually rises, reaching about $x = 20$ It decays after the peak, and starts to rise steadily after $x = 25.$
Figure 14c shows the change curve of the turbulent kinetic energy of the flow at different spanwise positions at the vertical position $y = 0.5$ h. It can be seen from Figure 8d that the turbulent kinetic energy at positions $z = 0$ and $z = 0.13$ h still has the above-mentioned regular pattern at this time. In the spanwise position $z = 0.39$ h, the curve fluctuation rises and reaches the peak at $x = 13$ ($x = 0.256$ h), and then the fluctuation drops; the turbulent kinetic energy at $z = 0.64$ h is much higher than the other three, and the maximum turbulent kinetic energy can reach 66 J. From Figure 13c, it can be found that the turbulent kinetic energy at the four spanwise positions at the vertical direction $y = 0.5$ h is constantly attenuating the distance along the flow direction. When the vertical position is higher, the turbulent kinetic energy is mainly concentrated on both sides of the middle section, that is, the position where the absolute value of z in the span direction is larger.
A longitudinal comparison of the turbulent kinetic energy curves at three different vertical positions in Figure 15 shows that each vertical position has the maximum value of turbulent kinetic energy ($y = 0.18$ h is about 935 J; $y = 0.36$ h is about 248 J; $y = 0.5$ h is about 67 J) is gradually decreasing, and the energy of the wake vortex is constantly attenuating, indicating that the wake vortex in the near-wake region is mainly concentrated near the lower end of the nose tip near the ground and continues to propagate downstream.
## 3.4.2. Under atm2 and atm3 Conditions
Figure 16, Figure 17, Figure 18 and Figure 19 show the change curve and partial enlarged view of the turbulent kinetic energy along the flow direction under atm2 and atm3 conditions. Comparing the changes of the turbulent kinetic energy flow in the wake area under three different characteristic lengths, it can be found that the turbulent kinetic energy curve at each position is similar to the above-mentioned change trend, and the fluctuations begin to attenuate or increase after reaching their respective peaks.
## 4. Conclusions
In this paper, the IDDES method is used to simulate the flow field around the simplified model of high-speed EMU based on vacuum pipes, so as to obtain and analyze the aerodynamic resistance of high-speed EMU and its turbulent characteristics in the near wake region. The relevant conclusions are as follows:[1]There are strong vortices in the wake area, and the energy carried by them is concentrated at the lower vertical position. The tail vortex is falling off from the tail end. In the process of propagating downstream, the vortex structure away from the tail vehicle is gradually increasing, but from the characterization of velocity, the strength of vortex is gradually decreasing.[2]When the vertical position is closer to the ground, the train wind shows a chaotic state, and the vortex has a large energy concentration near the ground. In the wake area, the train wind speed peak appears many times, but the peak value is gradually decreasing, and the overall trend is gradually decreasing.[3]When the train speed is constant, with the increase of the characteristic length of the EMU model, the train surface area increases, the proportion of differential pressure resistance decreases gradually, the proportion of viscous resistance in aerodynamic resistance is more significant, and the contribution to aerodynamic resistance increases.[4]The turbulent kinetic energy of the three train models with different lengths is basically the same, gradually decreasing and then rising to the peak, repeating many times and gradually decreasing. The maximum turbulent kinetic energy of each vertical position is also gradually declining, indicating that the wake vortex in the near wake area is mainly concentrated near the lower end of the nose tip near the ground, and continues to propagate downstream and move horizontally.
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|
---
title: Enzymatic, Antioxidant, and Antimicrobial Activities of Bioactive Compounds
from Avocado (Persea americana L.) Seeds
authors:
- Kaja Kupnik
- Mateja Primožič
- Vanja Kokol
- Željko Knez
- Maja Leitgeb
journal: Plants
year: 2023
pmcid: PMC10007261
doi: 10.3390/plants12051201
license: CC BY 4.0
---
# Enzymatic, Antioxidant, and Antimicrobial Activities of Bioactive Compounds from Avocado (Persea americana L.) Seeds
## Abstract
The aim of this research was to identify and quantify biologically active compounds from avocado (*Persea americana* L.) seeds (AS) utilizing different techniques with the use of ultrasound (US), ethanol (EtOH), and supercritical carbon dioxide (scCO2) for possible applications in (bio)medicine, pharmaceutical, cosmetic, or other relevant industries. Initially, a study of the process efficiency (η) was carried out, which revealed yields in the range of 2.96–12.11 wt%. The sample obtained using scCO2 was found to be the richest in total phenols (TPC) and total proteins (PC), while the sample obtained with the use of EtOH resulted in the highest content of proanthocyanidins (PAC). Phytochemical screening of AS samples, quantified by the HPLC method, indicated the presence of 14 specific phenolic compounds. In addition, the activity of the selected enzymes (cellulase, lipase, peroxidase, polyphenol oxidase, protease, transglutaminase, and superoxide dismutase) was quantified for the first time in the samples from AS. Using DPPH radical scavenging activity, the highest antioxidant potential ($67.49\%$) was detected in the sample obtained with EtOH. The antimicrobial activity was studied using disc diffusion method against 15 microorganisms. Additionally, for the first time, the antimicrobial effectiveness of AS extract was quantified by determination of microbial growth-inhibition rates (MGIRs) at different concentrations of AS extract against three strains of Gram-negative (Escherichia coli, Pseudomonas aeruginosa, and Pseudomonas fluorescens) bacteria, three strains of Gram-positive (Bacillus cereus, Staphylococcus aureus, and Streptococcus pyogenes) bacteria, and fungi (Candida albicans). MGIRs and minimal inhibitory concentration (MIC90) values were determined after 8 and 24 h of incubation, thus enabling the screening of antimicrobial efficacy for possible further applications of AS extracts as antimicrobial agents in (bio)medicine, pharmaceutical, cosmetic, or other industries. For example, the lowest MIC90 value was determined for B. cereus after 8 h of incubation in the case of UE and SFE extracts (70 μg/mL), indicating an outstanding result and the potential of AS extracts, as the MIC values for B. cereus have not been investigated so far.
## 1. Introduction
Avocado (*Persea americana* L.) is a nutritious tropical fruit belonging to the Lauraceae family and is known also as alligator pear. Due to its taste and texture and medicinal and nutritional properties, avocado has become indispensable on menus around the world, which has increased its production over the last decade. Its higher direct consumption and industrial processing is reflected in its fastest and highest production growth in 2020 and in previous years among tropical fruits, especially for the most popular Hass variety [1]. Accordingly, avocado seeds (AS), which are underutilized as inedible part and therefore discarded, can account for up to $26\%$ of the total fruit weight [2] and represent large amounts of waste biomass but are, on the other hand, an inexpensive alternative source of potential bioactive compounds that remain unrecovered.
In addition to the exceptional nutritional quality of avocado fruits, the chemical profile of various AS extracts comprises a high content of phytochemicals exhibiting a variety of biological activities. These include polyphenols, triterpenoids, acetogenins, fatty acids, and other compounds that have resulted in various properties and are beneficial to health because of their antihypertensive, antimicrobial, antioxidant, larvicidal, and hypolipidemic activity [3,4,5]. For example, aqueous AS extract showed a reduction in low-density lipoprotein cholesterol, total cholesterol, and triglycerides levels in hypertensive rats and also reduced their blood pressure [6], while aqueous AS extract reduced hyperglycemia in adult male rabbits [7]. AS extracts are also promising in the management of diabetes mellitus, as blood glucose levels in rats were significantly decreased because of antihyperglycemic and hypoglycemic activities [8], and inhibitory activities on some type 2 diabetes enzymes (α-glucosidase and α-amylase) have been also demonstrated [9]. Additionally, avocado leaves and seeds are also used in traditional medicine for the management of Alzheimer’s disease, which was also supported by an in vitro study that showed antioxidant and anti-cholinesterase activities [10]. The in vitro cytotoxic properties of AS against divergent types of cancer cell lines have also been reported in the literature [11,12,13] and have been examined in preclinical animal models [14].
For retrieval of the mentioned phytochemicals and biologically active substances from AS, mainly traditional and conventional methods have been used, including maceration, Soxhlet extraction (SE), and steam distillation, as these methods are easy to use and are well established. Nevertheless, these methods are time-consuming, require large amounts of organic solvents, and operate at high temperatures. Consequently, this leads to higher emissions of volatile organic solvents, can result in degradation of thermo-labile compounds, and significantly reduce the biological activity of the obtained active ingredients [15]. These shortcomings have recently prompted studies of more efficient, advanced, and green extraction techniques including ultrasonic (UE) [16], enzyme-assisted [17], cold-pressed [18], microwave [19], and supercritical fluid (SFE) extraction [20,21].
Among the above-mentioned extraction techniques, UE is one of the emerging methods, as it often provides a high extraction yield, uses mild temperatures, and is simple and inexpensive. Due to the frequency of ultrasonic irradiation, vibration occurs between matrix molecules, which leads to cavitation and, consequently, by creating macroturbulences and high-velocity inner-particle collisions, to easier leaching of bioactive compounds from the plant matrix into the solvent [22]. On the other hand, SFE with carbon dioxide (scCO2) as a solvent provides separation at near-ambient temperatures, therefore minimizing the degradation of thermo-labile compounds. Moreover, due to its green process (e.g., solvent-free products, short extraction time, low operating temperature, high extract quality, environmental friendliness, simplicity, etc.) and the great characteristics of scCO2 (e.g., innocuous to environment and human health, non-toxicity, mild critical temperature, low critical pressure, non-flammability, odorless nature, prevent extract oxidation, easily volatilized, etc.), it is one of the most promising techniques in the recovery of pure and clean extracts with bioactive compounds that can be used in biomedicine and for cosmetics, pharmaceutical products, nutraceuticals, and functional food [22,23]. It should be noted that a very limited number of studies using SFE for extraction of biologically active compounds from AS has been found in the literature. Hence, this investigation focuses on phytochemicals and bioactive compounds derived from AS and compares well-established UE and SE to the sustainable extraction method of SFE, which has never been done before. The findings of this study present useful information and tools, as avocado by-products from industrial processing could be used to generate a functional, high-value-added product with biological activity through efficient, feasible, and sustainable extraction processes.
The content of bioactive compounds and overall yield are vigorously influenced by the method of extraction and by the choice of solvent. Therefore, the presented research emphasizes the comparison of different extraction techniques (UE, SE, and SFE), process conditions (temperature and time), and solvents (H2O, EtOH, and CO2 with EtOH as co-solvent) used on the production yield of phytochemicals and high-value-added bioactive compounds such as phenols (TPC), proanthocyanidins (PAC), proteins (PC), and specific polyphenols (i.e., flavonoids and phenolic acids) in the AS extracts. A major contribution and the importance of our research is in the comparison of SFE results with the results obtained from the remaining, more widely used extraction methods, namely UE and SE. To the best of our knowledge, and after reviewing the literature, the content of certain polyphenols in SFE AS extracts has not yet been reported. This is also the first study in which various enzymatic activities (α-amylase, cellulase, lipase, peroxidase, protease and transglutaminase, polyphenol oxidase, and superoxide dismutase) have been determined in UE, SE, and SFE AS extracts and compared. In addition to the various enzymatic activities, the antioxidant and antimicrobial activity of UE, SE, and SFE extracts were also evaluated and compared. Antimicrobial activity was determined against 15 microorganisms including Gram-negative, Gram-positive bacteria, and fungi. Additionally, the antimicrobial activity of the extracts was quantified after 8 and 24 h of incubation by determination of microbial growth-inhibition rates (MGIRs) at five different concentrations and by determination of MIC90 value, which, to the best of our knowledge, has not yet been so thoroughly studied. Our study provides a comprehensive insight and new information on the recovery of bioactive compounds from AS, which could be potentially used in cosmetics, functional food, nutraceuticals, pharmaceutical products, and (bio)medicine.
## 2.1. Effect of Changing Solvent and Techniques on Process Efficiency
The results in Figure 1 represent the extraction yield and thus indicate the efficiency of the performed AS extractions using three different methods (UE, SE, and SFE). UE provided the highest yield with $12.11\%$, followed by SE ($9.88\%$) and SFE ($2.96\%$). There was also a statistically significant difference between extraction yields as determined by one-way ANOVA. A Tukey’s post hoc test revealed that the extraction yield was statistically significantly lower using SFE ($p \leq 0.001$) as the extraction method compared to UE and SE.
The explanation lies in the use of different solvents in the extractions. H2O, which was used in UE, is the most polar solvent. Following in polarity is EtOH, used in SE. CO2 in SFE is non-polar, and therefore, EtOH was added as a co-solvent, which increases its polarity and thus enables better solubility and isolation of more polar phytochemicals such as polyphenols. Additionally, grinding the AS before the extraction processes allowed the reduction of particle size and increased the surface area, which contributed to higher extract diffusion (and consequently higher η) during the extraction process. Tan and colleagues [24] recently performed aqueous UE and achieved a $15.13\%$ yield. SE with EtOH and SFE with EtOH as co-solvent was performed by Páramos et al. [ 21]. The study revealed that among the solvents, EtOH provided the best yield with SE (8.2–$10.3\%$), while in SFE, the addition of EtOH as a co-solvent allowed yields from 1.9 to $6.9\%$. The higher yields of SE are mainly due to the longer extraction time and the greater amount of the more polar solvent used, which otherwise speaks in favor of SFE, leading to lower energy and material consumptions. According of the reviewed literature, a small number of studies reported on the extraction of biologically active compounds from AS using SFE, and even fewer reported extraction yields. The before-mentioned research by Páramos et al. [ 21] studied the influence of temperature and pressure, as main operating variables, on extraction efficiency. The highest extraction yield ($6.9\%$) was achieved at 250 bar and 80 °C. Recently, Restrepo-Serna et al. [ 25] reported on a biorefinery approach for valorization of AS through SFE. SFE was conducted at 80 °C and 250 bar, and the yield of AS extract amounted to 0.12 g/g of raw material. On the other hand, Wang et al. [ 26] performed SFE at 50 °C and 250 bar using only CO2 as solvent and achieved a yield of $1.61\%$. On that account, the choice of using EtOH as a co-solvent turned out to be favorable. Hereafter, to increase the efficiency of SFE itself, it would be necessary to optimize important variables (operating conditions) in the process.
## 2.2. Presence of Phytochemicals in AS Samples
The AS extracts were subjected to a phytochemical screening using various well-known standard qualitative methods. The results are shown in Table 1.
It is important to highlight that alkaloids, flavonoids, phenolic compounds, saponins, steroids, terpenoids, and quinones were present in all three types of extracts. The research showed the absence of anthraquinones, carbohydrates, cardiac glycosides, emodins, phlobatannins, and tannins. Anthocyanins and coumarins were present in the UE and SE extracts, while they were absent in the SFE extract, which can be justified by explaining the polarity of the solvents used in the extraction process. Anthocyanins are known as water-soluble flavonoids [27] and were not extracted by SFE most likely due to the lower polarity of scCO2 and EtOH solvent mixtures. Coumarins are well soluble in EtOH and slightly soluble in H2O, while the solubility of scCO2 depends on the variables in the extraction process itself (operating pressure and temperature) [28,29], and therefore, it is possible that they were not extracted under the applied operating conditions.
No phytochemical screening on SFE avocado seed extracts was detected in the reviewed literature, which means that this study is the first to provide additional insight into a more modern, green, and unconventional technique for extracting phytochemicals from AS. On the other hand, quite a few studies [30,31,32,33] with a preliminary screening of phytochemicals in AS extracts obtained by conventional extractions have already been published, but the results themselves are difficult to compare due to the different extraction methods, conditions, and solvents used. For example, Rivai et al. [ 34] found the presence of tannins and the absence of flavonoids, terpenoids, saponins, and steroids in EtOH and H2O extracts of AS, which is inconsistent with results in the presented study. On the contrary, Oboh et al. [ 10] found the presence of alkaloids, saponins, and terpenoids and the absence of anthraquinones and phlobatannins in the aqueous extract, which is in line with the presented results. Here, it is necessary to emphasize, however, that the chemical profile, the content, and activities of biological compounds from AS may differ due to the influence of the variety, origin, season, maturity, growth, post-harvest, and environmental conditions [35].
## 2.3. Content of Total Phenols, Proanthocyanidins, and Total Proteins in AS Samples
As seen in the previous subsection, the presence of phytochemicals in AS extracts, the method, and the solvent used greatly affected both the extraction efficiency and the successful extraction of various phytochemicals. Accordingly, a quantitative investigation of the content of TPC, PAC, and PC of the examined AS extracts was carried out. The results of the study are presented in Figure 2.
TPC obtained with SFE was the highest among all extracts and amounted to 36.01 mg GAE/g of extract. Compared to SFE, TPC with UE (6.31 mg GAE/g) was almost six times lower, while TPC with SE (15.91 mg GAE/g) was almost two times lower than SFE. In the reviewed literature, the TPC values for AS extracts ranged up to 146.60 mg GAE/g with UE using hydroalcoholic mixtures as solvents [36], while no TPC values were detected in the reviewed literature using only H2O as a solvent. Segovia et al. [ 37] demonstrated that ultrasonic power and temperature have a huge impact on the extraction of polyphenols with H2O as solvent, as with the increase of the mentioned parameters, the TPC should also be enhanced. Therefore, it is likely that the UE efficiency of the present study is lower due to the low operating temperature (20 °C), and with more optimal conditions (higher operating temperature), the UE yield of AS extracts could be increased. With SE using EtOH as solvent, the TPC values ranged between 19.87–29.92 mg GAE/g [21,38], while with SFE, the TPC values reached up to 51.36 mg GAE/g [21]. Hence, the lower TPC value for SE compared to SFE is also justified, as SE needs higher operating temperatures to improve solvent solubility and to reduce its viscosity and surface tension, while high temperatures can lead to degradation of certain phenolic compounds and therefore lower TPC. On the other hand, low viscosity and near-zero surface tension allow scCO2 to easily penetrate the matrix material to extract phenolic compounds. Additionally, Kruskal–Wallis H test showed that there was a statistically significant difference between TPC prepared by different extraction methods. A post hoc Dunn–Bonferroni test revealed that there was statistically higher content of TPC present in extract obtained by SFE compared to SE and UE.
Hereafter, PACs were determined in AS extracts. The experiments showed that the PAC value was the lowest in the SFE extract (2.84 mg/g), followed by the UE extraction (24.67 mg/g). The highest PAC value, 32.13 mg/g, was detected in the AS extract obtained with SE. After a thorough review of the already-published literature, it was found that the PAC value was not quantitatively determined comparatively in AS extracts. Only a study by Noorul et al. [ 39] determined PAC using the vanillin-H2SO4 protocol in AS extracts obtained by SE. The EtOH extract showed 13.70 μg catechin/mL and the H2O extract 9.30 μg catechin/mL. PACs are phytochemicals with added value, as their health aspects (e.g., antimicrobial, antioxidant, anticancer, antidiabetic, and neuroprotective activities) have been validated [40,41]. The mentioned results of the presented study are important data for potential further applications of AS extracts in biomedicine, pharmaceutical products, cosmetics, functional food, and nutraceuticals. Additionally, PC in AS extracts was examined. The highest PC value is contained in the SFE extract (16.10 mg/g), followed by the UE extract (12.43 mg/g) and finally the SE extract (11.74 mg/g). In the reviewed literature, quantitatively determined PC was not detected in AS extracts, but in some studies [42,43,44], proximate composition analyses were performed, where PC values reached up to $23.0\%$. The use of scCO2 affects the AS cells by damaging the cell walls, which results in the release of intracellular materials. Consequently, intracellular proteins contribute to PC [45], which explains the higher content in the SFE extract. Regarding content of PAC and PC, there was also a statistically significant difference using different extraction methods.
## 2.4. Content of Certain Phenolic Compounds in AS Samples
A comprehensive analytical characterization of certain phenolic compounds was performed in AS extracts. The HPLC allowed effective and rapid separation and identification of 14 divergent phenolic compounds based on comparison of retention times with corresponding standards. The content of three flavonoids (epicatechin, hesperidin, and quercetin), ten phenolic acids (benzoic acid, 2,3-dihydroxy benzoic acid, 4-hydroxy benzoic acid, caffeic acid, chlorogenic acid, cinnamic acid, p-coumaric acid, ferulic acid, gallic acid, and salicylic acid) and vanillin was determined in UE, SE, and SFE extracts of AS, which are presented in Table 2.
Regarding the flavonoids in AS extracts, the common fact between the UE and SE extracts is that hesperidin predominated (118.10–226.78 mg/100 g DW), while its content was lower in the SFE extract (9.37 mg/100 g DW). In comparison, hesperidin was also detected by Zaki et al. [ 46], but its content was much higher in peel extracts (up to 61.94 mg/100 g DW) than in seed extracts (up to 2.62 mg/100 g DW). However, the presence of quercetin was found in the SFE extract (58.07 mg/100 g DW), while it was not detectable in the remaining extracts. In the reviewed literature, different quercetin contents in AS extracts are reported, ranging from 0.07 to 88.18 mg/100 g [5,19,46,47,48]. Since epicatechin is a building block of proanthocyanidins, whose total content was high, especially in the UE extract, it was unambiguously identified in all AS extracts (15.56–39.20 mg/100 g DW) by comparing the retention time with that of the standards. It has also been confirmed many times in similar AS extracts in the reviewed literature [5,16], up to a content of 2.91 mg/100 g DW.
When comparing the presence of valuable phenolic acids in AS extracts, 2,3-dihydroxybenzoic acid (also known as pyrocatechuic acid) content was the highest (14.42–106.48 mg/100 g DW) in all extracts (UE > SE > SFE). The same sequence of the AS extracts (UE > SE > SFE) can also be detected in the content of 4-hydroxybenzoic acid, which is also known as p-hydroxybenzoic acid (3.47–15.41 mg/100 g DW), while benzoic acids were found only in UE extract. All mentioned acids were also identified in the reviewed literature [5,47,49]. Caffeic acid has only been detected in SFE extract at a content of 5.92 mg/100 g DW, while chlorogenic acid appeared in all AS extracts (UE > SE > SFE, 0.73–9.36 mg/100 g DW) as well as cinnamic acid (UE > SFE > SE, 11.33–34.36 mg/100 g DW). The content of caffeic acid (4.18–60.19 mg/100 g), chlorogenic acid (0.05–2.61 mg/100 g), and cinnamic acid (up to 0.09 mg/100 g) was previously determined by other researchers [5,19,46,48]. Next, the highest content of p-coumaric acid was detected in the SE extract (6.26 mg/100 g DW), followed by the SFE and UE extracts. The literature indicates p-coumaric acid content in AS extracts from 0.62 to 10.23 mg/100 g [19,46,48]. Hereafter, ferulic acid was found in SE and UE extracts (up to 6.86 mg/100 g DW), gallic acid in SFE and UE extracts (up to 3.99 mg/100 g DW), and salicylic acid in SE and SFE extracts (up to 15.15 mg/100 g DW). For comparison, the content values for ferulic acid in the reviewed literature are between 0.09–82.53 mg/100 g, for gallic acid between 1.21–67.38 mg/100 g, and for salicylic acid between 2.57–5.50 mg/100 g [5,19,46,48]. Finally, vanillin was also previously determined in AS extracts [47] and is known as an antimicrobially active phenolic compound [50,51]. In the presented study, vanillin was identified in all extracts (UE > SE > SFE) and quantified up to a content of 55.02 mg/100 g DW.
Overall, the highest total content of analyzed phenolic compounds was contained in UE extract (393.94 mg/100 g DW), followed by SE extract (351.03 mg/100 g DW) and SFE extract (177.99 mg/100 g DW). In the presented study, compared to the contents of other authors, the high contents of hesperidin, epicatechin, cinnamic acid, and salicylic acid stood out. Additionally, 2,3-dihydroxybenzoic acid, 4-hydroxybenzoic acid, benzoic acid, and vanillin have been identified in already-published research, while their quantification in AS extracts has not yet been detected. Importantly, many studies have examined the content of different phenolic compounds in AS extracts obtained by different extraction methods and under different conditions and with different solvents. Therefore, it must be emphasized that there are certain deviations in the results of various studies, as the chemical profile and the content of biologically active compounds in AS extracts differ due to the previously mentioned different extraction conditions as well as due to the variety and maturity of the fruit used in the studies. What gives great importance to the presented study is the characterization and quantification of selected flavonoids and phenolic acids in SFE extracts since no similar study could be detected in the reviewed published literature heretofore. However, the optimization of important variables (operating conditions) in SFE is also important here, which could further increase the efficiency of the recovery of phenolic compounds. In any case, for the extraction and potential isolation of an individual compound, it would be necessary to carefully study the influence of the operating conditions since the solubility trend changes for certain compounds [23].
## 2.5. Enzyme Activities of AS Samples
Since plants are a valuable source of enzymes [52], a comparative study of important enzyme activities in AS extracts was carried out. The obtained results of the study are delineated in Table 3.
The results of the study showed cellulase activity only in the SFE extract. Cellulases are extremely applicable enzymes in many industries and have recently shown good potential in the fight against antibiotic-resistant bacteria [53] and in the conversion of agricultural waste into bioethanol and sugar [54]. Furthermore, the highest lipase activity was demonstrated in the UE extract (56.30 U/g), followed by the activity in the SFE extract (24.54 U/g), while lipase activity was not detected in the SE AS extract. Since lipases catalyze the hydrolysis of ester-carboxylate bonds and release organic alcohols and fatty acids with high efficiency and stability [55], they are extremely appealing from a commercial point of view for quite a few industries. Many different seeds have already been demonstrated as a potential source for possible lipase exploitation [56], and with this study, AS have also become an attractive alternative. Thereafter, peroxidases are important antioxidant enzymes that are also applied in medicine, analytics, agriculture, and other fields [57]. Peroxidase was active in all AS extracts, but compared to other analyzed enzymes, its activity was the lowest. On the contrary, the polyphenol oxidase (PPO) activity was generally the highest and was expressed in all extracts (SE > UE > SFE, up to 4250.00 U/g). The results are not surprising since PPO is the enzyme responsible for the browning. In the presence of oxygen, PPO changes phenolic compounds into various quinones through the oxidation process, which further react and form melanin. Melanin is dark pigment that colors the fruits/seeds brown. When AS are crushed in the presence of air, they soon develop a red-orange color [58]. Hatzakis et al. [ 59] discovered that AS extract contains a pigment called perseorangin, which is a yellow-orange solid and is the result of a PPO-dependent reaction. All AS extracts also resulted in protease (SFE > UE > SE) and transglutaminase activity (UE > SE > SFE). Plant proteases are also actively used in medicine, as they exhibit a wide spectrum of therapeutic actions. Moreover, antimicrobial, antioxidant, anti-inflammatory, and antidiabetic properties of bioactive peptides obtained from plant proteases have also been proven [60]. Transglutaminase, in addition to other applications (e.g., food additives), may be considered as an innovative category of wound-healing mediators [61,62]. Next, superoxide dismutase (SOD) activity has been evaluated in AS extracts. Specifically, SE and UE AS extracts demonstrated high SOD activity (up to 3123.97 U/g), while the activity in SFE extract was slightly lower. Due to their antioxidant effects, SODs have enormous potential for applications in many industries, including medicine, as an abundant number of studies have reported their physiological importance and therapeutic potential [63]. Importantly, to the best of our knowledge, only studies containing enzyme-inhibitory potential against specific enzymes [64,65] are found in the reviewed literature, while no similar comparative study covering the activity of selected enzymes in any AS extracts has been published so far. Accordingly, the presented results greatly contribute to the identification of valuable enzymes in AS, which could potentially be a source for their exploitation and further applications in divergent branches and industries. However, it is important to point out that only the SFE extract contains all the tested enzymes in their active form; therefore, the SFE extract is the most suitable source of active enzymes from AS of all tested extracts.
## 2.6. Antioxidant Activity of AS Samples
Plants contain a large number of bioactive compounds with high antioxidant activity. Studies of the antioxidant activity of various plant species contribute to revealing the value of these species as a source of new antioxidant compounds. Therefore, the antioxidant potential of AS extracts was examined. The results are depicted in Figure 3.
Using the DPPH method, the extracts showed inhibition in the range of 58.50–$67.49\%$. The highest antioxidant activity was detected in SE extract, followed by SFE and UE extract. Health-promoting biologically active compounds (e.g., phenolic compounds) definitely contribute to the antioxidant potential of the extracts. Various studies indicate that some phenolic compounds (epicatechin, quercetin, benzoic acid, caffeic acid, chlorogenic acid, ferulic acid, 4-hydrohibenzoic acid, and p-coumaric acid) also quantified in the presented study have already been proven to be associated with the antioxidant activity of AS extracts [66].
Nonetheless, the before-mentioned high enzyme activity of the SOD enzyme in AS extracts, which is well known for its antioxidative effects, should be emphasized. Kruskal–Wallis H test showed that there was a statistically significant difference in the antioxidant activity of AS extracts prepared by different extraction methods. For comparison, Weremfo et al. [ 19] detected $73.61\%$ inhibition of AS EtOH extract obtained by microwave-assisted extraction (MAE) and $60.56\%$ inhibition of extract obtained by conventional solvent extraction (CSE). In the present study, $67.49\%$ inhibition was achieved with the same EtOH solvent, but the extraction method was different (SE). In comparison, the extraction method has an effect on the antioxidant potential of the extracts, as SE in the present study resulted in a higher I than CSE, while it demonstrated lower I than MAE. Furthermore, Zaki et al. [ 46] determined up to $97.84\%$ inhibition (MeOH extracts), while Al-Juhaimi et al. [ 48] determined up to $83.70\%$ inhibition using UE (hexane, MeOH/H2O extracts). The importance of the solvent used with the same extraction method can also be detected here, as with other solvents (MeOH, hexane, etc.) AS extracts showed a higher antioxidant potential than the presented UE H2O extract ($58.50\%$). Recently, Kautsar et al. [ 67] studied the effect of AS extract storage on antioxidant activity, which decreased from 91.58 to $84.05\%$ after 4 weeks. Differences between studies also arise as a result of the influence of many factors such as the variety, origin, and maturity of the fruit, and as mentioned, additional discrepancy is caused by the use of different methods and solvents for the extraction of biologically active compounds.
## 2.7. Antimicrobial Activity of AS Samples
The increase in antimicrobial resistance, the decrease in the effectiveness of synthetic drugs, and, at the same time, their increased toxicity has led to an ever-increasing search for alternative biologically active substances. As AS are a by-product of the avocado industry, they have already been investigated as a potential source of antimicrobial compounds. Data in previously published studies indicate that diverse AS extracts inhibit the growth of Candida spp., Cryptococcus neoformans, *Malassezia pachydermatis* [4], Corynebacterium ulcerans, Escherichia coli, Staphylococcus aureus, Streptococcus pyogenes, *Salmonella typhi* [30], Entamoeba histolytica, Giardia lamblia, Trichomoniasis vaginalis, *Mycobacterium tuberculosis* [68], *Clostridium sporogenes* [69], Proteus mirabilis, Pseudomonas aeruginosa, Aspergillus niger [70], *Porphyromonas gingivalis* [71], Bacillus cereus, Listeria monocytogenes, Pseudomonas spp., *Yarrowia lipolytica* [72], *Klebsiella pneumoniae* [3], Staphylococcus epidermidis, Enterococcus faecalis, Salmonella enteritidis, Citrobacter freundii, Enterobacter aerogenes, Zygosaccharomyces bailii, Aspergillus flavus, and Penicillium spp. [ 73]. Many studies have investigated the antimicrobial effect of AS extracts obtained by well-known conventional methods on plenty of microorganisms. On the other hand, it should be point out that so far, it is possible to find more than one a recent study, which is by David et al. [ 74], that tested the antimicrobial effect of AS extracts obtained with greener SFE. The susceptibility of L. monocytogenes, S. typhimurium, and E. coli to AS extracts obtained by scCO2 at different operating temperatures and pressures was studied. Extracts obtained at 40 °C and 30 MPa and at 50 °C and 20 MPa showed only inhibition of L. monocytogenes growth. Hence, the presented study is a major contribution to this field, as it includes a comprehensive and comparative study of the qualitative and quantitative antimicrobial efficacy of UE, SE, and SFE AS extracts on 15 different microorganisms.
Initially, the antimicrobial activity was tested qualitatively using the disc diffusion method. The results are shown in Table 4.
Promisingly, all AS extracts inhibited the growth of all selected Gram-negative bacteria. The inhibition zone with the addition of AS extracts obtained with different methods on P. aeruginosa was the same, while the addition of SE extract to E. coli and P. fluorescens resulted in the largest inhibition zone. Furthermore, SE extract inhibited the growth of all Gram-positive bacteria, while B. cereus was not susceptible to the addition of UE extract and S. pyogenes to the addition of UE and SFE extracts. The sensitivity of fungi to AS extracts was also examined. The SE extract showed the lowest antifungal performance, as it only inhibited the growth of P. cyclopium. UE extract only inhibited the growth of A. fumigatus and P. cyclopium. On the contrary, the SFE AS extract was very effective as an antifungal agent, as it inhibited the growth of six out of eight selected fungi; only S. cerevisiae and T. viride were not susceptible to its addition. Overall, using the disc diffusion method, SFE AS extract proved to be the most antimicrobially effective, inhibiting the growth of 11 out of 15 microorganisms, followed by SE ($\frac{8}{15}$) and UE extract ($\frac{7}{15}$).
Given the promising results of the qualitative study, the antimicrobial effectiveness of AS extracts was quantified using broth microdilution method. The MGIRs were determined for selected Gram-negative and Gram-positive bacteria and fungi at five different concentrations of AS extracts. A comparison of MGIRs after 8 and 24 h was also carried out, which enables a more comprehensive insight of the antimicrobial activity of AS extracts for possible applications in various industries. According to all the reviewed literature, similar studies containing a certain percentage level of growth inhibition for selected microorganisms with any AS extracts have not yet been published. Therefore, Figure 4 shows the results of a quantitative study of the antimicrobial efficacy of AS extracts against selected Gram-negative bacteria, which was carried out by broth microdilution method.
After 24 h of incubation with the highest added concentration of inhibitors, all AS extracts completely inhibited growth of Gram-negative E. coli (with $100\%$ MGIR). More specifically, UE extract at the concentration of 2780 μg/mL inhibited MGIR by $82\%$ after 8 h of incubation, while E. coli was then not susceptible to lower concentrations. After 24 h, the addition of the lowest UE extract concentration (70 μg/mL) resulted in $71\%$ MGIR and addition of 210 μg/mL in as much as $98\%$ MGIR. Furthermore, the addition of SE extract at a concentration of 210 μg/mL showed $72\%$ MGIR after 8 h, and after 24 h, more than $50\%$ inhibition of E. coli growth was achieved with 140 μg/mL. At both times, almost complete inhibition (99 and $98\%$ MGIR) was achieved with the addition of 280 μg/mL SE extract as an inhibitor. The SFE extract also proved to be a good inhibitor of the growth of E. coli, which already showed $33\%$ MGIR after 8 h with the concentration of 70 μg/mL as the lowest concentration and as much as $83\%$ MGIR with 140 μg/mL. After 24 h, the concentration of 210 μg/mL SFE extract reached as much as $98\%$ MGIR.
Gram-negative P. aeruginosa was the least susceptible to the addition of UE extract, as only the highest added concentration of the extract achieved its complete growth inhibition. Lower concentrations of UE extract, however, resulted in MGIRs of up to $65\%$. On the contrary, SE and SFE extracts even with the addition of the lowest concentration, 70 μg/mL, showed MGIRs between 46–$64\%$ after 8 and 24 h. In the case of the addition of SE extract as an inhibitor, higher-than-$90\%$ MGIRs were achieved with a concentration of 280 μg/mL. However, the SFE extract proved to be exceptionally effective as a growth inhibitor of P. aeruginosa, as it reached $95\%$ MGIR after 8 h of incubation with 140 μg/mL of the added extract and $97\%$ MGIR after 24 h of incubation with 280 μg of SFE extract per mL of suspension.
Exceptional results of antibacterial efficiency were achieved with the addition of UE and SFE extracts as inhibitors on the growth of Gram-negative P. fluorescens. After 8 h of incubation, the addition of 70 μg/mL UE extract resulted in $77\%$ MGIR, while 140 μg/mL and higher concentrations showed complete inhibition of P. aeruginosa growth. After 24 h of incubation, the lowest concentration inhibited its growth with $50\%$ MGIR, and further concentrations resulted in more than $85\%$ MGIRs. After 8 h of incubation of P. aeruginosa with 210 μg/mL SE extract, the result was $50\%$ MGIR and $96\%$ with the addition of 280 μg/mL. The mentioned bacterium was less susceptible to lower concentrations of SE extract after 24 h of incubation, but the highest concentration of 2780 μg/mL still completely inhibited its growth. Importantly, even the lowest added concentrations of SFE extract significantly affected the growth of P. aeruginosa (42 and $49\%$ MGIR after 8 and 24 h), while the addition of 140 μg/mL SFE extract completely inhibited its growth in both time periods.
Figure 5 shows the results of a quantitative study of the antimicrobial efficacy of AS extracts against selected Gram-positive bacteria, which was carried out by BMM.
Regarding Gram-positive B. cereus, generally higher antibacterial efficiency of AS extracts was observed after 8 h of incubation, while after 24 h, the efficiency decreased except at the highest added concentration. The UE extract has a remarkable impact on the B. cereus, as it completely inhibited ($100\%$ MGIR) its growth even at the lowest concentration after 8 h of incubation. The results were similar regarding SFE extract, starting with $91\%$ MGIR at 70 μg/mL. B. cereus was least susceptible to the addition of SE extract. After 8 h of incubation, concentrations of SE extract in the range of 70–280 μg/mL resulted in 37–$41\%$ MGIRs, while 2780 μg/mL completely inhibited the growth of B. cereus.
AS extracts were shown to be good growth inhibitors of Gram-positive S. aureus. Similar to B. cereus, generally, after 8 h of incubation even the lowest added concentrations, AS extracts greatly inhibited the growth of S. aureus (79–$100\%$ MGIRs). Again, all tested concentrations of UE extract completely inhibited the growth of the mentioned Gram-positive bacteria after 8 h, and after 24 h, the MGIRs increased from 64 to $91\%$. Moreover, 70 μg/mL of SE extract resulted in $86\%$ MGIR after 8 h, and further concentrations inhibited the growth of S. aureus with 90 and $99\%$ MGIRs. After 24 h, 210 μg/mL of SE extract completely inhibited its growth. Finally, SFE extract inhibited S. aureus growth with $79\%$ MGIR (8 h) when added at 70 μg/mL, and MGIRs increased with a concentration up to $100\%$. S. aureus was not susceptible to the three lowest concentrations of SFE extract after 24 h of incubation, although 280 and 2780 μg/mL completely inhibited its growth.
The effect of the addition of AS extracts as inhibitors on Gram-positive S. pyogenes was also investigated. In contrast to B. cereus and S. aureus, the greatest antibacterial potential was shown by SE extract, where even lower concentrations resulted in higher MGIRs compared to UE and SFE extracts. After 8 h, 140 μg/mL of SE extract inhibited the growth of S. pyogenes by $90\%$ and after 24 h by $73\%$, while higher concentrations resulted in MGIRs ranging between 83 and $99\%$. UE extract showed a higher level of inhibition after 24 h (47–$95\%$ MGIRs). In contrast, SFE extract showed a certain level of inhibition (39–$72\%$ MGIRs) after 8 h of incubation, while S. pyogenes was not susceptible to the addition of SFE extract after 24 h of incubation, which is in agreement with the findings of the disc diffusion method.
Using the broth microdilution method, it could be estimated that, in general, when lower concentrations of AS extracts were added, Gram-positive bacteria were more sensitive and susceptible to the addition of AS extracts, which is in line with the claims of other authors [72]. This can be explained by the fact that Gram-negative bacteria are more resistant to the addition of inhibitors due to the presence of an additional protective outer membrane, which Gram-positive bacteria lack [75].
Furthermore, the antifungal activity of AS extracts was also investigated. Since most fungi form spores, which makes them incompatible with the broth microdilution method, the quantitative antifungal efficacy of UE, SE, and SFE extracts was tested against C. albicans. The results are shown in Figure 6.
The results are in accordance with the disc diffusion method because even when using BMM, only the SFE extract showed good antifungal efficiency. UE extract resulted in 12–$43\%$ MGIRs after 8 h of incubation, but after 24 h, the fungus was not sensitive to the addition of the extract at all. In the case of the addition of SE extract as an inhibitor, lower concentrations did not affect the growth of C. albicans, and the two highest concentrations (2780 and 280 μg/mL) reached 32–$65\%$ MGIRs. On the other hand, the SFE extract was more effective against C. albicans. A higher degree of growth inhibition was achieved after 24 h of incubation, when even 70 μg/mL of SFE extract showed $65\%$ MGIR, and complete inhibition was achieved with the addition of 210 μg/mL.
Antimicrobial activity is attributed to many phytochemicals or biologically active compounds. Other authors [76,77,78] mainly cite the antimicrobial effect of phenolic compounds, which change the function of bacterial cell membranes and thereby slow down growth and inhibit bacterial reproduction. Furthermore, the antimicrobial effect of fatty acids (e.g., palmitic acid) and their derivatives acetogenins (e.g., avocadene, persin, persediene, and persenone A, B, and C) obtained from AS is also reported [79,80]. The fluidity, disorganization, and also the disintegration of the cell membranes occur due to disordering of the phospholipid chain, which is the cause of the leakage of intracellular content and, consequently, cell death [81]. However, the correlation between the identified phenolic compounds in the obtained AS extracts and their antimicrobial effectiveness is important. The antibacterial/antifungal activity of hesperidin [82], quercetin [83], benzoic acid [84], 2,3-dihydroxybenzoic acid [85], 4-hydroxybenzoic acid [86], caffeic acid [87], chlorogenic acid [88], cinnamic acid [89], p-coumaric acid [90], ferulic and gallic acid [91], salicylic acid [92], and o-vanillin [50] has already been demonstrated. It is possible to conclude that the high content of the mentioned phenolic compounds synergistically affects the antimicrobial efficiency of the obtained AS extracts.
For ease of review, MIC90 values were also determined from the BMM results as the concentrations at which AS extracts inhibited the growth of a particular bacterium/fungus by at least $90\%$ of the MGIR. The results are shown in Table 5.
Only a small number of studies covering MIC values for AS extracts can be detected in the literature. Nwaoguikpe and colleagues [3] determined MIC values for E. coli, P. aeruginosa, and S. aureus in the range of 40,000–50,000 μg/mL for aqueous, methanolic, and ethanolic AS extracts. Furthermore, a study by Idris et al. [ 30] resulted in MIC values for petroleum ether, chloroform, ethyl acetate, and methanol SE AS extracts in the range of 10,000–50,000 μg/mL for E. coli, P. aeruginosa, S. aureus, S. pyogenes, and C. albicans. Raymond et al. [ 73] determined MIC values for P. aeruginosa of 250.0 ± 216.5 μg/mL and for E. coli and S. aureus of greater than 500 μg/mL with ethanolic AS extract (Hass variety). MIC values for Gram-negative P. fluorescens and Gram-positive B. cereus have not been studied in the literature so far. In the presented research, the lowest MIC value was determined for B. cereus after 8 h of incubation in the case of UE and SFE extracts (70 μg/mL) compared to all tested microorganisms. MIC values were also determined for P. fluorescens and were especially promising for SFE extract (140 μg/mL) after 8 and 24 h of incubation. Compared to the previously listed research, our extracts showed incomparably lower MIC values for the remaining microorganisms, which greatly contributes to the current information on the antimicrobial activity of AS extracts.
For further applications in which it is necessary to consider, for example, the release of AS extract as a potential antimicrobial agent and the MIC for microorganisms after a certain incubation, contact time is extremely important. In the presented study, it was demonstrated that microorganisms are differently susceptible to the addition of diverse AS extracts and are variously sensitive to the addition of inhibitors after certain periods of time. For example, S. aureus is more susceptible to the addition of all three studied AS extracts after 8 h (lower MIC90 values) than after 24 h incubation time. *In* general, SFE extract compared to UE and SE extract resulted in lower MIC90 values, which is a great contribution to research in the field of the antimicrobial action of SFE AS extracts.
## 3.1. Chemicals, Reagents, and Microorganisms
Chemicals including malt extract, potato dextrose broth, Triton X-100, tryptic soy broth, and tryptone were obtained from Fluka, Buchs, Switzerland. Mueller–Hinton broth (MHB) and potato dextrose agar (PDA) were purchased from Biolife, Milano, Italy. 4-Aminoantipyrine (4-APP), Coomassie Blue G-250, ethanol (EtOH, ≥$99.5\%$), ferric chloride (FeCl3), hydrochloric acid (HCl, $37.0\%$), iodine (I2), meat extract, meat peptone, n-butanol (≥$99.5\%$), 1-napthol (≥$99.0\%$), phosphoric acid ($85.0\%$), potassium dihydrogen phosphate (KH2PO4), potassium iodide (KI), sodium chloride (NaCl), sodium dihydrogen phosphate monohydrate (NaH2PO4·H2O), and sodium hydrogen phosphate (Na2HPO4) were from Merck, Darmstadt, Germany. Calcium chloride (CaCl2), D-(+)-glucose anhydrous, and ferrous sulfate heptahydrate (Fe(SO4)·7H2O) were purchased from Kemika, Zagreb, Croatia. Carbobenzoxy-L-Glutaminylglycine (CBZ-Gln-Gly) was obtained from Zedira GmbH, Darmstadt, Germany. Acetic acid (glacial, ≥$99.7\%$), acetonitrile (≥$99.9\%$), agar, ammonium hydroxide solution (NH4OH), ascorbic acid, benzoic acid (≥$99.5\%$), bovine serum albumin (BSA), caffeic acid ($98.0\%$), casein, chloroform (CHCl3, ≥$99.0\%$), chlorogenic acid (≥$95.0\%$), cinnamic acid (≥$99.0\%$), 2,3-dihydroxybenzoic acid ($99.0\%$), L-3,4-dihydroxyphenylalanine (L-DOPA, ≥$98.0\%$), 3,5-dinitrosalicylic acid (DNS), 2,2-diphenyl-1-picrylhydrazyl (DPPH, ≥$97.0\%$), ellagic acid (≥$95.0\%$), [-]-epicatechin (≥$90.0\%$), ethylenediaminetetraacetic acid (EDTA, 98.5–$101.5\%$) ferulic acid ($99.0\%$), Folin–Ciocalteu’s phenol reagent (FC), gallic acid (GA, ≥$97.5\%$), glucose assay, hesperidin (≥$97.0\%$), hydrogen peroxide (H2O2), hydroxylamine hydrochloride (HONH2·HCl), L-glutamic acid ($99.0\%$), L-glutathione reduced (≥$98.0\%$), maltose, methanol (MeOH, ≥$99.9\%$), o-vanillin ($99.0\%$), yeast extract, peptone from soybean, phenol (C6H5OH), p-coumaric acid ($98.0\%$), p-hydroxy benzoic acid ($99.0\%$), p-nitrophenyl butyrate (p-NPB, ≥$98.0\%$), potassium sodium tartrate tetrahydrate (KNaC4H4O6·4H2O, $99.0\%$), 2-propanol ($99.9\%$), pyrogallol (≥$99.0\%$), quercetin (≥$95.0\%$), salicylic acid (≥$99.0\%$), Sigmacell cellulose, sodium acetate (CH3COONa, ≥$99.0\%$), sodium carbonate (Na₂CO₃, ≥$99.5\%$), sodium hydroxide (NaOH, ≥$95.0\%$), starch, sulfuric acid (concentrated, H2SO4) trichloroacetic acid (TCA, ≥$99.0\%$), and Trizma Base (NH2C(CH2OH)3, ≥$99.7\%$) were purchased from Sigma-Aldrich, St. Louis, USA. Carbon dioxide (CO2, purity 2.5) was obtained from Messer, Ruše, Slovenia.
Selected microorganisms including bacteria (B. cereus (DSM 345), E. coli (DSM 498), P. aeruginosa (DSM 1128), P. fluorescens (DSM 289), S. aureus (DSM 346), S. platensis (DSM 40041), and S. pyogenes (DSM 11728)) and fungi (A. brasiliensis (DSM 1988), A. flavus (DSM 818), A. fumigatus (DSM 819), A. niger (DSM 821), C. albicans (DSM 1386), and S. cerevisiae (DSM 1848)) were purchased from DSMZ-German Collection of Microorganisms and Cell Cultures GmbH from Berlin, Germany. The fungi P. cyclopium and T. viride were gifted from the Department of Agricultural Chemical Technology, Budapest University of Technology and Economics, Hungary.
## 3.2. Plant Material and Preparation of Samples
Avocado fruits (Hass variety) were stored at room temperature until full ripeness. Complete avocado seeds were then manually separated from the ripe avocado fruits and washed under the continuous flow of tap water. Obtained avocado seeds (AS) were then chopped into small pieces in order to accelerate the drying process. The final drying process took place at room temperature to avoid any effect of higher temperature on the content of bioactive compounds. Sliced, dried seeds (including seed coats) were then evenly ground and stored at room temperature and protected from light until their extraction and further analysis.
Extractions of dried AS were performed using different extraction methods in order to compare the content of various phytochemicals and bioactive compounds and to evaluate their bioactivity. First, under UE, the mixture of 20 g of dried AS and 150 mL of water (H2O) as solvent was exposed to ultrasonic irradiation at 40 kHz for 3 h at 20 °C using an ultrasonic bath (Iskra PIO-Sonis 4, Iskra, Šentjernej, Slovenia). After extraction, the mixture was filtered using Büchner funnel and flask to remove solid particles. Furthermore, for SE, about 25 g of dried AS was added into porous cellulose thimble and placed in Soxhlet extractor. A volume of 150 mL EtOH as a solvent was added into flask, attached to extractor and condenser, and heated to reflux. Extractions were conducted for 6 h. SFE of dried AS was performed using a semi-continuous apparatus [93]. To achieve better extraction of phenolic compound, EtOH was added as a co-solvent. Approximately 20 g of dried AS was placed into the extractor (60 mL), which was placed into a water bath preheated to operating temperature (40 °C). SFEs were performed at an operating pressure of 200 bar. The flow rate of CO2 was maintained at a constant of 2 mL/min while EtOH was pumped continuously using a high-pressure pump with a flow rate of 0.5–0.8 mL/min. The extractions took place for 2 h. Samples were collected in the previously weighted glass tubes at ambient conditions. After the conducted extractions, the solvents (H2O and EtOH) and co-solvents (EtOH) were evaporated under reduced pressure at 40 °C using the rotary evaporator (Büchi Rotavapor R-114, Flawil, Switzerland). The final extracts were stored and kept in a freezer at −20 °C until further use.
## 3.3. Process Efficiency Evaluation
The extraction yields (η) are presented as mean values of two conducted extractions and were calculated as recently described by Kupnik et al. [ 23] using the equation below. η [%]=(mextractmdry material)×100 where η is extraction yield (%), mextract is mass of extract (g), and mdry material is mass of dry AS (g).
## 3.4. Qualitative Determination of Phytochemicals
Phytochemical screening of extracts was conducted in order to qualitatively determine the presence of various phytochemicals and bioactive compounds. Prior to analysis, the extracts were prepared at a concentration of 1 mg/mL. Phytochemicals were determined using standard qualitative tests previously described in the literature [94,95,96], with slight modifications. The procedures and observations indicating a positive test are presented in Table 6. All experiments were carried out in triplicates, and results are presented based on visual observations.
## 3.5. Total Phenolic Content (TPC) Determination
The colorimetric method with Folin–Ciocalteu’s phenol reagent, accurately described by Leitgeb et al. [ 97], was used for determination of TPC. The results are reported as mg of gallic acid equivalents (GAE) per g of extract. Experiments were carried out in triplicates, and results are presented as mean value ± standard deviation (SD).
## 3.6. Total Proanthocyanidins Content (PAC) Determination
The calorimetric method, where acid hydrolysis occurs using Fe(SO4)·7H2O in a mixture of HCl and n-butanol, accurately described by Leitgeb et al. [ 97], was availed for determination of PAC. The results are reported as mg of PAC per g of extract. Experiments were carried out in triplicates, and results are presented as mean value ± SD.
## 3.7. Total Protein Content (PC) Determination
PC was determined by Bradford method [98] using BSA as a standard. The results are reported as mg of total proteins per g of extract. Experiments were carried out in triplicates, and results are presented as mean value ± SD.
## 3.8. Identification and Quantification of the Phenolic Compounds
Prior to analysis, the extracts were prepared at a concentration of 10 mg/mL and filtered using 0.2 μm pore-size cellulose syringe filters. Extracts of dried AS were analyzed using an Agilent 1200 Series HPLC System equipped with a quaternary HPLC high-pressure pump, automatic sampler, column compartment, and variable wavelength detector (VWD). The chromatographic separation was performed with a Zorbax SB-C18 column (4.6 × 150 mm i.d., 5.0 μm particle size) at a flow rate of 2.0 mL/min using an injection volume of 10 μL. The column temperature was maintained at 25 ± 1 °C. The elution gradient consisted of acidified water ($0.1\%$ acetic acid, v/v) and acidified acetonitrile ($0.1\%$ acetic acid, v/v) as mobile phases A and B, respectively. To achieve efficient separation, the following multistep gradient program was used: $5\%$ B (5 min), $10\%$ B (10 min), $11\%$ B (12 min), $18\%$ B (18 min), $42\%$ B (19 min), $50\%$ B (24 min), $60\%$ B (25 min), $70\%$ B (28 min), and $5\%$ B (30 min). Each standard and sample were analyzed in triplicate. The peaks were detected at 280 nm. In order to identify phenolic compounds in the extracts, their retention times were compared with corresponding standards. Additionally, all identified phenolic compounds were quantified using calibration curves. The results are reported as mg of certain phenolic compound per 100 g of DW. All experiments were carried out in triplicates, and results are presented as mean value ± SD.
## 3.9. Determination of Enzymatic Activity
Specific spectrometric assays (Varian—CARY® 50 UV–VIS Spectrophotometer, Varian Inc., The Netherlands) were availed for determination of activities of selected enzymes. Exact procedures for the determination of α-amylase, cellulase, lipase, peroxidase, protease, and transglutaminase activities are precisely described by Leitgeb et al. [ 97]. Polyphenol oxidase activity was determined using Creative Enzymes® protocol [99], where the concentration of o-benzoquinone formed from L-DOPA by polyphenol oxidase was measured at 265 nm. Superoxide dismutase activity was determined using a reaction of pyrogallol autoxidation at 325 nm, described by the Creative Enzymes® protocol [100]. The results are reported as units (U) per g of extract. All experiments were carried out in triplicates, and results are presented as mean value ± SD.
## 3.10. Determination of Antioxidant Activity
DPPH free radical method, based on electronic transfer that produces a purple-colored solution in EtOH and accurately described by Leitgeb et al. [ 97], was used for determination of antioxidant activity. The results are reported as a percentage of inhibition (I) relative to reference solution. Experiments were carried out in triplicates, and results are presented as mean value ± SD.
## 3.11. Determination of Antimicrobial Activity
First, the disc diffusion method, previously described by Kupnik et al. [ 101,102], was used to qualitatively assess the antimicrobial activity of the analyzed extracts. Tests were carried out at selected concentrations of microorganisms (1–5 × 106 CFU/mL). Deionized H2O and $5\%$ DMSO were used as negative controls, while 30 μg of amoxicillin, nystatin, or vancomycin per disc was used as positive control. The results are reported as mm of inhibition zone. All experiments were carried out in triplicates, and results are presented as mean value ± SD.
Furthermore, the broth microdilution method [101,102] was used to quantitatively assess the antimicrobial activity of the analyzed extracts. Tests were carried out at selected concentrations of microorganisms (1–5 × 106 CFU/mL) using MHB as a universal nutrient medium for all microbial strains. As a negative control, $5\%$ DMSO was used. MGIRs were determined after 8 and 24 h of incubation under optimal conditions for the growth of each microbial strain for five concentrations of added extract (2780, 280, 210, 140, and 70 μg extract/mL of suspension). Additionally, the concentration at which the extract inhibited the growth of the microorganism with at least $90\%$ MGIR was defined as MIC90. All experiments were carried out in triplicates, and results are presented as mean value ± SD.
## 3.12. Statistical Analysis
All statistical data analyses were performed using IBM® SPSS® Statistics. Statistical data analysis was performed to study differences between extraction methods. The normality of the distribution of data was tested using Shapiro–Wilk’s test. The homogeneity of variances was determine using Levene’s test. Differences between extractions were determined with one-way analysis of variances (ANOVA) followed by Tukey’s post hoc test (for normally distributed data) and with nonparametric Kruskal–Wallis H test, followed by pairwise comparison using the Dunn–Bonferroni post hoc method (for abnormally distributed data).
## 4. Conclusions
Humanity is increasingly striving for a broader strategy for the shift and transition from a linear to a circular economy, including through a number of initiatives to reduce the inexhaustible waste generated annually. Food wastes present a renewable resource that can be collected and subsequently converted into value-added products, while reducing the volume of waste gathered in landfills and at the same time expanding the economic market share of new sustainable products. With this aim, avocado seeds were investigated as a potential source of biologically active compounds in extracts obtained by different methods. It was found that different solvents and extraction techniques affect both the extraction yield itself and the content of the selected biologically active compounds. Due to the different content of biologically active compounds, the enzymatic, antioxidant, and antimicrobial activities of AS extracts also differ. Importantly, compared to already known data, an extremely high content of hesperidin was found in SE (226.78 mg/100 g DW) and UE (118.10 mg/100 g DW) extracts. For the first time, the high content of 2,3-dihydroxybenzoic acid in the UE extract (106.48 mg/100 g DW) and vanillin (up to 55.02 mg/100 g DW) in all AS extracts was quantified. All three phenolic compounds are well known for their antimicrobial effects, so they most likely contributed to the good antimicrobial potential of the tested AS extracts. Furthermore, the high activity of the well-known antioxidant enzyme superoxide dismutase (up to 3123.97 U/g extract) most likely contributed to the antioxidant potential of AS extracts. However, AS extracts had a significant effect on various microorganisms, as they inhibited as many as 13 out of 15 tested fungi and bacteria. Only the fungi S. cerevisiae and T. viride were not susceptible to the addition of any of the AS extracts. Additionally, compared to the literature, extremely low MIC90 values were determined, starting with the lowest of 70 μg/mL for UE and SFE extract against the Gram-positive bacterium B. cereus. For this reason, the obtained AS extracts could be good alternative to synthetic antimicrobial agents. Overall, it is necessary to accentuate the AS extract obtained with a sustainable, greener, and modern SFE. The comprehensive study of the SFE extract in the presented research, which offers a comparison with the more well-known and conventional UE and SE, provides a great deal of new information. The SFE extract resulted in the highest content of total phenols and total proteins. It contained 12 of the 14 selected phenolic compounds and was the only one to show the activity of all the studied enzymes. It also emerged as the most promising antimicrobial agent, as it preliminarily inhibited as many as 11 out of 15 selected microorganisms and generally showed the lowest MIC90 values for 6 out of 7 investigated microorganisms.
Despite many published studies involving the phytochemistry and bioactivity of avocado seed extracts, the presented study provides additional knowledge and the contribution of important new information. The significant results of SFE provide additional insight into the extraction of bioactive ingredients from AS, while the results in the field of enzymatic activity and antimicrobial efficiency of AS extracts prove their extraordinary potential for further applications. Therefore, it would definitely make sense to exploit AS as a food waste for the recovery and production of value-added biologically active compounds, which could be further utilized in biomedicine, pharmacy, cosmetics, or other industries.
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|
---
title: Measuring the Effectiveness of a Multicomponent Program to Manage Academic
Stress through a Resilience to Stress Index
authors:
- Carlos Figueroa
- Andrés Ayala
- Luis A. Trejo
- Bertha Ramos
- Clara L. Briz
- Isabella Noriega
- Alejandro Chávez
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10007324
doi: 10.3390/s23052650
license: CC BY 4.0
---
# Measuring the Effectiveness of a Multicomponent Program to Manage Academic Stress through a Resilience to Stress Index
## Abstract
In this work, we evaluate the effectiveness of a multicomponent program that includes psychoeducation in academic stress, mindfulness training, and biofeedback-assisted mindfulness, while enhancing the Resilience to Stress Index (RSI) of students through the control of autonomic recovery from psychological stress. Participants are university students enrolled in a program of excellence and are granted an academic scholarship. The dataset consists of an intentional sample of 38 undergraduate students with high academic performance, $71\%$ [27] women, $29\%$ [11] men, and $0\%$ [0] non-binary, with an average age of 20 years. The group belongs to the “Leaders of Tomorrow” scholarship program from Tecnológico de Monterrey University, in Mexico. The program is structured in 16 individual sessions during an eight-week period, divided into three phases: pre-test evaluation, training program, and post-test evaluation. During the evaluation test, an assessment of the psychophysiological stress profile is performed while the participants undergo a stress test; it includes simultaneous recording of skin conductance, breathing rate, blood volume pulse, heart rate, and heart rate variability. Based on the pre-test and post-test psychophysiological variables, an RSI is computed under the assumption that changes in physiological signals due to stress can be compared against a calibration stage. The results show that approximately $66\%$ of the participants improved their academic stress management after the multicomponent intervention program. A Welch’s t-test showed a difference in mean RSI scores (t = −2.30, $$p \leq 0.025$$) between the pre-test and post-test phases. Our findings show that the multicomponent program promoted positive changes in the RSI and in the management of the psychophysiological responses to academic stress.
## 1. Introduction
The concept of stress encompasses both psychological and physiological aspects, since it is integrated by the abstract perception of a demand from a real environment and the biological repercussions of subsequent adaptation [1,2]. Biologically, stress can be analyzed based on the fact that, from one individual to another, there will be variations in the physiological markers that are altered when experiencing a stressful situation. Among these markers, elements directly related to the activation of the Autonomic Nervous System, and more specifically its sympathetic component, stand out, such as the galvanic response of the skin, the blood volume pulse, and the frequency of breathing, among others [3]. For example, research conducted by Lizheng et al. [ 4], which aimed to understand which physiological markers can determine specific emotions, suggested that the identification of emotions is not obtained from a singular psychophysiological signal, but from the interaction of the observed behavior of several of them. Several studies have been conducted to observe the psychophysiological impact of anxiety or psychological stress in different scenarios. In these studies, stress has been induced using different psychological stressors either in the laboratory or in real scenarios, using machine learning models for the analysis of physiological responses such as peripheral temperature, electrodermal activity, and heart rate variability, among others. On the same line, Šalkevicius et al. in Ref. [ 5] recorded blood pressure, galvanic skin response, and distal peripheral temperature to determine their subjects’ anxiety levels during a training program for managing social anxiety disorder (fear of public speaking) using virtual reality applications.
Due to the psychophysiological characteristic of stress, the ability to recover from alterations generated by the adaptation to perceived demands has both mental and biological implications [6]. In this study, the definition of resilience to stress is considered as the “properties contributing to the speed and amount of possible recovery of physiological variables after exposure to a stressful event” [7].
In this work, we evaluate the effectiveness of a multicomponent program in university students with academic excellence, aiming at improving academic stress management. The multicomponent program includes psychoeducation in academic stress, mindfulness training, and biofeedback-assisted mindfulness; the invited students participate in the “Leaders of Tomorrow” scholarship program of Tecnológico de Monterrey University.
To evaluate the effectiveness of the program, we used a recently proposed index to measure the resilience to stress of an individual, based on his/her physiological response to stressful situations. The index was proposed by Díaz et al. [ 7] and is referred to as Resilience to Stress Index (RSI). Our results indicate that the program enhanced the RSI of participants through the control of autonomic recovery from psychological stress.
## 1.1. Elements of the Multicomponent Program Proposed in this Work
Next, we briefly describe the intervention program techniques applied in this research.
## 1.1.1. Mindfulness
There is a diverse range of techniques and interventions that have been used for the development of coping strategies for stress. Psychoeducation about stress, understood as a therapeutic approach that enhances therapeutic compliance through informed decision-making about stress management, such as its symptoms or risk and protective factors, is a valuable component in programs targeting stress, and it is recommended as part of multimodal interventions [8,9]. Physical exercise, relaxation techniques, biofeedback, and mindfulness training, are other approaches that have proven efficacy in the management of stress [8,10]. Mindfulness is to intentionally pay attention or become aware of what is happening in the present moment, with acceptance and in a non-judgmental way (non-judgmental awareness) [11]. Mindfulness-based programs train awareness through formal meditation practices that include different types of meditation, such as breathing meditation and body scan meditation, among others, as well as informal meditation practices that are done during activities of everyday life [12].
## 1.1.2. Biofeedback
Biofeedback is a technique for developing self-regulation strategies that increase the voluntary control of physiological (related to the autonomic nervous system) and cognitive processes. Through this technique, the interaction between the sympathetic and parasympathetic systems, as well as the autonomic reactivity and recovery from stress are recorded. For example, maintaining controlled diaphragmatic breathing with biofeedback results in an increased heart rate variability (HRV), decreased blood glucose, and baroreflex activity with sympathetic-vagal balance, respiratory sinus arrhythmia, promoted resonance frequency, and improved cardiac autonomic function [13,14,15].
## 1.1.3. Biofeedback-Assisted Mindfulness
Combining mindfulness and biofeedback in the same intervention enhances the ability to pay attention to the present moment and physical sensations, which is achieved typically with mindfulness training. Mindfulness strengthens the skill of non-judgmental awareness that favors biofeedback training by avoiding thoughts of rejection or control that activate the sympathetic system [16]. On the other hand, biofeedback makes it possible for the subject to know their physiology through instrumental measurements that provide information during skill training for subjects to observe their “inner world”, the physiological changes that happen and how their thoughts, movements, emotions, and actions affect it, making it possible to self-regulate the arousal of the autonomic nervous system during non-judgment skill training [16,17,18,19,20,21,22].
The primary physiological response used in biofeedback as an addition to mindfulness is HRV because of its effectiveness in self-regulation training [23]. With HRV biofeedback, it is possible to learn self-regulation skills that mindfulness training alone does not offer. For example, the benefit of mindfulness breathing with biofeedback is enhanced by accompanying it with a balance in autonomic nervous system functioning. In short, mindfulness enhances the ability to be aware of physical sensations, while biofeedback helps regulate autonomic nervous system arousal [20].
Some of the benefits of the combination of mindfulness and biofeedback are the improvement of emotional self-regulation, empathy, compassion, a decrease in distress and anxiety, as well as positive changes in physiological indicators (increased HRV, changes in blood pressure and respiratory rate) and biochemical markers (HbA1c, cortisol, and triglycerides) [24]. Furthermore, biofeedback training and practice allow participants to have control over their autonomic responses [20,25].
Despite the existing evidence about the effectiveness of both techniques for stress management, to the best of our knowledge, in Mexico there are no protocols that apply mindfulness and/or biofeedback-assisted mindfulness techniques to enhance academic stress management. Hence, it is essential to design and implement intervention strategies that can prevent and address the psychophysiological outcomes of academic stress by teaching the students about stress, its symptoms, and the psychophysiological implications that could eventually trigger a physical disease (e.g., hypertension) or a psychological disorder (e.g., generalized anxiety).
As we will see later in the related work section, there is a lack of a standard way to evaluate the success of a mindfulness or equivalent program. In our study, we propose the RSI as a metric for fair comparison purposes. Therefore, the contributions of this work are the following:1.A novel multicomponent program specially designed to improve stress management;2.The use of a new index, the RSI, to measure the effectiveness of such a multimodal program.
This paper is organized as follows: In Section 1, we presented our introduction, including the main techniques employed during the study. In Section 2, we introduce related work relevant to ours. In Section 3, our methodology is explained in detail along with Section 4, where we describe our procedure. In Section 5 we present our final results and in Section 6 we give a discussion, limitations of the study, and future lines of research.
## 2.1. Regarding Known Physiological Markers Closely Related to Stress
Concerning the galvanic response of the skin, several studies have found that there is an increase in sweating when experiencing a stressful situation [26]. The greater the sweating, the higher the skin conductance (SC), so there is a positive correlation between SC and the stress experienced by a person, and this happens especially in the limbs, armpits, and face [27]. Generally, sweating measurements are performed based on skin conductance using micro Siemens units.
One of the most evident bodily effects of stress is the increase in heart rate. Generally, heart activity is deeply involved with the autonomic nervous system, as well as regulatory vagal and circadian rhythm modulation. The sympathetic nervous system makes it easier for the heart to beat with greater force and speed, with the main function of providing the body with oxygenated blood to meet the perceived needs of the environment [28]. Consequently, the activity of the heart related to stress is deeply involved in the breathing process. A measurement of Heart Rate Variability (HRV), found through the analysis of the variability of time intervals between heartbeats, can be used to determine the effects of stress on the body. Several studies have found that stressful experiences lead to a lower HRV [29,30]. Likewise, blood volume pulse (BVP), despite not having a standard unit, can be used to calculate the HRV, since there is a measurable signal change when sympathetic arousal is detected, shown in the variability of intensity between signal peaks.
Another reaction of our body strongly related to stress is the frequency of breathing, considering that the activation of the cardiovascular system fosters an increase in the frequency of breathing to function effectively through the pumping of oxygenated blood [31]. Thus, stress-derived body activation implies a higher breathing frequency in contrast to a relaxed body state [32].
## 2.2. Regarding Stress and Resilience to Stress
Several authors agree that the main source of stress in adolescents and young adults is academic performance [33], even reporting a prevalence of stress in $55\%$ to $87.9\%$ of students [34]. The repercussions of stress in this age group are observed in university students who frequently encounter academic challenges and responsibilities that, when not adequately addressed, may result in emotional and behavioral changes, health disorders, and school difficulties [35]. Studies in the school environment have suggested that academic stress occurs at all school levels, but that upon reaching university, it is at its highest peak due to both heavy workloads and their complexity, as well as the fact that they coincide with a stage in life where the student faces decisions that will define the following years of his or her life [36,37]. Moreover, entering and staying in college potentially coincides with the process of separation from the family into independence, incorporation into the labor market, romantic relationships, and other highly stressful decisions [38].
In recent years, several studies have focused on the feasibility of enhancing stress resilience in different contexts. In 2019, in a study by Kloudova et al. [ 39], aviation pilots underwent mental training biofeedback therapy to reduce physiological stress symptoms, which are indicators of anxiety that could impede flying performance. The measurements included physiological stress-related variables such as BVP, heart rate, HRV, SC, temperature, and breathing rate. After six biofeedback sessions, a t-test evidenced an important contrast of $p \leq 0.01$ between pre-test and post-test measurements. Vitasari et al. in 2011 worked with young adult university students with anxiety-related symptoms, which are importantly similar to stress response due to the activation of the sympathetic system. They conducted a biofeedback training program that used heartbeats per minute, as well as breaths per minute as measurements [40]. They concluded that, after 10 training sessions, psychophysiological management of bodily responses was successful in gaining control of symptoms of anxiety.
## 2.3. Regarding the Multicomponent Program Elements
Mindfulness training has been successfully implemented in a variety of populations and for diverse objectives. A recent systematic review of 44 meta-analyses evaluated mindfulness-based interventions (MBIs) in populations that included adults, students, medical professionals, and children for problems such as anxiety, eating disorders, depression, pain, stress, and different physical and health issues. They concluded that MBIs have significant effects with transdiagnostic relevance except for substance use and sleep disorders [41]. Many examples of successful implementation of mindfulness training can be found in scientific literature. In diabetes patients, for example, results showed better emotional regulation, acceptance and reinterpretation of thoughts, more adaptive behaviors, reduction of distress at the hypothalamic pituitary adrenal axis level, and improvement of diabetes self-efficacy, and metabolic control [42,43,44]. Relevant to stress coping, the mechanisms of mindfulness include the ability to direct mental resources to the development of behaviors that are under the control of the subject and to discern between alternative responses instead of focusing strictly on the stressful event [21].
In education, the implementation of mindfulness in college students resulted in the improvement of working memory, attention, academic performance, social skills, emotional regulation, self-esteem, mood, and the reduction of anxiety, stress, and fatigue. Positive effects have also been reported in the areas of critical thinking, focus, test anxiety, test scores, study habits, organizational skills, self-control, and attention deficit hyperactivity disorder [45]. Additionally, the combination of mindfulness and biofeedback has been successful in reducing stress, improving academic performance, and training self-regulation skills in both students and teachers [17,18,19,23]. Rush et al. [ 2017] applied mindfulness and biofeedback to 14 students diagnosed as emotionally disturbed, and in comparison to a control group, achieved significant changes (t [29] = 2.730, $$p \leq 0.011$$) with a large effect size ($d = 0.985$; 95 CI = 0.236, 1.73) specifically in reducing disruptive behaviors unrelated to academic tasks, which would be expected to have a positive impact on academic performance [19]. Biofeedback was provided through an electronic game to teach and guide proper breathing rate to improve HRV.
## 3. Methodology
We employed a quasi-experimental design pre-test and post-test by following the steps below.
## 3.1. Participants
Participants were an intentional and voluntary sample of 38 undergraduate students with high academic performance, $71\%$ [27] women, $29\%$ [11] men, and $0\%$ [0] non-binary, with an average age of 20 years (18 to 25), belonging to the “Leaders of Tomorrow” scholarship program from Tecnológico de Monterrey University in Mexico. The inclusion criteria were that participants did not have any heart disease or were on anxiolytics or antidepressant medication. When any of these conditions occurred, they had to report it during the initial interview; as a result, they were removed from the study even though they could continue participating if they wished. However, their results were not taken into account for the final report.
## 3.2. Equipment and Data Acquisition
The psychophysiological measurements during the stress profile and the biofeedback-assisted mindfulness sessions were recorded using a laptop and a ProComp5 Infiniti equipment model T7525 (https://thoughttechnology.com/procomp5-infiniti-system-w-biograph-infiniti-software-t7525/, accessed on 25 January 2022), which is integrated by a 4-channel decoder that measures heart rate variability, blood volume pulse, breathing rate, and galvanic skin response. Non-invasive surface electrodes are used to record these responses. The ProComp5 device has an ADC (Analog to Digital Converter) resolution of 14 bits, and we measured using 256 samples per second (256 Hertz sample frequency). Table 1 shows the description of variables measured with the biofeedback device. It is worth noting that HRV is obtained through BVP recording, so it is measured through the same sensor.
Figure 1 illustrates the raw data of a single subject during the ProComp5 measurements performed at different phases of the study. The three variables are represented in each slot through time (256 samples per second). Table 2 presents the descriptive statistics of such data.
## 3.3. Signal Preprocessing
The next two sections describe the data preprocessing performed on the subject’s data obtained using the biofeedback device.
## 3.3.1. Median Filter
To avoid pseudo detection of peaks or noise, the acquired signals must be preprocessed before using the data. Thus, we applied a median filtering technique to remove the peaks and noise from the signal. The kernel size w of the filter was selected according to Equation [1] [46]. [ 1]$w = 14$fs∗length(n) where fs is the sampling frequency and length(n) is the total number of instances. Moreover, the raw signals from the biofeedback device have an offset at the beginning of the sensor measurements; therefore, for our analysis, we removed the first 0.5 s of data.
## 3.3.2. Standard Scaler
The unit of measure of each sensor is on a different scale; hence, a critical step is standardization. For this purpose, we applied a standard scaler after the median filter. We used Equation [2] to standardize each point of all features, where a is the mean of the feature, and s is the standard deviation of the variable. Standard scaling is a way of normalizing features by deleting their mean and scaling their variance to one. Since the normalized value is determined uniquely by the mean and variance, it has some advantages, including being linear, reversible, rapid, and highly scalable [47]. [ 2]z=x−as Figure 2 illustrates the laboratory setup. ( A) Psychophysiological stress profile measurement during the pre-test and post-test evaluation sessions (see Section 4.1 and Section 4.3). ( B) Biofeedback-assisted mindfulness training session (see Section 4.2). ( C) Sensor placements as recommended by the manufacturer.
## 4. Procedure
The program consisted of 16 individual sessions during 8 weeks, divided into 3 phases: pre-test evaluation, multicomponent intervention program, and post-test evaluation. Figure 3 shows a diagram that represents the general procedure of data collection.
## 4.1. Phase 1: Pre-Test Evaluation
This phase was carried out in the first week in a 30-min in-person session. During this phase, the psychophysiological stress profile of the participant is built. At the beginning of the session, non-invasive surface electrodes are placed on the participant to measure four physiological responses in real-time: heart rate variability, blood volume pulse, breathing rate, and galvanic skin response. After the lab setup, the psychophysiological stress profile is recorded, with a duration of 18 min, divided into 9 stages of 2 min each. The first stage consisted of a baseline measurement, in which the participant is instructed to remain silent and with her/his eyes closed. Then, the subsequent stages alternated between stressful tasks, where the participant is presented with different psychological stressors, and recovery periods. The stressful tasks are: a Stroop test, an arithmetic test (such as subtracting 7 by 7 from a random four-digit number), an auditory stressor, and an emotional stressor in which the participant evokes a stressful academic moment. Figure 4 shows the activities of the psychophysiological stress profile creation.
## 4.2. Phase 2: Multicomponent Intervention Program
The following subsections describe the elements of the multicomponent intervention program: psychoeducation on academic stress, mindfulness training, and biofeedback-assisted mindfulness.
## 4.2.1. Psychoeducation on Academic Stress
This is an online task carried out asynchronously in one session (session 2) during week two. The session included pre-recorded videos that addressed various topics, such as the conceptualization of academic stress, sources of stress, symptomatology, risk factors, protective factors, and coping techniques.
## 4.2.2. Mindfulness Training
This training spans 2 weeks (weeks 3 and 4), including 10 online sessions (sessions 3 to 12), with 1 per day. Each session included a video explaining the topic and an audio guide detailing a body scan and breathing awareness meditation.
The training begins with basic concepts and scientific foundations of mindfulness; it also explains the conceptualization and scientific grounds of mindfulness training. Likewise, it includes information and practices on awareness of language, emotions, thoughts, and physical sensations, as well as acceptance, non-judgmental awareness, and the application of mindfulness to stress management.
## 4.2.3. Biofeedback-Assisted Mindfulness
This task consists of three in-person sessions (sessions 13–15) taking place in a 2-week period, considering at least 1 session per week. The sessions begin by placing surface sensors on the participant, to record in real-time HRV from blood volume pulse and diaphragmatic breathing. The training session lasts 24 min, divided into 6 stages. The first stage lasts for two minutes, during which physiological measurements are recorded, which serve as a baseline. In stages two and four, the participant listens to audio guides of mindfulness meditations: breathing awareness (Audio 1) and body scan (Audio 2), each lasting five minutes. In stages three and five, also five minutes each, participants continue the meditation with their eyes open, and are asked to observe a computer screen that provides biofeedback of their HRV; the screen plays a video that only moves forward when the participant manages to increase their HRV. Lastly, during stage six, for two minutes, physiological measurements are recorded at rest. It is expected that participants develop control of their physiological responses as an outcome of practicing mindfulness meditation. Figure 5 shows the structure of the biofeedback-assisted mindfulness sessions.
## 4.3. Phase 3: Post-Test Evaluation
During this phase, the post-test psychophysiological stress profile of the participant is built. It takes place during 8 eight, session 16, to close the intervention program. The procedure is carried out in the same way as in Phase 1, described in Section 4.1.
It is important to mention that the protocol, considering mainly the psychophysiological stress profile performed in phase 1, and the biofeedback component of the biofeedback-assisted mindfulness sessions, performed in phase 2, was based on the Mindfulness Suite by the Biofeedback Federation of Europe (https://www.bfe.org/buy/mindfulness-suite-p-550.html, accessed on 25 January 2022), translated into Spanish and adapted for the target population.
## 5. Experimental Results
After a quick introduction to Principal Component Analysis, we briefly introduce the resilience to the stress index used in this work, and then describe how we employed the index to measure the effectiveness of the multimodal intervention program.
## 5.1. Principal Component Analysis
The Principal Component Analysis (PCA) is a well-known unsupervised learning technique for reducing the dimensionality of data. Moreover, it increases interpretability and, at the same time, minimizes information loss. Additionally, it transforms the data, making it easier for 2D and 3D visualization [48].
We present Figure 6 only for visualization purposes. The figure shows the distribution of clusters using two principal components of the PCA of a single subject. As it can be noticed, clusters are well-defined at each stage. It is worth mentioning that for the rest of the experiments, we use all principal components.
## 5.2. Resilience to Stress Index (RSI)
The Resilience to Stress Index (RSI) is a new indicator proposed by Díaz et al. [ 7] to measure a subject’s ability to recover from stress. The index is calculated starting from a “baseline” or calibration stage, obtained by measuring the vital signs of each person before starting with the application of emotional stressors. During the following stages, the evaluator intervenes with stressors and successively provides a quiet space. In this way, changes in the users’ vital signs, as well as their attempts to return to their baseline, are recorded. It is important to note that the baseline, as well as the other stages, are unique to each participant.
With the Principal Component Analysis technique, each stage is distinguished by having its own cluster corresponding to the psychophysiological measurements; in other words, it is possible to obtain the distances between clusters. PCA obtains a covariance matrix that eliminates the multicollinearity between its attributes [49], making it possible to calculate the Euclidean distance and use it as a valid metric to represent these distances. Equation [3] is used to compute the RSI for each subject. [ 3]RSI=ΔRΔS *For this* equation, ΔR represents the distance between the centroids of the last two stages, which in this case, are eight and nine. ΔS represents the largest distance found between the centroid of the baseline and the other phases given for the sample.
As a result, the RSI is able to capture the physiological responses of each individual. An RSI close to one indicates that the individual has a high resilience to stress by presenting the ability to recover from stressors and approaching his/her initial baseline. Small values, on the other hand, indicate that the individual failed to return to the baseline and showed poor resilience.
## 5.3. Use of the Resilience to Stress Index (RSI) in Our Study
To compute the RSI, we followed the methodology presented in Ref. [ 7]. For each subject in the dataset, based on the PCA components, we used the Euclidean Distance to calculate inter-stage distances, and their RSI using Equation [3].
As mentioned previously, we used the Procomp5 biofeedback device to obtain the physiological variables during the psychophysiological pre-test, and post-test sessions. For each subject, we calculated the RSI at each session. In Table 3, we present our results obtained during the pre-test and post-test sessions.
Figure 7 shows the box and whisker diagram of the data with the quartile values shown in Table 4. Furthermore, we point out a couple of interesting things, as all points in Q4 of the post-test are higher than the upper whisker of the pre-test, except for the outliers, and the median in the post-test (0.28) is higher than the median in the pre-test (0.22).
The results show that approximately $66\%$ of the participants ($80\%$ women and $20\%$ men) improved their stress management after applying the multicomponent intervention program, which we consider encouraging, due to the short intervention time and the psychological complexity of stress management in our current time and society. The means and standard deviations of the RSI values for the pre-test and post-test are X¯1=0.247,s1=0.096 and X¯2=0.321,s2=0.174, respectively, showing an improvement in the overall performance of participants.
A Welch’s t-test was performed to determine if there was a statistically significant difference in RSI scores obtained during the pre-test and post-test. Between tests, participants were exposed to an intervention program (psychoeducation in academic stress, mindfulness training, and biofeedback-assisted mindfulness). The sample size for both groups was 38 students. The Welch’s t-test rejected the null hypothesis and revealed a difference in mean RSI scores (t = −2.30, $$p \leq 0.025$$) between the two groups. Table 5 summarizes the metrics gathered to compute the statistical test assuming unequal variances.
To reinforce this conclusion, we decided to run a non-parametric hypothesis test, such as Wilcoxon signed-rank test on the data. The results confirmed the statistical significance, as shown in Table 6, where the null hypothesis, i.e., the two samples are equally distributed, is rejected.
## 6. Discussion
In addition to learning and academic challenges, the Leaders of Tomorrow academic excellence students experience stressors at school that arise from many sources, including possible disruptions to the family system, peer conflicts, maintaining a scholarship, socio-cultural components, and vulnerability to physical and mental health risk factors. Depending on individual characteristics, these stressors may represent a challenge, a learning opportunity, or negatively affect academic and personal life.
The results of this research showed a favorable RSI in students who participated in the multicomponent biofeedback-assisted mindfulness program, which meant adequate coping and resilience in the face of academic stress. The RSI is derived by comparing changes in physiological signals due to stress against a calibration stage, and was first introduced by Díaz et al. in Ref. [ 7]. The current research is innovative in Mexico since, to the best of our knowledge, there is no precedent of a multicomponent program that successfully integrates psychoeducational elements, mindfulness, and biofeedback focused on self-management of academic stress and its autonomic modulation in university students of academic excellence. The RSI obtained from an objective and precise protocol to measure physiological indicators during psychophysiological stress is an accurate measure to determine the changes in the autonomic nervous system in the face of sympathetic activity produced by cognitive or perceptual tasks compared with periods of recovery or production of parasympathetic activity in university students. Depending on the result obtained from each participant, the RSI can be considered as a protective (increased HRV) or health risk (decreased HRV) differentiating factor that can impact the student’s health-disease process [6]. The results of this study show that $66\%$ of students significantly improved their stress resilience.
Looking at possible reasons for this finding from the psychological mechanism perspective, there are some meaningful connections to make between this finding and what has been found in the literature. Chin and colleagues published in 2019 a study in which they investigated the role of acceptance training inside the Mindfulness Based Stress Reduction (MBSR) program as a mechanism of action for stress resilience [50]. Their findings suggest that the training in acceptance that takes place in MBSR through the instruction of meeting what is experienced during meditation with an attitude of acceptance, is a necessary component that leads to stress resilience, resulting in training of acceptance as a skill. The multicomponent program used in our research included acceptance training in two ways: first, by including acceptance as a topic in the mindfulness training component and second, by repeating the phrase “...with acceptance and non-judging attitude...” during the meditation guides, including the guides used for the biofeedback-assisted mindfulness sessions. Acceptance training through mindfulness has also been mentioned as an essential component of biofeedback training in stress reduction [16], as discussed earlier in the introduction of this work.
Along with mechanisms of action, Steffen and Bartlett gathered information from empirically supported interventions addressing stress resilience in both biofeedback and psychotherapy and so, pointing to three specific mechanisms or, what they call practices, to build stress resilience as being: balancing of life demands with equanimity, awareness leading to the reduction of worry, and engaging in flexible coping skills [51]. In connection with acceptance training, they include acceptance as an important component for awareness to reduce worry [16,50]. It is clear that the combination of biofeedback and mindfulness in our proposed multicomponent program contributed to the training of awareness of stressful tasks and of the physiological manifestations of stress along with acceptance training, and at the same time, provided the opportunity of experiencing the use of mindfulness meditation as a coping skill to use for stress reduction.
It has been stated in biofeedback literature that the use of HRV biofeedback for stress reduction is based on the fact that higher HRV values are associated with individuals being more physically and emotionally resilient [52], resulting in an increased ability to respond more skillfully during stressful tasks. During training sessions in phase two of this study, most of the students were able to play the video, meaning the participants raised their HRV level during the meditation practice; additionally, they were simultaneously informed about the effect of meditation on their physiology and training emotional resilience. This is different from what is usually done in mindfulness training for stress reduction, in which participants do not know about the impact of meditation at the moment, but further when they start to notice changes in their stress responses in everyday experiences [53].
Whether the addition of biofeedback can accelerate the training on stress resilience due to objectively learning about changes in stress physiology and achieving higher HRV values, is a very interesting topic that entails future research.
The results of this research allow us to observe how with sustained mindfulness practice, college students improved attentional and emotional self-regulation and its effect on mindfulness psychophysiology to manage academic stress as well as to gain potential health benefits (e.g., immune system function, cardiovascular, neuroendocrine, improved health behaviors including eating, sleeping, etc., more positive moods, improved quality of life, and increased gray matter density in the hippocampus) [20,54].
Our results contribute to knowledge and evidence-based practice that seeks to integrate clinical psychological research preventing health problems from a psychosocial perspective in academically outstanding university students [40]. Psychophysiological assessment procedures and the multicomponent program for self-management of academic stress offer a viable option to comprehensively address (psychological and psychophysiological) the eventual clinical symptomatology reported by these students [45]. A detail worth noting is that the current research was carried out throughout a regular academic semester. The timing of the post-test was effectively close to the final academic evaluations of the participants. Usually, the evaluations at the end of the school term tend to be one of the most stressful moments for a student, due to factors such as the accumulated psychological tension from the progression of the term or the perceived impact of the evaluations on the final grade [55]. Although this context may have altered the results obtained, it is suggested that, despite the fact that the students were in a naturally more stressful environment, a significant decrease in stress was achieved. Conducting this research during a regular academic period allowed a natural evolution of stress to occur in these students. However, one of the limitations of this research is the absence of a control group that reveals the differences in autonomic activity and psychological stress between students of the Leaders of Tomorrow scholarship that took part in the multicomponent program and those who did not participate. Likewise, since the participants are part of a high-performance scholarship program, the sample may not be representative of all university students. This research was designed and conducted during the COVID pandemic, which limited the inclusion of a control group or more university participants to strengthen the internal validity of the results, especially considering a more balanced gender distribution. Despite said limitations, the results reported allowed for the observation of favorable changes in the RSI of participants in the program.
Certain institutions provide technological devices for both academic activities and personal use [56], and the inclusion of biometric devices that encourage practices such as HRV enhancement training through biofeedback could have a positive impact on the development of resilience to stress in an academic context. However, based on the results shown in this research, it is recommended that schools incorporate in their educational policies the practice of mindfulness assisted with biofeedback, targeting teachers, students, administrative, and managerial staff. The goal would be to improve their capacity to face their psychosocial and academic challenges, according to the scientific evidence based on the effectiveness of this type of program [18,19,23]. In the case of students, specific interventions can be conducted for groups vulnerable to anxiety, attention deficit disorder, academic lag, among other problems.
It is also recommended to continue with the psychophysiological assessment protocol used in this research as it will allow monitoring, throughout their academic life at the university, the psychological adjustment of students of academic excellence by detecting and addressing moments vulnerable to academic stress and thus improving their psychological well-being. The current research focused on high-achieving students who must maintain high performance in order to preserve their scholarship. Likewise, the incentive received by the participants was social service hours, which are also part of their scholarship requirements. Future work regarding this line of research includes but is not limited to: (a) adding a control group to assess the effectiveness of the program within the same population, which would strengthen the internal validity of this research; (b) replicating the experiment in a representative sample of university students, considering a distribution that takes into account the variability of bodily responses to stress in regard to gender, age, and other demographic variables; (c) reducing the number of sessions in the program to make it more accessible to the academic activities of the participants, which would increase student interest in participating in the program without being distracted from their own academic or personal activities; (d) including more face-to-face activities that allow timely feedback on the skills acquired through the program as well as an immediate correction of meditative practices; (e) carrying out follow-up sessions to measure the permanence of the favorable results obtained in the post-test, strengthening the evidence on the acquisition and development of a resilience to stress skill; (f) broadening the profile of participants to consider other characteristics, such as students with low or mixed academic performance, those that are part of a representative cultural or sports team, students that also have jobs, or those that have a diagnosed mental disorder, especially one related to poor stress management; (g) considering a statistically relevant and balanced sample to address gender differences in stress resilience, which could develop into a research path that puts emphasis on the physiological stress response based on gender.
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|
---
title: Physicochemical Characterization and Antioxidant Properties of Chitosan and
Sodium Alginate Based Films Incorporated with Ficus Extract
authors:
- Saurabh Bhatia
- Ahmed Al-Harrasi
- Yasir Abbas Shah
- Muhammad Jawad
- Mohammed Said Al-Azri
- Sana Ullah
- Md Khalid Anwer
- Mohammed F. Aldawsari
- Esra Koca
- Levent Yurdaer Aydemir
journal: Polymers
year: 2023
pmcid: PMC10007391
doi: 10.3390/polym15051215
license: CC BY 4.0
---
# Physicochemical Characterization and Antioxidant Properties of Chitosan and Sodium Alginate Based Films Incorporated with Ficus Extract
## Abstract
Aqueous extract of fruit obtained from *Ficus racemosa* enriched with phenolic components was used for the first time to fabricate chitosan (CS) and sodium alginate (SA)-based edible films. The edible films supplemented with Ficus fruit aqueous extract (FFE) were characterized physiochemically (using Fourier transform infrared spectroscopy (FT-IR), Texture analyser (TA), *Thermogravimetric analysis* (TGA), scanning electron microscopy (SEM), X-ray diffraction (XRD), and colourimeter) and biologically (using antioxidant assays). CS–SA–FFA films showed high thermal stability and high antioxidant properties. The addition of FFA into CS–SA film decreased transparency, crystallinity, tensile strength (TS), and water vapour permeability (WVP) but ameliorate moisture content (MC), elongation at break (EAB) and film thickness. The overall increase in thermal stability and antioxidant property of CS–SA–FFA films demonstrated that FFA could be alternatively used as a potent natural plant-based extract for the development of food packaging material with improved physicochemical and antioxidant properties.
## 1. Introduction
Petroleum-based films are one of the factors contributing to the dramatic rise in pollution over the past few decades, and several health problems are linked to using plastics as food packaging material. To overcome these challenges, edible films prepared from natural polysaccharides, lipids, and proteins or their combinations have gained much attention during the last few years. Polysaccharides have several potential uses in edible film preparations because of their nontoxic nature, biocompatibility, biodegradability, and processibility. Chitosan is a cationic polysaccharide that has been used in film fabrication due to its excellent film-forming properties [1,2]. However, due to its innate hydrophilicity, pure chitosan films have low water resistance. Furthermore, it is challenging to form chitosan-based edible films with desirable mechanical properties, which restricts their applicability in the fabrication of films [3]. Therefore, a variety of approaches have been suggested to address these problems and enhance the qualities of chitosan-based materials, including surface coating [4], crosslinking [5], enzyme treatment [6], and combining them with other natural polymers [7].
Sodium alginate is an inexpensive hydrocolloid that is safe to use, biodegradable and biocompatible, and provides a strong film structure in food packaging applications [8]. Both chitosan and sodium alginate are macromolecules safe for human consumption and can be combined to fabricate edible films with the most desirable properties [9]. Moreover, biopolymer films are promising carriers for different bioactive components, such as plant extracts, antioxidants, and antimicrobial agents.
Extensive research has been conducted on the cluster fig tree, scientifically known as *Ficus racemosa* (Moraceae), due to its various health-promoting properties and nutritional aspects. The fruit of *Ficus racemosa* contains several pharmacologically active compounds such as glauanol, hentriacontane, -sitosterol, tiglic acid, -sitosterol, cycloartenol, cycloeuphordenol, euphol, euphorbinol, isoeuphorbol, and palmitic acid as evidenced by various studies [10]. In different studies, the fruit extract of *Ficus racemosa* has shown various biological activities such as hypoglycaemic, antioxidant, gastroprotective, and anti-filarial activity [11,12,13]. Different studies have demonstrated the safety and nontoxicity of *Ficus racemosa* fruit extract [13,14]. The incorporation of naturally occurring sources of antioxidants and antimicrobials in edible films such as plant extracts has been studied extensively. According to the findings of several studies, the incorporation of a variety of fruit extracts into edible films enhances the antioxidant activity of the films [15]. Chitosan-based edible films were prepared with the addition of *Berberis crataegina* fruit extract. Compared to other films, the film containing fruit extract showed improved thermal stability and antioxidant and antibacterial activities [16].
Despite its high food and medicinal potential, so far, aqueous fruit extract of F. racemosa has not been used in edible films to study its impact on its physiochemical characteristics. As a result, the purpose of the current study is to examine the physiochemical properties and antioxidant potential of the chitosan–sodium alginate-based edible films incorporated with *Ficus racemosa* fruit aqueous extract (FFE).
## 2.1. Chemical Procurement
Ficus racemosa aqueous fruit extract (FFE) was procured from Ajmera Pharmaceutical, Indore, India. Sodium alginate (pure) and chitosan (extra pure, $90\%$ DA) were purchased from Sisco Research Laboratories (SRL), Mumbai, India. BDH Laboratory, London, England, supplied the glycerol used as a plasticizer in films. Additionally, other required chemicals such as 2,2′-diphenyl-1-picrylhydrazyl (DPPH), butylated hydroxyl anisole, ABTS (2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid)) and Trolox (6-Hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid) were supplied by the Sigma-Aldrich (St. Louis, MO, USA).
## 2.2. Film Preparation
The casting method was employed to form the edible films based on chitosan (CS) and sodium alginate (SA). Four types of films (FC-1–FC-4) were developed, out of which three (FC-2, FC-3, and FC-4) contained different concentrations (0.5–$1.5\%$) of FFE, while the first (FC-1) was used as a control without FFE addition. Initially, $1\%$ (w/v) CS solution was prepared by dissolving the polymer in $1\%$ (v/v) acetic acid solution. Similarly, $3\%$ (w/v) SA solution was prepared by dissolving the polymer in distilled water. Then, both solutions were mixed thoroughly in a beaker with the help of a magnetic stirrer. Following the solubilization of CS and SA, the solution was divided equally and transferred to four separate beakers (50 mL) labelled FC1–FC4. $5\%$ glycerol was added to the film-forming solution as a plasticizer. The first beaker (FC-1) contained the CS–SA film-forming solution with $5\%$ glycerol (v/v) and was considered the control sample in the current study. The second, third, and fourth beakers, labelled as FC-2, FC-3, and FC-4, contained $5\%$ glycerol (v/v) film-forming solution and 0.5, 1, and $1.5\%$ FFE, respectively. For the drying purpose, the solution was poured into Petri plates at room temperature for 48 h. After drying, the films were visually observed and then peeled out from the Petri plate’s surface for further examination. The composition of the CS–SA-based films is presented in Table 1.
## 2.3. Thickness
A handheld digital micrometre was utilized to measure the thickness of CS–SA-fabricated films. For each prepared film sample, five random measurements were taken. The calculated mean thickness for each sample was determined in mm.
## 2.4. Mechanical Properties of Edible Films
A standardized method (by the American Society for Testing and Materials. ASTM D882, 2010) was followed to determine the mechanical properties of the prepared film samples. Before being evaluated, the films were first conditioned for at least 40 h in a test cabinet (Nüve TK 120, Türkiye). A Universal Tester (TA. XT plus, Stable Micro Systems, UK) coupled with a 5 kg load cell was utilized to perform the experiment. The films were cut into uniform strips and inserted into the apparatus; the assessment of the mechanical properties of the films was carried out at a speed of 30 mm per minute. The mechanical properties of the films were evaluated by measuring their tensile strength (TS), elongation at break (EAB), and young’s modulus (YM). The following equations were applied to measure the mechanical assessment parameters. [ 1]Tensile Strenght (TS)=(FA) F represents the force, and A shows the cross-sectional area of the film. [ 2]Elongation at Break (EAB) (%)=Lf−LiLi×100 Lf presents the final length at a break, and Li shows the initial length of the film.
## 2.5. Assessment of Water Solubility
The prepared film samples were examined for water solubility by following the procedure described by Kim and Song [17]. The films cut in dimensions of 3 cm by 4 cm were placed in a hot air oven at 105 °C. When films attained a constant weight, the weight at this point was noted as W1. The films were taken into 20 mL distilled water and placed in a shaking incubator (IKA KS3000 IC, IKA®-WerkeGmbH&Co. KG, Staufen, Germany) for 24 h. Then, the samples were removed from the flasks and subjected to drying in the hot air oven at 105 °C. After drying, the weight of the sample was noted as W2. The following equation was used to determine the water solubility of the CS–SA-based film samples. [ 3]Water Solubility=W1−W2W1×100
## 2.6. Moisture Content
The moisture percentage of CS–SA-based film samples was assessed by placing the film samples in an oven at 105 °C for at least three hours. The initial weight of the film samples before drying was measured as W1. After drying the final weight of the film samples was measured as W2. The moisture content (MC) was determined by the equation as follows:[4]Moisture Content=W1−W2W1×100
## 2.7. Determination of the Water Vapor Permeability
The methodology adopted by Erdem et al. [ 18] was employed to determine the water vapour permeability of the film samples. Glass cups with 5 cm diameter and 3 cm depth were used in the procedure. The relative humidity (RH) of the measuring systems was regulated by using water and silica gel having RH of $100\%$ and $0\%$, respectively. To evaluate weight gain during the day, cups containing silica gel were covered with films that were firmly sealed and periodically weighted every hour. The WVP of the films was calculated and presented in g mm/(m2) (d)(kPa) by applying the below equation. [ 5]Water Vapor Permeability=ΔmΔt×ΔP×A×d ∆m/∆t represents the weight of moisture gain per unit of time in g/d.
A represents the film area in m2.
∆P is the water vapour pressure difference between the two sides of the film in kPa.
d is the film thickness in mm.
## 2.8. Transparency
To measure the transparency of the film samples, Erdem et al. ’s [18] methodology was followed by using a spectrophotometer (ONDA-vis spectrophotometer). The film samples were placed in Spectro cuvettes and transparency was measured with a spectrophotometer adjusted at 550 nm. To calculate the transparency of the films, the following formula was used, in which the X is the film thickness. [ 6]Transparency=(A550X)
## 2.9. Colour
The colour analysis of the CS–SA-based film samples was carried out by using a colourimeter CR-400 by Minolta, Tokyo, Japan. The parameters such as lightness (L*) and yellow-blue (b*) and red-green (a*) were assessed. Different positions of the film surface were used for the colour analysis. The overall colour difference, denoted by the symbol delta E, was determined by calculating the following formula: [7]ΔE=[(ΔL*)2+(Δa*) 2+(Δb*)2]$\frac{1}{2}$
## 2.10. Thermogravimetric Analysis
Using a TG analyser (TA Instruments New Castle, DE 19720, USA) thermal gravimetric examination of films was performed. Composite films were scanned from room temperature to 600 °C at a heating rate of 10 °C/min under constant purging with nitrogen gas.
## 2.11. X-ray Diffraction
For analysing the XRD pattern of fabricated edible films, a Bruker D8 Discover instrument was used. The samples were examined at a 2θ diffraction angle and 40 kV voltage and a current in the range of 5–50° at a rate of 0.500 s/point, and the Scherrer constant (K) was 1.5418 Å.
## 2.12. FTIR Analysis
An FTIR Spectrometer (InfraRed Bruker Tensor 37, Ettlingen, Germany) was used to determine the elemental structure of the samples by setting them up with an attenuated total reflection (horizontal) device (45° ZnSe) [19]. For each spectrum, 32 scans in the range of 400–4000 cm−1 with a resolution of 4 cm−1 were performed. All the measurements were conducted at room temperature.
## 2.13. SEM Analysis
The surface and cross-sectional structural features of the EFs were assessed by using SEM (JSM6510LA, Analytical SEM, Jeol, Japan) at 10 kV [19]. The films were coated with gold prior to taking images. The analysis was carried out at an acceleration voltage of 10 kV under high vacuum mode. The samples were placed on an aluminium stub covered with adhesive tapes and gold sputter-coated.
## 2.14. Antioxidant Activity
The addition of plant extracts in edible films significantly affects the antioxidant activity. The antioxidant activity of CS–SA-based film samples was evaluated using two methods, DPPH and ABTS. The methodology described by Brand-Williams et al. [ 20] was employed to determine the DPPH radical scavenging activity of 12.5 mg of film samples (FC-1–FC-4). A spectrophotometer (Labart LFD-10N, Italy) was used to measure the absorbance value of the film samples at 517 nm. The results obtained for the DPPH radical scavenging activity were presented as % inhibition.
For the ABTS assay, the methodology of Re et al. [ 21] was used with slight modifications. In the present work, samples were measured at 734 nm after vortexing 6.25 mg of film for 30 s with 1.9 mL of 7 mmol/L ABTS radical solution produced with potassium persulfate solution (2.45 mM). The results for the ABTS radical scavenging activity were presented as % inhibition.
## 2.15. Statistical Analysis
The statistical analysis was carried out to check the significance level between various samples. The results were presented as mean value and standard deviation. A one-way analysis of variance was conducted using statistical analysis software, followed by Duncan’s test with a $5\%$ significant level.
## 3.1. Thickness
The results for the thickness showed a slight increase with the incorporation of the FFE. FC-4 and FC-3 samples with $1.5\%$ and $1\%$ extract demonstrated the maximum thickness (0.05 mm), followed by FC-2 and control (Table 2). The results obtained are in agreement with the findings of Kumar et al. [ 22], in which the chitosan-based edible films exhibited similar behaviour when incorporated with pomegranate peel extract.
## 3.2. Mechanical Properties
A composite film meant for food packaging must possess appropriate mechanical properties including resistance to common external forces during handling, shipping, and storage. The CS–SA-based edible film samples were examined for their mechanical properties and the obtained results are expressed in Table 2. The tensile strength (TS) of the control or FC-1 sample without FFE addition was relatively higher than FC-2, FC-3, and FC-4 samples. The tensile strength of the FC-4 samples with $1.5\%$ FFE was lower than other samples. The decrease in the TS of the films with increasing the extract concentration could be ascribed to the weak molecular interaction between the film-forming components with the incorporation of FFE.
However, the EAB value for different CS–SA-based film samples increased from 16.21–$21.47\%$. The control or FC-1 sample without FFE addition exhibited the lowest values ($16.21\%$) compared to other samples comprising FFE. The EAB value of the FC-4 sample ($21.47\%$) was higher than other samples; this could be due to the increase in molecular mobility by adding extract. The results of our study are corroborated with the findings of Nemazifard et al. [ 23] where a decrease in TS and an increase in EAB among cellulose films incorporated with pomegranate seed extract is reported. Zhang et al. [ 24] also reported similar behaviour of the mechanical characteristics of CS films with the addition of banana peel extract. However, the mechanical properties of the films also depend upon different factors such as film constituents, types of polymers, processing technique, drying conditions, moisture, etc.
## 3.3. Moisture Content and Water Solubility
For food applications, edible films should be preferably water-resistant. However, natural polymer-based films usually have poor water resistance (Al-Harrasi et al., 2022). In the current study, an increase in the moisture content among FC-1–FC-4 samples was observed due to the incorporation of the FFE (Table 2). The maximum ($34.82\%$) and minimum ($24.16\%$) moisture content was found in FC-4 and control, respectively. This could be due to the hydrophilic nature of the polymers as well as the extract added. Recent studies have shown that adding plant extracts in biopolymer-based films enhances their water moisture and solubility [25]. In a previous study, conducted by Augusto et al. [ 26], the researchers found that the incorporation of *Codium tomentosum* seaweed extract led to an increase in the amount of moisture present in edible films based on chitosan and alginate.
The water solubility of the CS–SA-based edible film samples showed $100\%$ values for all the film samples. This behaviour could be ascribed to the hydrophilic nature of the film-forming components, including chitosan and sodium alginate, as well as FFE.
## 3.4. Water Vapor Permeability
The composition of the films generally determines the water vapour permeability because the hydrophobic compounds present in the film prevent the transfer of moisture from the surroundings to the packaged food. Ideally, food packaging materials should have lower WVP values, thereby reducing food spoilage [22]. The results for the WVP of tested film samples have been presented in Table 2. The WVP value of the control film sample (0.356) was greater than the samples with the added extract. The lowest water permeability was observed in FC-4 (0.333), followed by FC-3 (0.337) and FC-2 (0.348).
A decline in the WVP was observed with an increase in the concentration of the FFE. This behaviour could be due to good intermolecular interaction between the film-forming components with the addition of FFE. The findings of our study are in line with Talón et al. [ 27], who reported a decline in the WVP of the chitosan–starch-based films when incorporated with thyme extract. Furthermore, Yong et al. [ 28] presented parallel results when chitosan-based films were incorporated with purple and black eggplant extracts.
## 3.5. Transparency
An essential physical characteristic of edible films is transparency, which refers to the lack of visibility or resistance to light transmission. The results of the transparency of the CS–SA-based edible films are presented in Table 3. A reduction in the transparency of the films was observed by increasing the concentration of the FFE. The maximum transparency was observed in the control (FC-1) sample ($80.45\%$). The lowest transparency was shown by FC-4 ($46.427\%$) loaded with the highest concentration ($1.5\%$) of FFE. This could be due to the scattering of light with colour components and phenolic compounds in the extract. Qin et al. [ 29] also reported similar results in which pomegranate peel extract was added to the chitosan-based films and the transparency decreased.
## 3.6. Colour Analysis
The apparent colour of the film has a significant impact on how packed food appears, and the colour is a crucial determinant of packaging acceptance by the end consumers. According to the literature, adding plant extracts changes the original colour of the films, but the change is dependent on the source and quantity of the extracts [30]. The colour parameters of the FC-4 sample with a maximum concentration ($1.5\%$) of FFE shifted from transparent to blue-yellow (b*). The control sample (FC-1) was more evident as compared to samples with extract added (Table 3). Adding the FFE slightly decreased the lightness (L*) of the film samples. The current study results demonstrate that adding FFE significantly affected the colour parameters of the film samples.
Zhang et al. [ 24] also reported an increase in the yellow colour of the chitosan-based edible films with the incorporation of banana peel extract. Moreover, in the results of our study, the difference in the colour parameters could be ascribed to the different concentrations of the FFE.
## 3.7. TGA
The thermogravimetric analysis of the film samples was performed to investigate the impact of FFE on the thermal stability of the CS–SA film samples. The examined film samples exhibited similar cycles of weight loss in the TG curve (Figure 1). In the temperature range of 35 to 130 ° C, the occurrence of first thermal degradation could be associated with water evaporation [2]. A weight loss of approximately $10\%$ was observed during the first stage in all the samples, excluding the control (FC-1), which had a relatively dramatic weight loss compared to the other samples. This weight loss could be due to the thermal degradation of polymers having low intermolecular interaction as the addition of FFE would have improved the interaction among the polymers [31].
The second weight loss phase was observed for all samples between 150 and 400 °C. It could be due to the thermal degradation of film-forming components, including sodium alginate, chitosan, glycerol, and FFE. According to reports, glycerol-rich material, chitosan, and sodium alginate break down between 150–260 °C, 240–360 °C, and 229–243 °C, respectively [32,33,34]. The FC-4 sample with $1.5\%$ extract showed good thermal stability compared to the control, FC-2, and FC-3. The results obtained from the thermogravimetric analysis demonstrate that FFE enhanced the intermolecular interaction between chitosan and sodium alginate; hence, thermal stability of the polysaccharide chains improved.
## 3.8. XRD
The XRD patterns of examined film samples, including FC-1, CS + SA; FC-2, CS + SA + FC ($0.5\%$); FC-3, CS + SA + FC ($1\%$); and FC-4, CS + SA + FC ($1.5\%$) is shown in Figure 2. The control (FC-1) sample showed a broader peak at 20° of 2θ and two small peaks, one peak at 10.21° of 2θ, and a small sharp peak at 13.5. The characteristic peak at 10.21° of 2θ disappeared with the incorporation of FFE in film samples. A broader peak was observed for FC-2, FC-3, and FC-4 samples at 20° of 2θ as well as one small sharp peak being observed for all the samples at 13.7° of 2θ. In our previous work, all the edible film samples of chitosan and sodium alginate exhibited a characteristic peak at 14° of 2θ [19].
The results of the current study show the partial crystalline nature of CS–SA-based film samples incorporated with FFE. The percentage of crystallinity of the FC-1, FC-2, FC-3, and FC-4 film samples was $18.3\%$, $14.5\%$, $15.1\%$, and $15\%$, respectively. The addition of FFE slightly decreased the crystallinity of CS–SA-based edible films. The decrease in peak intensity could be attributed to the intermolecular interaction between CS–SA and FFE, which reduces molecule mobility and thus prevents crystallization. The difference in the concentrations of FFE added to the films could be the reason for the different peak intensities observed. The alterations in the peak intensities as a result of alteration in the concentration of film-forming components could be correlated with the mechanical characteristics of the edible films [19].
## 3.9. FTIR Analysis
The interactions between the functional groups of film-forming components including chitosan, sodium alginate, and the FFE were examined. The FTIR spectra of the examined samples is demonstrated in Figure 3, emphasizing the molecular interaction of CS, SA, and FFE. Most of the samples’ FTIR patterns were approximately similar, with a slight difference in the transmission intensity. The peak intensity alteration may indicate the change in extract concentration added during film formation.
The characteristic peaks noticed at 3325 cm−1 represent the stretching of the N–H group while the peaks at 2910.4 cm−1 indicate C–H stretching. The peaks observed at 1623.9 cm−1 and 1550.6 cm−1 specify the stretching of the C=C group and N–O group, respectively. Moreover, the O–H bending of carboxylic and stretching of the C–O group can be observed at 1400.6 cm−1 and 1022.2 cm−1, respectively (Figure 3). A previous study reported that chitosan and sodium alginate-based films exhibited similar patterns during the FTIR analysis [31]. Previous studies described that peaks at 1632 and 1550.6 cm−1 may indicate the presence of chitosan [35,36]. Studies reported that the presence of sodium alginate in the film sample could be attributed to the characteristic peaks at 2910.4 cm−1 and 1400.6 cm−1 [19,37].
In a recent study, the FTIR analysis of the F. racemosa fruit extract exhibited the stretching of the N–H or amine, stretching of the N–O group, and O–H blending of the carboxylic [38]. The characteristics peak at 3325, 1400.6, and 1550.6 cm−1 could be ascribed to the presence of FFE in the edible films and its interaction with film-forming polymers. FTIR analyses showed the interactions between chitosan, sodium alginate, and FFE from the corresponding peak positions.
## 3.10. Scanning Electron Microscopy
The prepared CS–SA-based edible films were examined for their morphological structures. Figure 4 shows the morphological characteristics of film samples, including FC-1, CS + SA; FC-2, CS + SA + FC ($0.5\%$); FC-3, CS + SA + FC ($1\%$); and FC-4, CS + SA + FC ($1.5\%$). The effect of FFE on the characteristics of CS–SA-based films was investigated by observing these samples’ microstructural properties. The structural morphology of the CS (control) FC-1 sample showed roughness, pores, and the presence of tiny particles on the surface (Figure 4). These structural discontinuities in the FC-1 sample align with the findings of Li et al. [ 31] where chitosan and sodium-alginate-based films showed a rough surface, indicating reduced components homogeneity in the film matrix. Previous studies have demonstrated that pores and tiny particles on the surface significantly affect the mechanical and barrier characteristics of films [39].
SEM characteristics of the FC-2 sample with $0.5\%$ FFE showed less roughness and pores than the control. Still, more tiny particles on the surface and bulge structure were observed compared to FC-3 and FC-4 samples. This could be the effect of FFE at $0.5\%$ that interfered with the plasticizing effect of Gly and the film-forming capacity of chitosan [2]. The FC-3 sample with $1\%$ FFE showed less roughness and particles on the surface when compared with the control and FC-1 samples (Figure 4). However, the surface of the FC-3 sample was uneven with cracks compared to the control, FC-2, and FC-4 samples. The processing conditions, such as temperature conditions and film drying, could cause cracks in the film sample [40].
The SEM characteristics of the FC-4 sample with $1.5\%$ FFE showed good morphological characteristics with a uniform structure without any pores, cracks, and fewer particles on the surface as compared to control, FC-2, and FC-3 samples (Figure 4). These morphological changes could be due to the presence of phenolic components acting as crosslinkers in the FFE [41]. It was assumed from the current analysis that increased FFE concentration possibly increased the crosslinking between chitosan and sodium alginate and thus resulted in more uniform films.
## 3.11. Antioxidant Properties
One of the main problems impacting food quality is oxidation-induced deterioration, and this can be minimized with the addition of natural antioxidants to food packaging materials such as edible films [42]. In the current study, FFE was incorporated into chitosan and sodium alginate-based edible films owing to its potential antioxidant activity as demonstrated by various studies [11,43]. Figure 5 shows the free radical scavenging activity of FC-1–4 samples using DPPH and ABTS radical scavenging assays. Results obtained from the DPPH radical scavenging assay showed that the FC-1 sample/control without FFE showed less antioxidant activity than FC-2, FC-3, and FC-4 samples. An increase in the antioxidant value could be attributed to different phenolic compounds present in the fruit of F. racemosa, as evidenced by various studies [10]. The highest % inhibition was found in the FC-4 sample with $1.5\%$ FFE, followed by FC-3 with $1\%$ FFE and FC-2 with $0.5\%$ FFE.
Similar to DPPH radical scavenging, the control/FC-1 sample without FFE exhibited less antioxidant activity, as determined by ABTS radical scavenging activity. The FC-4 sample with $1.5\%$ FFE exhibited the highest % inhibition than control, FC-2, and FC-3. The antioxidant potential of F. racemosa fruit extract was evaluated by Hasan et al. [ 44] using the ABTS method, and the findings revealed that F. racemosa fruit extract contains several potent phytochemicals that present significant antioxidant activity. These findings are in alignment with work where an increase in the antioxidant activity of the chitosan films was observed when incorporated with purple and black eggplant extracts [28].
## 4. Conclusions
The present study reported the development, characterization and biological assessment of CS–SA films loaded with FFE. For the first time, FFE extract was used for edible film production. It was found that the incorporation of extract into the films improved the overall thermal stability and antioxidant activity. Additionally, the incorporation of plant extract into CS–SA films improved the overall elasticity and decreased the WVP and crystallinity of the films. Thus, based on the present findings, FC-4 was more suitable for food packaging. However, further research is required to improve its tensile strength and transparency without impacting its physiochemical properties.
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|
---
title: Electrospun PLA-Based Biomaterials Loaded with Melissa officinalis Extract
with Strong Antioxidant Activity
authors:
- Nikoleta Stoyanova
- Mariya Spasova
- Nevena Manolova
- Iliya Rashkov
- Mariana Kamenova-Nacheva
- Plamena Staleva
- Maya Tavlinova-Kirilova
journal: Polymers
year: 2023
pmcid: PMC10007429
doi: 10.3390/polym15051070
license: CC BY 4.0
---
# Electrospun PLA-Based Biomaterials Loaded with Melissa officinalis Extract with Strong Antioxidant Activity
## Abstract
In the present study, the plant extract *Melissa officinalis* (M. officinalis) was successfully loaded in polymer fibrous materials on the basis of a biodegradable polyester–poly(L-lactide) (PLA) and biocompatible polyether–polyethylene glycol (PEG) by applying the electrospinning method. The optimal process conditions for the preparation of hybrid fibrous materials were found. The extract concentration was varied—0, 5 or 10 wt% in respect of the polymer weight, in order to study its influence on the morphology and the physico-chemical properties of the obtained electrospun materials. All the prepared fibrous mats were composed of defect-free fibers. The mean fiber diameters of the PLA, PLA/M. officinalis (5 wt%) and PLA/M. officinalis (10 wt%) were 1370 ± 220 nm, 1398 ± 233 nm and 1506 ± 242 nm, respectively. The incorporation of the M. officinalis into the fibers resulted in slight increase of the fiber diameters and in increase of the water contact angle values to 133°. The presence of the polyether in the fabricated fibrous material assisted the wetting of the materials imparting them with hydrophilicity (the value of the water contact angle become 0°). Extract-containing fibrous materials displayed strong antioxidant activity as determined by the 2,2-diphenyl-1-picryl-hydrazyl-hydrate free radical method. The DPPH solution color changed to yellow and the absorbance of the DPPH radical dropped by $88.7\%$ and $91\%$ after being in contact with PLA/M. officinalis and PLA/PEG/M. officinalis mats, respectively. These features revealed the M. officinalis—containing fibrous biomaterials promising candidates for pharmaceutical, cosmetic and biomedical use.
## 1. Introduction
Since ancient times, humans have known about plants as a medicinal cure. However, in recent years, plant extracts have attracted increased interest due to their natural origin and complex of desirable properties [1]. Plant extracts have high content of various bioactive compounds such as polyphenols [2] and carotenoids [3] and, therefore, could be used as therapeutic drugs [4], health foods [5], cosmetics [6], chemical alternatives [7], biopesticides, etc. [ 8].
Melissa Officinalis L. (lemon balm or) is a cultivated perennial lemon-scented herb of the Lamiaceae family [9]. This medical plant commonly grows in south-central Europe, North Africa, the Mediterranean region, and Central Asia [10]. Studies have shown that *Melissa officinalis* contains mainly alkaloids, tannins, flavonoids, saponins, and phenolic compounds [11]. The main active constituents of this medical plant are volatile compounds such as geranial, neral, citronellal and geraniol; triterpenes-ursolic acid and oleanolic acid; phenolic compounds such as rosmarinic acid (RA), caffeic acid, and protocatechuic acid and flavonoids-quercetin, rhamnocitrin, luteolin [12].
Many pharmacological studies reported the diverse favorable effects of Melissa officinalis. This medical plant possesses antioxidant [13,14], cytostatic [15,16] and anti-inflammatory effects [17]. In recent decades, several studies revealed the anxiolytic effects of this plant. The methanol extract of M. officinalis and its main component rosmarinic acid (RA) showed GABA-T inhibitory activity on rat brain [18]. Oral administration of the hydroalcoholic and ethanolic extracts of the plant induced anxiolytic-like effects [19]. The use of M. officinalis in the treatment of depression [20], dementia, amnesia [21] and diabetes [22] is described as well.
In recent years, the development of, and advances in, nanotechnology allow the creation of novel materials with improved properties for diverse applications [23]. Electrospinning is a modern, versatile and cost-effective method that enables fabrication of continuous nano- and micro-fibrous materials with adjustable structure, properties, and functions [24]. Electrospun fibers possess very large surface area to volume ratios, high porosity, good mechanical properties, flexibility in functionalization, etc. [ 25]. Moreover, nanofibrous mats can be modified in order to meet the needs of certain applications, such as by incorporation of functional additives or active compounds into the spinning solution [26,27], formation of a coating on their surface [28], or interfacial interaction or polymerization [29,30]. These modification approaches are leading to preparation of electrospun nanofibers with advantages for applications in different fields such as medicine (drug delivery, tissue engineering, wound healing, enzyme immobilization), cosmetics, the food industry, agriculture, water and air filtration, energy, biotechnology, and sensors [31,32,33].
Recently, many research studies have proved that eco-friendly materials for medicinal purposes can be created by combining plant extracts with natural or synthetic polymers. Up until now, diverse plant extracts have been loaded into electrospun fibers such as: *Coptis chinensis* [34] and *Tridax procumbentsis* [35] in poly(vinyl alcohol) nanofibers, Inula graveolens (L.) in polycaprolactone (PCL) [36], eucalyptus citriodora in zein [37], *Curcuma longa* in cellulose acetate [38], *Portulaca oleracea* in PLA [39], etc.
Polylactide (PLA) is a biodegradable thermoplastic polymer derived from renewable, organic sources such as corn starch or sugar cane [40]. The advantages of PLA compared to other biopolymers are numerous, including: it is eco-friendly, biodegradable, recyclable, compostable and non-toxic. PLA hydrolyzes to its constituent α-hydroxy acid when implanted in the human body or in other living organisms. Then, it is incorporated into the tricarboxylic acid cycle and excreted. Moreover, PLA possesses better thermal processability compared to other biopolymers such as polyhydroxyalkanoates (PHA), and poly(ε-caprolactone) (PCL). However, PLA has some drawbacks such as: poor toughness, lack of reactive side-chain groups, slow degradation rate and relatively high hydrophobicity that can lead to low cell affinity and some inflammatory response from the living host [41].
To our knowledge, there is no study in the literature which has reported on the incorporation of a M. officinalis plant extract into electrospun polymer fibers and studies their properties.
In the present work, for the first time, novel biomaterials loaded with M. officinalis plant extract were fabricated by electrospinning. Optimal process parameters were determined in order to obtain uniform fibers. In order to impart hydrophilicity that could assist the action of the extract, a second polymer which was water soluble was added to the polymer matrix. The effect of the incorporation of the plant extract and PEG to the PLA fibers and their properties were studied. Additionally, in the view of the possible materials application in biomedical field, the antioxidant activity of all fibrous materials was investigated.
## 2.1. Used Materials
Poly(L-lactide) (PLA, Ingeo™ Biopolymer 4032D, NatureWorks LLC—USA; Minnetonka, MN, USA; MW = 259,000 g/mol; MW/Mn = 1.94; as determined by size-exclusion chromatography using polystyrene standards), polyethylene glycol (PEG 100,000, Serva, Heidelberg, Germany) were used. Dichloromethane (DCM, Merck, Darmstadt, Germany) and ethanol (abs. EtOH, Merck, Darmstadt, Germany) of analytical-grade purity were used. The 2,2-Diphenyl-1-picrylhydrazyl (DPPH) was supplied from Sigma-Aldrich (Darmstadt, Germany). All chemicals used were of analytical grade and were used as received without any further purification.
Plant material from cultivated Lemon balm (Melissa officinalis) was provided by the company “Essential Oils and Herbs“ Ltd. (grown in the village of Blatets, Bulgaria).
The plant extract was prepared by stirring 268 g of air-dried and ground leaves, flowers and stems of lemon balm (M. officinalis) in $70\%$ of aqueous methanol (solid/liquid ratio of $\frac{1}{30}$ (g/mL) for 24 h at room temperature. Further, the mixture was filtrated and the methanol was evaporated under reduced pressure using a rotary evaporator. The aqueous residue was spray dried on a Buchi Mini Spray Dryer B-290 and 23.01 g ($8.6\%$ yield) of dry extract of M. officinalis was isolated as a yellow-green powder.
## 2.2. Instrumentation and Chromatographic Conditions for Chemical Characterization of Dry Extract of M. officinalis
The chemical characterization of dry extract of M. officinalis was performed by HPLC-DAD-ESI/MS on a Shimadzu LC-2040C 3D Nexera-i and Shimadzu LCMS 2020 (single quadrupole). Separation of compounds was carried out on a column Force C18 (Restek, Bellefonte, PA, USA), 3 μm, 150 mm × 4.6 mm, thermostated at 40 °C. The UV spectra were recorded from 190 to 800 nm. The ion spray voltage was set in the negative mode at −4.50 kV; scan range: 100–1000 m/z; interface temperature: 350 °C; desolvation line: 250 °C; heat block: 200 °C; nebulizing gas flow: 1.5 L/min and drying gas flow: 15 L/min. The solvents used were: (A) $0.1\%$ formic acid in water and (B) acetonitrile. The following gradient program was performed: $12\%$ B isocratic for 5 min, 12–$30\%$ B over 45 min, 30–$90\%$ B over 5 min, $90\%$ B isocratic for 1 min, 90–$12\%$ B over 1 min, and re-equilibration of the column for 5 min. The flow rate was 0.5 mL/min and the injected volume was 2 μL. The extract was dissolved in methanol at a concentration of 550 mg/L. At the same conditions quantification of rosmarinic acid was performed in the extract. The wavelength selected for the quantification was 330 nm. Identification was accomplished by comparing the retention times (Rt) and UV spectra of the corresponding peak in the sample to those of the standard. The amount of rosmarinic acid was calculated utilizing a calibration curve (1–50 mg/L, r2 = 0.9999).
## 2.3. Fabrication of Fibrous Mats by Electrospinning
Different types of fibrous materials, including PLA, PLA/PEG, PLA/M. officinalis and PLA/PEG/M. officinalis were fabricated by electrospinning. Prior to electrospinning, the following spinning solutions were prepared in a mixture of dichloromethane/ethanol $\frac{80}{20}$ v/v: [1] PLA (10 wt%), [2] PLA/PEG ($\frac{80}{20}$ w/w), [3] PLA/M. officinalis (5 and 10 wt%) and [4] PLA/PEG/M. officinalis (5 and 10 wt%). The total polymer concentration was 10 wt%.
The prepared solutions were then transferred into a 5 mL syringe equipped with a metal needle (size: 20GX1½″) whose tip was attached to the positively charged electrode. The electrode was connected to a specially constructed high voltage power source that could generate positive DC voltages between 10 and 30 kV. The electrospun fibers were collected on a grounded rotating drum, which had a diameter of 45 mm. The collector was placed 15 cm away from the needle’s tip. The collector rotation speed was 1000 rpm. The spinning solutions were delivered by an infusion pump (NE-300 Just InfusionTM Syringe Pump, New Era Pump Systems Inc., Farmingdale, NY, USA) at a constant feed rate of 3 mL/h. The other parameters were as follows: applied voltage −25 kV, room temperature −21 °C and a relative humidity of $53\%$. All the prepared fibrous materials were placed under reduced pressure at 25 °C to remove any remaining solvent.
## 2.4. Complex Characterization of the Fibrous Materials
The dynamic viscosity of the prepared spinning solutions were determined on a Brookfield DV-II+ Pro programmable viscometer equipped with a sample thermostatic cup and a cone spindle for the one/plate option operating at room temperature −25 °C.
Scanning electron microscopy was used to study in detail the morphology of the fabricated electrospun fibrous materials. Prior to SEM observation on Jeol JSM-5510 (JEOL Co., Ltd., Tokyo, Japan), the materials were vacuum-coated with gold on a Jeol JFC-1200 fine coater for 60 s. The captured SEM micrographs were used to determine the mean fiber diameter and the standard deviation by using Image J software and measuring at least 30 fibers from SEM images [42].
Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic analysis was performed on IRAffinity-1 spectrophotometer (Shimadzu, Kyoto, Japan) equipped with a MIRacle ATR accessory (diamond crystal, depth of penetration of the IR beam into the material is 2 μm). The spectra were recorded in range 4000 to 500 cm−1 with a spectral resolution of 4 cm−1 using a DLATGS detector connected with a temperature controller. H2O and CO2 content of all spectra was adjusted with IRsolution’s software. All the samples were dried under reduced pressure before analysis.
The hydrophobic/hydrophilic balance of the surface of the prepared fibrous materials was studied by measuring the static contact angle on a DSA 10-MK2 drop shape analyzer system (Krüss, Hamburg, Germany) at 20 ± 0.2 °C. Contact angles of the fibrous materials were measured by dropping a deionized water droplet with volume of 10 μL controlled by a computer dosing system. The droplet’s temporal photographs were captured. Computer analysis of the obtained images was used to measure the water contact angles. The represented mean contact angle value is a result of 20 measurements taken on various regions of the mat surfaces.
X-ray diffraction analysis (XRD) was performed to analyze the crystalline structure of the plant extract and fabricated electrospun fibrous materials. D8 Bruker Advance powder diffractometer (Bruker, Billerica, MA, USA) equipped with a filtered CuK radiation source and a luminous detector (step of 0.02° and counting time of 1 s/step) was used to record the XRD patterns.
Mechanical properties of the fibrous materials were determined by tensile measurements performed on a single column system for mechanical testing, INSTRON 3344, equipped with a loading cell of 50 N and Bluehill universal software. The initial length between the clamps was 40 mm and the used stretching rate was 10 mm/min. The fibrous samples were cut with dimensions of 20 × 60 mm2. A Digital Thickness Gauge FD 50 (Kafer GmbH, München, Germany) was used to determine the thickness of the fibrous materials. The average thickness was ca. 250 μm ± 20 nm.
Thermogravimetric analysis (TGA) was performed on Perkin Elmer TGA 4000 (Waltham, MA, USA) at 10 °C/min heating rate under argon flow of 60 mL/min. Pyris v.11.0.0.0449 software was used for instrument control, data collecting, and data processing.
A radical scavenging assay with 2,2-diphenyl-1-picrylhydrazyl (DPPH) was used to determine the antioxidant activity of the M. officinalis extract and the antioxidant capacity of the fabricated fibrous materials. Ethanol solution of the plant extract and PLA, PLA/M. officinalis (10 wt%), PLA/PEG and PLA/PEG/M. officinalis (10 wt%) electrospun samples with weight of 0.5 mg were immersed in 3 mL of ethanol DPPH solution (1 × 10−4 M). Then, the solutions were placed for 30 min at 20 °C in the dark. A DU 800 UV-Vis spectrophotometer (Beckman Coulter, Brea, CA, USA) was used to characterize the solutions’ absorbance at 517 nm in order to determine how many DPPH radicals were still present in the solution. The antioxidant activity (AA%) was calculated by using the following equation:Inhibition, AA,%=[(ADPPH−Asample)ADPPH]× 100 In the used equation, the Asample- ADPPH• is the solution absorption at 517 nm after the addition of the extract solution or fibrous materials. ADPPH• presents the absorption for DPPH• solution at 517 nm. Each experiment was performed three times.
## 2.5. Statistical Analysis
The results’ data were displayed as means ± standard deviation (SD). One-way analysis of variance (ANOVA) and the post hoc comparison test (Bonferroni) were used with the GraphPAD PRISM program, version 5, to evaluate the statistical significance of the data (GraphPad Software Inc., San Diego, CA, USA). Statistics were considered to be significant for values of * $p \leq 0.05$, ** $p \leq 0.01$ and *** $p \leq 0.001.$
## 3.1. Identification of Main Phenolic Compounds and Quantification of Rosmarinic Acid in the Dry Extract of M. officinalis
The identification of the main phenolic compounds in the extracts of M. officinalis was performed by HPLC-DAD-ESI/MS. ESI negative mode was performed since it is more sensitive for phenolic compounds. Peak identification was carried out by comparing retention times, UV and mass spectra of individual components with those of standards and literature data. Total ion chromatogram (TIC) in negative mode of M. officinalis extract is shown on the Figure 1 and the data of the tentatively identified compounds is presented in Table 1.
The LC-MS data showed the presence of rosmarinic acid—a caffeic acid ester (Figure 1; peak 8, rt: 34.1 min, [M-H]−: 359) and caffeic acid (Figure 1; peak 4, rt: 12.8 min, [M-H]−: 179). Their identity was confirmed by comparison with commercial standards. Other compounds identified included RA derivatives such as sulphated rosmarinic acid (Figure 1; peak 7, rt: 32.2 min, [M-H]−: 439), sagerinic acid—a rosmarinic acid dimer (Figure 1, peak 6, rt: 30.4 min [M-H]−: 719), lithospermic acid A (Figure 1, peak 10, rt: 41.6 min M–H: 537), salvianolic acid isomer and salvianolic acid C derivative (Figure 1, peak 11 and 12, rt: 45.2 and 52.2 min, [M-H]−: 717 and 715, respectively). Other phenolic acid identified were 3-(3,4-dihydroxyphenyl)-lactic acid (Figure 1; peak 2, rt: 5.1 min, [M-H]−: 197; dimer [2M-H]−: 395) and caftaric acid (Figure 1; peak 3, rt: 6.5 min, [M-H]−: 311). The cyclic polyol quinic acid (Figure 1; peak 1, rt: 3.0 min, [M-H]−: 191) was also detected in the sample. Finally, peak 9 was identified as luteolin 7-O-glucuronide, the only flavonoid found in the extract (Figure 1, rt: 35.5 min, [M-H]−: 461). Its identification was based on the fragmentation found in the literature (Table 1). Since, rosmarinic acid was identified as a major component in the extract, quantification studies for this compound were performed. The amount of rosmarinic acid found in the extract was 76.27 ± 0.1 mg/g. Numerous studies have shown a direct relationship between the presence of RA and the in vitro bioactivities demonstrated by extracts [12].
## 3.2. Morphological Analysis of the Obtained Electrospun Materials
The morphology of the obtained electrospun fibers was assessed by using scanning electron microscopy (SEM). Figure 2 reveals the morphology of the prepared fibrous materials based on: (a) PLA, (b) PLA/PEG, (c) PLA/M. officinalis (5 wt%); (d) PLA/PEG/M. officinalis (5 wt%), (e) PLA/M. officinalis (10 wt%) and PLA/PEG/M. officinalis (10 wt%). All the materials were obtained by one-pot electrospinning. As seen in Figure 2, the all fibrous mats were composed of cylindrical and defect-free fibers. The incorporation of PEG and the plant extract to the PLA solution and subsequent electrospinning do not provoke the appearance of process instability and defect formation. The average fiber diameter of the prepared fibers was determined by using the acquired SEM images. The measured mean fiber diameters of the PLA, PLA/M. officinalis (5 wt%) and PLA/M. officinalis (10 wt%) were 1370 ± 220 nm, 1398 ± 233 nm and 1506 ± 242 nm, respectively. It was ascertained that the loading of the plant extract of M. officinalis resulted in slight increase of the measured fiber diameters, while preserving their cylindrical form. This slight increase in fiber diameters and their distributions is most probably due to increase of the dynamic viscosity of the spinning solutions. The measured values of the PLA, PLA/M. officinalis (5 wt%) and PLA/M. officinalis (10 wt%) spinning solutions were 1700 cP, 1767 cP and 1802 cP, respectively. Furthermore, the incorporation of a second polymer–PEG to the spinning solution had influence on the viscosity and mean fiber diameters as well. The PLA/PEG, PLA/PEG/M. officinalis (5 wt%) and PLA/PEG/M. officinalis (10 wt%) spinning solutions have the following dynamic viscosity values: 1245 cP, 1500 cP and 1528 cP. The incorporation of PEG to PLA solution resulted in decrease of the viscosity and fiber’ diameters compared to the PLA one. The loading of M. officinalis extract to the PLA or PLA/PEG resulted in a slight raise in solution viscosity and in the resulted fiber diameters. Increasing the concentration of the crude plant extract in the spinning solutions and, consequently, in the fibrous mats led to preparation of thicker fibers.
## 3.3. ATR-FTIR Analysis
PLA, PLA/PEG, PLA/M. officinalis and PLA/PEG/M. officinalis fibrous materials, as well as M. officinalis extract (powder) were characterized by ATR-FTIR spectroscopy. The recorded spectra of the extract, PLA and PLA/M. officinalis were presented in Figure 3. The methanolic extract of M. officinalis is a complex mixture which could be easily seen from its FTIR spectrum where a number of absorption peaks are present. The strong bands at 3358 and 3244 cm−1 could be assigned to O-H stretch, H-bonded corresponded to alcohols and phenols, 2359 cm−1 assigned for single aldehyde, 1594 cm−1 indicates the fingerprint region of C-O stretching. The absorption bands at 1259 cm−1 and 1394 cm−1 were attributed to the symmetric deformation of the –CH3 group. FTIR spectra of M. officinalis extract displays a band corresponding to C-O stretching vibrations (1047 cm−1). The absorption bands at 816 cm−1, as well as at 775 cm−1, are due to bending of the aromatic ring of the polyphenols [47]. In the FTIR spectra of the PLA mat, characteristic stretching frequencies for C=O, –CH3 asymmetric, –CH3 symmetric, and C–O, at 1747, 2995, 2945 and 1084 cm−1 were presented [39]. It was determined that the bending frequencies for –CH3 asymmetric and –CH3 symmetric are identified at 1452 and 1361 cm−1, respectively. In the spectrum of the PLA/M. officinalis fibrous mat, characteristic bands for PLA as well as for the lemon balm (1748 and 2359 cm−1, respectively) were detected. The presence of PEG in PLA/PEG and PLA/PEG/M. oficinalis mats resulted in detecting additional bands at 2881, 2332, 1359 and 963 cm−1, characteristic for PEG (Supplementary material Figure S1). In the IR spectrum of PLA/PEG/M. officinalis, fibrous material bands characteristic for PLA, PEG and plant extract are presented, proving the successful incorporation of the M. officinalis in the PLA/PEG polymer matrix.
## 3.4. Water Contact Angle
One of the key characteristics of materials is their hydrophilic/hydrophobic properties [48]. It is known that the wetting behavior depends on the chemical nature of the solid and liquid phases. Hydrophilic surfaces show strong affinity to water and the water droplet is spreading rapidly on this kind of surfaces. The degree of hydrophilicity/hydrophobicity of the materials’ surface could be determined by measuring the contact angle between the liquid and solid phases.
Therefore, the water contact angles values and the shape of the water drop on the prepared fibrous mats were detected. Digital images of the water droplets that were deposited on the fiber mats’ surfaces were displayed in Figure 4. The value of the contact angle of PLA fibers is 110° ± 2.6°. This value is in very good agreement with the values found in the literature, proving that the PLA fibrous mat is hydrophobic [49]. The incorporation of the M. officinalis crude extract resulted in slight increase of the measured water contact angle. The determined values for the PLA mat loaded with 5 wt% and 10 wt% of M. officinalis were 121° ± 2.7° and 133° ± 2.5°, respectively. The incorporation into the fibrous matrix of a second polymer–PEG which is water-soluble resulted in hyrophilization of the prepared hybrid mats. The water contact angle values right after dropping the water droplets on the PLA/PEG and PLA/PEG/M. officinalis (10 wt%) mats were 0° and 28°. After 2 min, the water drops completely absorb into the fibrous materials proving that the PEG-containing fibers are hydrophilic with water contact angle of 0°. This imparted hydrophilicity is a considered as crucial characteristic for attaining a rapid therapeutic action of the biologically active compounds from the extract when applying the obtained materials in medical and pharmaceutical fields.
## 3.5. X-ray Diffraction Analysis
Figure 5 presents the XRD patterns of M. officinalis powder extract and mats composed of PLA, PLA/M. officinalis (10 wt%), PLA/PEG and PLA/PEG/M. officinalis (10 wt%) which were recorded in the range 2θ 10–60°. As seen from the XRD pattern, M. officinalis powder diffractogram showed no crystalline planes. This finding is similar to the results reported by Santos et al., showing no crystallinity in the C. officinalis extract [50]. As expected, in the XRD spectra of electrospun PLA and PLA/M. officinalis fibrous mats, no diffraction peaks are detected, revealing that materials on the basis of PLA are amorphous. In the XRD pattern of PLA/PEG fibers, one of the PEG well-distinguished peaks appeared at 19.3° assigned to a set of planes [120] [51]. However, the X-ray diffraction studies revealed that the M. officinalis plant extract and the obtained electrospun mats were amorphous. Except the peak for the PEG crystallinity, in all patterns the existence of an amorphous halo is presented. This means that biologically active substances in the crude extract as well as in the electrospun fibrous mats are in an amorphous form.
## 3.6. Mechanical Properties
One of the most important properties of the electrospun fibers is their mechanical characteristics, which plays a key role in determining the fibers’ applications. The mechanical characteristics of the fibrous mats depend strongly on measurement technique, conditions of fiber fabrication, fiber orientation, point bonding, crosslinking, etc. Moreover, additional component(s) to the spinning solution might have significant effect on the mechanical behavior of the resulting hybrid fibers. Therefore, it is crucial to study the influence of the extract on the mechanical properties of the hybrid PLA/M. officinalis mats and PLA/PEG/M. officinalis. Mechanical characteristics of the obtained electrospun mats were determined using a single-column tensile testing machine. The typical stress–strain curves of PLA, PLA/M. officinalis, PLA/PEG and PLA/PEG/M. officinalis mats are presented in Figure 6. The highest value of the tensile strength was determined for the PLA fibrous mat −4.6 MPa. The incorporation of the M. officinalis extract resulted in a decrease of the tensile strength to 3 MPa. The decrease in mechanical characteristics might be due to the incorporation of low molecular weight compound in the PLA matrix, which might have generated weak spots when the tensile test was carried out. The incorporation of PEG affected the mechanical properties as well. The detected decease could be attributed to the molecular weight of the used PEG. Despite the slight decrease in the mechanical characteristics of the hybrid fibers, the fibrous materials preserve good mechanical properties.
## 3.7. Thermal Analysis
The thermal behavior of the plant extract (powder) and of the obtained novel fibrous materials of PLA and PLA/M. officinalis was determined by TGA analysis. The temperature range was from 50 to 800 °C. The TG thermograms of M. officinalis extract, PLA fibers and PLA/M. officinalis fibers were shown in Figure 7. As seen from Figure 7, the M. officinalis extract showed degradation in four steps. The first weight loss (~$3.5\%$) taking place between 50 °C to 110 °C is attributed to the loss of adsorbed water. The second weight loss (110 °C to 250 °C) of about $16.5\%$, third weight loss between 250 °C and 350 °C of ∼$16\%$ and fourth loss of about ∼$23\%$ are attributed to the decomposition of phenolic compounds and flavonoids. TG thermograms of PLA and PLA/M. officinalis fibrous mats showed one decomposition peak. The thermal decomposition of electrospun PLA mat started at 355 °C and ended at 423 °C due to the decomposition of the polyester. The presence of M. officinalis into the PLA shifts the thermal degradation temperature to lower temperatures. The thermal degradation of electrospun PLA/M. officinalis fibrous mat began at 312 °C and ended at 354 °C. The residual weight at 800 °C was $0.12\%$ for the PLA mat and $7.19\%$ for the PLA/M. officinalis electrospun material.
## 3.8. Determination of Antioxidant Activity
Oxidative stress plays major role in the pathogenesis of many neurological diseases, including Alzheimer’s disease, Parkinson’s disease and Huntington’s disease; therefore, it has been suggested that antioxidants that could counter cellular oxidative stress within the nervous system could be a potential treatment. Some plant extracts possess strong antioxidant activity [52]. It is known that *Melissa officinalis* is a rich source of antioxidants, in particular from the group of phenolic compounds [53].
A recognized method for evaluating the antioxidant activity of plant extracts is DPPH free radical scavenging [54]. The capacity of the plant extractives to donate hydrogen atoms was assessed using the decolorization of a solution of 2,2-diphenyl-1-picrylhydrazyl. In a methanol or ethanol solution, DPPH creates a violet or purple color that, in the presence of antioxidants, fades to varying colors of yellow. The absorbance of the solutions was measured spectrophotometrically at 517 nm.
The DPPH scavenging ability of PLA, PLA/PEG, PLA/M. officinalis and PLA/PEG/M. officinalis mats were determined. The DPPH capacity of the crude extract was assessed as well. As could be seen from Figure 8, the ethanol solution of M. officinalis extract showed the highest antioxidant activity ($93.2\%$ ± $2.1\%$). After being in contact with PLA/M. officinalis and PLA/PEG/M. officinalis, the DPPH solution color changed to yellow and the absorbance of the DPPH radical was dropped by $88.7\%$ ± $1.4\%$ and $91\%$ ± $2.2\%$, respectively. In contrast, upon contact with PLA and PLA/PEG fibrous materials, the absorbance of the radical decreased by $3.2\%$ ± $0.15\%$ and $2.4\%$ ± $0.2\%$, respectively, revealing the low antioxidant activity of the polymer materials itself. Moreover, after 30 min of exposure of the PLA and PLA/PEG to the DPPH solution, no significant change in the violet color of the DPPH solution was observed. The results on the antioxidant capacity of the prepared materials revealed that the polymer materials loaded with M. officinalis extract possessed high antioxidant activity comparable to that of the crude extract.
## 4. Conclusions
Novel PLA and PLA/PEG-based fibrous materials containing M. officinalis plant extract were fabricated by applying the electrospinning method. The average fiber diameters depend on the composition of the spinning solutions and the resulting fibers. It was established that the addition of the extract slightly enhanced the values of the dynamic viscosity, increased the mean fiber diameters and the hydrophobicity of the fibers while the addition of the water soluble polymer–PEG resulted in decrease of the spinning solutions’ viscosity values and imparted hydrophilicity to the fabricated fibrous materials. The PLA/PEG/M. officinalis mat and PLA/M. officinalis mat displayed strong antioxidant activity comparable to that of the crude extract. The obtained results reveal that the created novel fibrous materials loaded with the M. officinalis plant extract are prospective for use in wound dressing applications.
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|
---
title: Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass
Supply Chain
authors:
- Ange-Lionel Toba
- Rajiv Paudel
- Yingqian Lin
- Rohit V. Mendadhala
- Damon S. Hartley
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10007443
doi: 10.3390/s23052421
license: CC BY 4.0
---
# Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass Supply Chain
## Abstract
A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about $30\%$ more distance covered for feedstock transportation.
## 1. Introduction
The agricultural supply chain refers to the supply chain for any type of agricultural product such as dairy, grain, vegetable, fruit, or biomass feedstock. It normally starts from agricultural production and ends at distribution to customers, and it may include many actors such as farmers, suppliers, researchers, distributors, customers, and stakeholders. The sustainability of agricultural supply chains is about achieving maximal performances in economic growth, environmental protection, and social development [1]. To ensure the sustainability of agricultural supply chains, it is critical to improve farmers’ willingness to participate in marketing and coordination strategies and support the reliability of product supply [2]. Other key factors influencing sustainability include farm inputs, energy use efficiency, waste management, and logistical efficiencies during crop production, harvesting, storage, and handling [3].
Different frameworks and metrics have been developed and proposed to measure the sustainability (social, environmental, and economic) of agricultural supply chains. For example, social dimensions of an agricultural supply chain include aspects such as job creation [4], benefits to rural communities [5,6], health and safety practices [7], and community initiatives [8]. Environmental concerns are generally related to heavy chemicals (fertilizers and herbicides) and fossil fuel usage in farming practices, which lead to environmental degradation and public health issues [9]. Soil quality and soil carbon dynamics have also been identified as important metrics in measuring the sustainability of a crop production system [10], as well as soil carbon sequestration. Techniques such as reduced tillage [11], on-farm compost [12], cover crops [13,14] have been evidenced to affect soil health. Land suitability analysis has also helped inform decision making on environmental health [15,16]. Metrics that evaluate economic sustainability are generally revenue-oriented [1]. Key indicators in measuring economic sustainability or viability include profitability, liquidity, stability, and productivity [17]. Profitability measures the difference or ratio between the revenue generated and the cost of inputs used, liquidity indicates the return-on-investment term, stability is estimated by the share and development of capital investment, and productivity measures the ratio of input and out or production efficiency of a certain system. In a biofuel supply chain, the economic performance indicators are generally defined as cradle-to-reactor production costs [18] and potential savings from greenhouse gas emission reductions [19].
Integrated landscape management (ILM) has emerged as a strategy to integrate biomass production practices into agricultural production fields to increase agricultural supply chain sustainability [20]. Applicable field areas include low-yielding zones where soil properties are not conducive to row crop production or areas where high crop productivity generates excess crop residues that benefit from biomass residue harvest and collection. ILM can be viewed as an improvement over current agricultural management schemas that are wholly dependent upon monoculture agriculture and do not account for subfield variability. In this paper, we are interested in the application of ILM for biomass feedstock production supply chain sustainability via the simultaneous production of agricultural and woody crops on the same land parcel.
Integrating short-rotation woody crops (SRWC) such as poplars and shrub willows into the agricultural landscape has been demonstrated to result in many environmental and economic benefits such as water quality improvement (nutrient loading reduction), soil carbon sequestration [21], wildlife habitat benefits [22], nitrate leaching reduction [23,24] as well as GHG emissions reduction [25,26]. In addition, these woody crops are economically viable on lands marginal for row-crop production and able to produce more yield than normal hardwood species [27].
Several research papers have investigated how biomass supply chains can be organized in a cost-efficient manner to collect, process, and transfer available biomass to a specific biorefinery plant [28,29,30,31]. One of the main methods used is mathematical programming (or optimization theory), including linear programming (LP), nonlinear programming (NLP), mixed integer linear programming (MILP), and mixed integer nonlinear programming (MINLP) models [32,33,34,35,36,37,38,39,40,41]. Heuristics and meta-heuristic techniques have also been employed [42,43,44,45]. Although not necessarily providing optimal solutions, heuristics explores near-optimal solutions to complex problems in a quicker manner and with less computational demand. These approaches are combined with the geographic information system (GIS) to capture spatial and temporal information to conduct biofuel logistics studies. Simulation modeling is also used [46,47,48,49,50,51] to assess biomass supply chain operations under various scenarios, including the uncertainties and variabilities that exist in systems.
Most of these studies only consider the economic aspect, that is, minimizing the total costs related to facilities, transportation, harvesting, and collection. There has also been a growing interest in incorporating environmental objectives to biomass supply chain analysis. However, environmental impacts have only been quantified in terms of CO2 emission due to transportation and biorefinery operations [52,53,54,55]. Although costs and GHG emission are important in developing a sustainable biomass supply chain, site suitability through ecological indicators is as important and needs to be accounted for. Soil characteristics or ecological indicators are less often investigated, yet critical in planning for a sustainable biomass supply, as it dictates feedstock production. The chain starts from raw materials supply and terminates with end customers thanks to various processing and movement across locations. Without feedstock supply, there can be no bioenergy, and this supply is only made possible within the constraints of acceptable soil features.
In this study, we aim to analyze biomass supply chain sustainability by considering both economic and environmental aspects, using suitability rather than optimality only, as opposed to the current literature. Economic impacts are captured via the distances traveled. Shorter distances would incur lower costs and generate lower emissions, with potential savings. Environmental impacts are captured via ecological indicators, gauging suitability for SRWC. Practically, our research investigates field-level design for polyculture landscapes with both agricultural crop and woody biomass feedstock crop production with environmental and economic sustainability. This method consists of integrating [1] a field suitability model for energy crop production, providing agricultural fields scoring based on soil characteristics relevant to SRWC, and [2] a transportation network model, also providing scoring based on pre-processing or depot location distance to fields.
The next section details the data used and the method developed, including problem characterization and methodology. Section 3 presents the results obtained, while Section 4 provides conclusions.
## 2.1. Problem Characterization
We define the feedstock supply system as a system infrastructure for the collection, transportation, and transformation of feedstock for bioenergy production. The objective is to manage the flow of material and information in the chain of supply in a way that will provide the highest cost-effective and environmentally friendly advantage. The idea is to move feedstock from the fields to the closest preprocessing depots, and later to biorefineries for conversion. Thin blue lines represent the transportation network system (Figure 1). Note that the lines indicate potential transport connections, as fields are not necessarily all connected to multiple depots.
We consider the integration of 2 layers: fields and transportation network layers (Figure 2). The fields layer encompasses data related to agricultural fields, including shape, geographical details (latitude and longitude), field suitability index, ecological indexes, and acreage. The transportation network layer encompasses data related to road and rail networks, including intersections and distances, enabling transportation of feedstock from fields to depots, and biorefinery. Note, however, that the focus of this study is more on depot location. Both layers exchange data, with the conversion of field data into road data, and vice versa. This conversion is explained in Section 2.2.2.
The integrated layer thus constitutes the location of the depots, as well as a biorefinery. The number of depots and their locations is determined based on proximity to the fields so as to minimize feedstock distance traveled and field suitability to SRWC. The location of the biorefinery is assumed at the most populous area in the region considered for analysis. The rationale behind this selection is that such a populated area can serve as a workforce.
## 2.2. Methodology
The methodology (Figure 3) consists of developing an integrated site suitability analysis, incorporating the SRWC feedstock along with the transportation network to score agricultural fields, for the potential to sustainably supply biomass. Data used include geospatial field information, as well as road/rail networks.
The first step is the site suitability analysis, consisting of scoring fields based on ecological/environmental factors. These factors are defined in Section 2.2.1. The scores help identify the best locations for an activity, in our case, SRWC production. Site suitability modeling is a widely used approach for these questions [56,57,58], factoring in multiple factors, different in importance, highlighting locations that best meet selected criteria for said site. The scores are normalized to a 0–1 scale. The fields’ centroids are also determined, to be used in the transportation network analysis. The second step is the transportation analysis. As feedstock is produced on fields, it needs to be processed, first through depots, and then through biorefineries, using road and rail networks as their transportation means. The selection of depots is performed with the goal of reducing costs and negative environmental implications. We look to find the closest routes/distances from each field to the depot locations. All distances are then normalized to a 0–1 scale, 1 scoring the fields that are closest from the depots and 0 the farthest.
The final step is the combination of both scoring values. This integrated site suitability index (SSI) is computed by taking the average of the initial SSI and normalized depot distance values.
## 2.2.1. Site SUITABILITY ANALYSIS
Site suitability modeling is used to identify, qualify, compare, and rank candidate pixels that are more appropriate for a certain crop. Each factor is standardized to a value between 0 and 1, 1 being the most suitable, and 0 being the least suitable. These factors selected are specific to SRWC and woody biomass site suitability analysis. See Table 1 for details about these factors and their data sources.
The site suitability value for each field is calculated using a linear fuzzy logic prediction model developed by Wu, et al. [ 59] and shown in the equation below:[1]SSIi=∑$k = 0$nfmiwm×∏bn where SSI for field i is the site suitability index, fm is the fuzzy value of criteria m for field i, wm is the weight of criteria m, bn is the criteria score of constraint n (binary value), and ∏ is the product. Binary values (0 and 1) were assigned to land cover and slope. Fuzzy logic membership functions were built to determine the fuzzy value of the other criteria, including water availability, soil productivity, soil texture, and erodibility factor. The final calculation normalizes the site suitability values to a range of 0 and 1 based on the weighting values. For this analysis, all weighting values were set to 1.
**Table 1**
| Factors | Data/Assumptions |
| --- | --- |
| Land cover/crop rotation: SRWC helps improve soil attributes, reduce soil erosion, and sequester soil organic carbon [60]. ILM SRWC land suitability analysis mainly targets low-productivity agricultural fields. This ensures energy production and environmental protection without compromising food production. | USDA National Agriculture Statistics Services (NASS) data obtained from https://nassgeodata.gmu.edu/CropScape/ (accessed on 26 August 2022). The crop data layer from 2018, which consists of 30 m resolution raster data, was converted to vector fields. Field-level cultivation information was also obtained from the CDL layer and a subset of cultivable area larger than 100 acres was considered for the analysis. |
| Slope: Growing SRWC on slopes helps in stabilizing land, reducing runoff, and controlling soil erosion. However, steep slopes could be problematic for equipment operations. A slope > 8% is less desirable for SRWC due to difficulties in using harvesting equipment [61,62]. | National elevation data (NED) for slope information were obtained from https://www.usgs.gov/the-national-map-data-delivery/gis-data-download (accessed on 26 August 2022). From the NED data, we only considered fields with slopes less than 8%. |
| Soil Productivity: We used the National Commodity Crop Productivity Index (NCCPI). This index was developed by the USDA to estimate commodity crop (i.e., corn, soybeans, cotton, or small grains) productivity in non-irrigated agricultural land [63]. Since we prioritize low-productive agriculture fields for SRWC production, areas having low NCCPI values are prioritized. | Gridded Soil Survey Geographic (gSSURGO) Database was used and obtained from https://data.nal.usda.gov/dataset/gridded-soil-survey-geographic-database-gssurgo (accessed on 26 August 2022). |
| Soil texture: Soil texture is important for the retention of soil moisture as well as plant root growth. Pinno and Belanger [64] study found soil texture to be the best predictor of tree growth. | Data used were from SSURGO datasets. (https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/, accessed on 26 August 2022). We prioritized land with high silt and clay for woody biomass production. |
| Soil erodibility factor: Soil erosion is the result of inadequate soil management and constitutes a major threat to the productivity and sustainability of crops [65]. We used the K factor. | Data used were from SSURGO datasets. (https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/, accessed on 26 August 2022). |
| Water Availability: Water is essential for plant growth. In dry and non-irrigated environments, soil moisture is extremely important for the growth of SRWC [66,67]. We used the soil availability water storage (AWS) index. | Data used were from SSURGO datasets. (https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/, accessed on 26 August 2022). |
## 2.2.2. Transportation Analysis
This analysis is conducted to determine the locations of depots using transportation networks. The road network is used for transportation from fields to depots. For implementation, we present 2 methods: clustering analysis and graph theory. Depots are assumed to be located at the center of spatial or graph-based clusters. We use OSMnx, a Python package to model, project, visualize, and analyze real-world street networks as well as geospatial geometries [68]. Other Python libraries used are Networkx, for the creation and analysis of the structure and dynamics of networks, represented in the form of graphs with nodes and edges [69], and GeoPandas, Shapely, Rasterio, and Rasterstats to manipulate geospatial data and allow spatial operations [70,71,72].
The methods employed essentially help translate information from the fields layer into information for the transportation network layer, and vice versa. In both methods, each field centroid is tied to, or paired with, the closest node on the road network in terms of distance. For our study, we used Euclidean distance, which is the straight-line distance d in parameter space between two points of coordinates (x1, y1) and (x2, y2), given by the following equation [73]:[2]d=(y2−y1)2+(x2−x1)2 Just like fields are scored for their suitability, distances from fields to depots, using road networks, are also scored. These distances are normalized to obtain a 0 to 1 score, with 1 for the closest and 0 for the farthest.
## Graph Theory
Graph theory helps describe the characterization of the transportation network. Graphs formally represent a network, which is basically composed of vertices (or nodes) that are connected by edges (or links). Graph theory provides meaningful information about the topological architecture of the networks at hand [74]. Applied to transportation, it helps quantify levels of modular organization and the connectedness of field locations for depot selection. The most connected/accessible location ranks the highest to be a depot. As we intend to identify connectivity patterns in the transportation network to identify the most suitable depot siting, this approach is appropriate. More information about graph theory can be found in [75,76].
For this analysis, we used the clustering coefficient, which represents “the degree to which nodes in a graph tend to cluster together” [77]. By nodes, we mean road intersections. This metric represents the level of connectivity in or density of the network. A dense network is a network in which each node is linked to almost all other nodes, while in a sparse network, the number of connections is low. All values belong to 0–1 interval, 1 indicating the densest, and 0 indicating the least dense.
All nodes’ data information is converted into field data. The goal is to find the closest field centroids to the road nodes/intersections. Using the coordinates of the nodes and the centroids, we compute the Euclidean distance to find these centroids (Figure 4, right. The red star is the field identified as a depot candidate). This is to make sure all fields are accessible via the road network. The field’s centroid that is the closest to the node/intersection with the most connected nodes is identified as a depot candidate. Considering the large size of the area (defined in Section 2.3), several nodes had 1 as the clustering coefficient. We, therefore, used the preliminary SSI and service area radius for depot selection. Candidate depots with 1 as the clustering coefficient value are ranked based on the highest SSI value. The 1st selected is the field with the highest SSI score. The 2nd field, with the 2nd highest SSI, is selected if the distance between 1st and 2nd is greater than the service area radius. The cycle continues until there are no additional candidate depots outside the service area.
## Clustering Analysis
Clustering is a popular machine learning technique that is a method of unsupervised learning used for statistical data analysis. We used K-means clustering (centroid-based clustering algorithm), which consists of sorting N items or observations into K groups/clusters, often to uncover a structure within a complex set of data [78]. Each of these points is assigned to a cluster based on its squared distance from the centroid of that cluster [79]. Applied to our transportation problem, it helps find the best depot locations to service a given set of fields. Depots are viewed as cluster centroids and field locations as the data to be clustered. As we intend to analyze connectivity patterns in the transportation network to identify the most suitable depot siting, this approach is also appropriate. More information about K-means can be found in [80].
For this analysis, our goal was to minimize the distance between the chosen depot location and field centroid. Mathematically, this comes down to maximizing the distance between, and minimizing the distance within a predetermined number of clusters, K, in Equation [3], as defined in Tan, et al. [ 81]:[3]E=∑j∑i(xij−xj)2 where E is the squared error function, xij is an item (here field) i assigned to cluster j, and xj is the centroid of cluster j. The objective is to minimize E. Evidently, clusters need to be distinct from each other [82]. In our case, clustering (clustering algorithm) is based on the core idea of the fields’ centroids being gathered into clusters based on distances. At different distances, different clusters will form. At the center of the cluster is a field centroid, representing the depot location. The number of depots is determined by the number of clusters formed. Unlike the previous method where road network data are converted to fields data, this approach presents the other way around. Field centroids data are converted to the road network, also using the Euclidian distance, to ensure all fields are accessible via road network (Figure 4, left. The red cross is the closest intersection to the depot candidate).
## 2.2.3. Integrated Site Suitability Analysis
This step integrates the scores obtained from SSI (see Section 2.2.1) and distances (see Section 2.2.2) computed earlier. These scores are averaged out, with equal weighting values. The result is an updated/integrated SSI value taking into account these 2 factors. The idea is to prioritize fields that are closer to depots that also have high SSI values.
## 2.3. Study Area
The area of interest is located in the Piedmont region, more specifically in South Carolina (Figure 5). South central/eastern US zones have been shown to be more prominent for SRWC [27,62,83]. This area is composed of 28 counties, including Abbeville, Greenwood, Laurens, Greenville, Chester, Spartanburg, Fairfield, Darlington, Newberry, Kershaw, Union, Lancaster, Chesterfield, Edgefield, McCormick, Barnwell, Aiken, Lexington, Saluda, Bamberg, Calhoun, Anderson, Orangeburg, Pickens, Richland, Cherokee, Allendale, Marlboro, and York. Fields in all these counties were captured using the USDA crop data layer from 2018.
## 3. Results and Discussion
A total of 13,063 fields spanning the 28-county region were scored using the criteria listed above. Figure 6 shows the SSI score distribution. The most suitable fields (highest SSI values) were located in the central area, with a good portion in the northeastern part. The northern and southern parts were the least suitable. The histogram shows that most fields have SSI values below 0.5, although most fields fall between 0.3 and 0.6. Looking at the map, fields in this scoring interval seem to be well spread, indicating an overall good suitability in the area.
Figure 7 shows the distributions and median scores for individual criteria used to calculate the field SSI and presents a profile of the fields in our region of interest. NCCPI and AWS show the highest values, indicating the fields have, for the most part, high commodity productivity and good water storage capability. Good water availability would facilitate plant growth, which could ultimately enhance productivity. These characteristics show that the fields look profitable for woody crops.
As noted earlier, distances from fields (via closest road nodes) to depots were recorded using the road network. The notion of service area therefore becomes critical. In network analysis, service areas are areas surrounding selected objects, or base points, defining boundaries within which these selected objects can operate. In this study, a service area would thus indicate the delimited zone around a depot, accounting for all fields belonging to this zone, and model the movement of feedstock moving along the network in an organized and efficient manner. The objective is to reduce costs and limit the carbon footprint and is critical to ensure appropriate routing. A base point describes the location of the depot, whose accessibility is determined by a cut-off distance. We used a distance of 150 miles (240 km) as suggested by the Federal Motor Carrier Safety Administration (FMCSA) for a truck carrying agricultural products [84]. This represents a Euclidean distance specifying a radius within which the feasibility and profitability of feedstock supply are most likely. However, because we considered the road network (which was not built in a concentric fashion), the cut-off distance actually represented the maximum distance that can be traveled along the road network.
The integrated SSI (intg_SSI) value for each field was calculated using the equation we propose below:[4]intgSSI=∑mfmwm where intgSSI for field i is the updated site suitability index, fm is the fuzzy value of a factor m for field i, and wm is the weight of factor m. Table 2 details the factors and the data sources.
Figure 8 shows the depot locations for both K-means clustering and graph theory methods.
K-means clustering: The extent of the area studied was around 200 × 225 miles. After running the K-means algorithm, we determined two depots located in Laurens and Richland counties. Considering the service area radius, we submit that two depots should be sufficient and cost-effective to streamline the woody biomass supply system. Figure 8 (left) shows depot locations. The different colors specify the boundaries of the service area.
Graph theory: This approach analyzes the road network by determining network statistics and indicators. The clustering coefficient used here represents the extent to which a node’s neighborhood forms a complete graph [85]. It measures the degree to which nodes in a graph tend to cluster together, scaled to 0–1 interval. With several nodes indicating a value of 1, we chose the ones with the highest SSI scores of corresponding field centroids to those nodes. This was to prioritize areas with higher suitability. Given the area limits and service area radius, three depots were found, located in Chesterfield, Laurens, and Orangeburg counties. Figure 8 (right) shows depot locations, with here, too, different colors specifying the boundaries of the service area.
Figure 9 shows the distribution of normalized scores for distances covered from fields to depots. In both graphs, distribution peaks appear to be the same, with most field scores within the 0.5–0.6 range. However, the distributions show differences. The K-means method (left) shows more scores closer to 0 (median value is 0.52 vs. 0.54 for the graph theory method) and fewer scores close to 1. This indicates that fields are generally farther from depots than the ones in the graph theory method (mean values of 0.52 vs. 0.54). Distances traveled are higher, illustrated by the right tail showing higher scores for the graph theory method (right). Figure 10 shows the region maps with SSI integrated using cluster analysis (left) and graph theory (right), respectively. Differences in the scores can be observed. The graph theory method seems to show more suitability around the depot locations. Because it presents more depots, transportation suitability scores are higher (as seen in Figure 9). Generally, the southwestern and northwestern parts are the least suitable, in both methods, illustrated by lower scores. The central parts show good suitability.
These differences might be explained by the number of depots and the difference in locations. Road networks are certainly different from one location to another. The changing distances from different points lead to different scores. The approaches in this study thus illustrate different supply chain configurations. Depending on where depots are located, and what the road network looks like in the area, a given configuration may be more cost-effective than the other. The graph theory approach presents a cumulative distance (from fields to depots in the respective service area) of 801,031,476 miles, while the K-means presents 1,037,606,072 miles, which is about $30\%$ more miles traveled than in the graph method. This finding suggests that a more decentralized system may be more cost-effective, with depots located closer to more fields, if tying distance driven to transportation costs. This is if only transportation costs are considered, as it would be reasonable to assume that depot construction and running costs might be higher for three depots than just two. Those costs were not considered here. Also not considered were collection and delivery costs from depots to a biorefinery. There are also implications on gas emissions. The more driving there is, the more CO2 emission there is. A more centrally oriented supply chain design may thus provide a less sustainable option.
Biomass supply systems were modeled as a way to understand how feedstock can be produced and cost-effectively moved in the network for processing. Decentralized networks, which are geographically more spread out, tend to provide better reliability, as they offer more processing locations. With depots spread across multiple physical locations, the supply chain gains in resilience and resource sharing opportunities. From a redundancy perspective, more centralized architectures can be deficient in the event of a disruption. As these are highly dependent on network connectivity, the supply system becomes rapidly vulnerable if depots lose connectivity. A sustainable supply chain would require a more resilient architecture, with more depots and a reduced miles driven volume. The way forward lies in a more decentralized supply chain design and a more progressive holistic approach tapping into land suitability for natural resources to create sustainable systems. In using a clustering algorithm and coefficient, this study captures the topology of road networks and helps highlight the role of their various topological features in depot siting. By using two different approaches, we provide results that are more robust and compelling than with one approach. This is the main benefit of using multiple methods, as it offers a broader outlook on the issue at hand. Both methods are pertinent in the study of facility location problems (FLP), concerned with the placement of facilities to minimize transportation costs. However, it is important to note their differences and how these impact the analysis.
While the clustering coefficient (graph theory) relies on the actual road network connectedness and degree of accessibility, K-means uses the field centroid, independent of the road network, to form clusters [86]. The K-means method is dependent on initial values, which are a random choice of cluster centers. The rescaling of datasets may potentially change results and provide different conclusions. The selection criteria used in graph theory were also a defining factor in obtaining these results, as it uses the highest SSI value for fields to prioritize depots. Since SSI calculation uses a weighting approach with equal importance for all criteria, a different weighting approach could potentially provide a different ranking and ultimately different depot locations. In that sense, the results obtained are not to be interpreted as one method being necessarily better than the other, rather, they should provide insights on biomass supply chain designs.
By prioritizing suitability over optimality for depot selection, this study integrates ecological indicators and transportation network analysis and presents a scoring mechanism to assess the appropriateness of processing locations. Ecological indicators are measures of key ecosystem properties, providing information on pressures on the environment, environmental conditions, and societal responses [87]. As such, these are critical for implementing a suitability analysis. Performing a transportation network analysis provides information on network features and helps define movements and flows of feedstock. Integrating these analyses offers a more complete suitability assessment for biomass production and sustainability implications. The use of scoring for both analyses to synthesize underlying complexity help in generating and effectively communicating information about the biomass supply chain. In contrast to the more familiar optimization approach, the suitability approach is not intended to find the best or most optimal depot location, but to identify potential locations with respect to preprocessing. This involves the quantification of the subjective importance placed on various factors, opening a wide range of possibilities necessary for long-term planning.
The transportation of products to pre-processing facilities or depots in supply chain analysis is recognized as one of the key components [88]. Facility location optimization substantially reduces transportation costs in the supply chain [89]. Different siting contributes to create different designs/architectures, with efficiency implications. As our results show, different designs resulting from depots differently distributed in the area of interest present different mileages driven, each of which incurs different costs and gas emissions. In this study, we show how ecological indicators of agricultural fields can and need to be integrated into the supply chain analysis. With the ILM approach, SRWC may be planted in environmentally adequate portions to protect soil resources by producing environmental and economic benefits, thereby improving the biomass supply–demand dynamics and making more feedstock available. This approach results in a landscape mosaic growing both conventional agricultural residues and dedicated energy crops at the same time, helping to implement a more sustainable biomass production scheme. Soil features representations afford, thus, a more realistic representation of the biomass supply chain, incorporating the conditions enabling cultivation and exploring the role of environmental factors via ecological indicators. This work makes progress toward a more advanced supply chain analysis, demonstrating that the sustainability of the biomass supply chain can be evaluated using suitability, measured by its capability to [1] develop a durable feedstock availability scheme and [2] offer a reliable transportation network enabling the conversion of raw biomass into a larger scale commodity feedstock.
This study can certainly be extended. Several factors including depot sizing and capacity, as well as feedstock blend components, can affect system costs [90]. Depot capacity, for instance, may be factored in to not overburden depot candidates and ultimately create an unfairly distributed network. Biomass quality is another factor having implications for the planning and design of the supply chain. Biomass quality, such as ash and moisture, impacts the overall cost and topology of the supply chain [91]. It would also be interesting to consider climate and its effects, as climate change is expected to impact soil properties and ecosystems [92]. The main limitation of this approach is in regard to the weighting, which we assume is equal across factors. This assumption does not account for the different needs of other crops on polyculture landscapes and also farmer preferences. A different weighting would provide different suitability scores.
## 4. Conclusions
The sustainability of the bioenergy supply in the US rests on the development of advanced approaches meant to inform renewable energy policy. Using geospatial data and heuristics, we investigated biomass supply chain design, leveraging soil characteristics relevant to SRWC and road networks. An integrated framework that models biomass production suitability and transportation throughout a biomass supply chain is presented. Biomass production was estimated based on ecological indicators facilitating growth, including land cover/crop rotation, slope, soil productivity, soil texture, and water availability. With successful ILM, SRWC may be planted in environmentally adequate portions to protect soil resources and improve the biomass supply–demand dynamics, making more feedstock available in the future.
This work proposes a different and innovative view of the biomass supply chain, proposing suitability rather than optimality. SSI scores were determined by conducting a site suitability analysis consisting of identifying, comparing, and ranking candidate fields more appropriate for SRWC. Transportation scores were obtained based on the location of depots, using two methods: graph theory and a clustering algorithm. Graph theory, via the clustering coefficient, helps to determine dense areas in the network and pinpoint the most appropriate depot location. The clustering algorithm, via K-means, helps discover grouping in clusters, classifying each field centroid into a specific cluster. The center of each cluster represents the location of the depot. A final score is then computed, using the average of SSI scores and transportation scores, estimating the overall supply chain suitability for SRWC.
While the K-means method found two depots, the graph theory method found three depots, resulting in a total of 1,037,606,072 and 801,031,476 miles driven from fields to depots, respectively. The distance covered by the former is about 1.3 times the distance covered by the latter, suggesting more decentralized systems to be more favorable to a sustainable supply chain, with the depots located closer to the fields. More decentralized-depot-based supply chain designs show more economical and environmental benefits in our region of interest. With depots spread across multiple physical locations, the supply chain system tends to be more robust in the event of disruptions.
The way forward for biomass supply chain analysis consists of a more holistic approach tapping into land suitability for natural resources to create sustainable systems. This work makes progress toward a more advanced supply chain analysis, demonstrating the use of suitability for biomass supply chain sustainability analysis. The approach presented lays out the foundations for discussion regarding scale-up potential and long-term viability of advanced bioenergy systems.
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|
---
title: A Fluorinated Polyimide Based Nano Silver Paste with High Thermal Resistance
and Outstanding Thixotropic Performance
authors:
- Zhenhe Wang
- Dong Wang
- Chunbo Zhang
- Wei Chen
- Qingjie Meng
- Hang Yuan
- Shiyong Yang
journal: Polymers
year: 2023
pmcid: PMC10007458
doi: 10.3390/polym15051150
license: CC BY 4.0
---
# A Fluorinated Polyimide Based Nano Silver Paste with High Thermal Resistance and Outstanding Thixotropic Performance
## Abstract
Because of high conductivity, acceptable cost and good screen-printing process performance, silver pastes have been extensively used for making flexible electronics. However, there are few reported articles focusing on high heat resistance solidified silver pastes and their rheological properties. In this paper, a fluorinated polyamic acids (FPAA) is synthesized by polymerization of the 4,4′-(hexafluoroisopropylidene) diphthalic anhydride and 3,4′-diaminodiphenylether as monomers in the diethylene glycol monobutyl. The nano silver pastes are prepared by mixing the obtained FPAA resin with nano silver powder. The agglomerated particles caused by nano silver powder are divided and the dispersion of nano silver pastes are improved by three-roll grinding process with low roll gaps. The obtained nano silver pastes possess excellent thermal resistance with $5\%$ weight loss temperature higher than 500 °C. The volume resistivity of cured nano silver paste achieves 4.52 × 10−7 Ω·m, when the silver content is $83\%$ and the curing temperature is 300 °C. Additionally, the nano silver pastes have high thixotropic performance, which contributes to fabricate the fine pattern with high resolution. Finally, the conductive pattern with high resolution is prepared by printing silver nano pastes onto PI (Kapton-H) film. The excellent comprehensive properties, including good electrical conductivity, outstanding heat resistance and high thixotropy, make it a potential application in flexible electronics manufacturing, especially in high-temperature fields.
## 1. Introduction
Owning to good adaptability to additive manufacturing process, flexibility, low environmental impact and low cost, printed electronics technologies have been used to fabricate flexible electronic devices such as actuators, sensors, and solar cell and displays during the past few decades [1,2,3,4]. As an indispensable component, conductive pastes play a key role in determining the functionality and reliability of electronic devices. At present, a variety of conductive pastes based on different conductive fillers have been developed, including conductive polymer pastes, copper pastes, silver pastes, gold pastes, carbon nanotubes pastes and graphene pastes [5,6,7,8,9,10]. Compared with other conductive pastes, silver pastes with excellent conductivity and remarkable antioxidant properties balance the contradiction between high performance and low cost, and attract extensive attention in academic and applied fields [11,12,13].
Silver pastes are commonly composed of silver powder, adhesive and diluent, supplemented by a small amount of the third component acted as dispersant, leveling agent, defoamer, moisturizer, thixotropic agent, etc. [ 14,15,16]. Depending on different types of adhesives, silver pastes can be divided into high temperature sintered silver pastes and low temperature solidified silver pastes. Despite showing exceptional heat resistance and electrical conductivity, sintered silver pastes, usually taking glass powder as the adhesive, possess a high forming temperature (more than 500 °C) [17,18,19], which exceeds the decomposition temperature of the polymer substrate and limits its application in flexible electronics. In contract, solidified silver pastes show a relatively low and wide forming temperature ranging from room temperature to 400 °C according to the different adhesives. The low forming temperature, adjustable electrical conductivity and good flexibility make solidified silver pastes an ideal choice for fabricating flexible electronic devices by multiple printing technologies. Liang developed water-based AgNW inks and fabricated the thin-film transistors (TFT) by fully screen printing. The printed TFT had a yield of $91.7\%$ and an average mobility of 33.8 ± 3.7 cm2V−1s−1 [20]. Zhu developed a maskless and templateless fabricating approach for high-performance flexible transparent electrodes with nano silver pastes by combining electric-field-driven microscale 3D printing and hybrid hot-embossing [21]. The fabricated flexible, transparent electrode exhibits excellent photoelectric properties, remarkable mechanical stability, and environmental adaptability.
At present, polymers used as adhesive for preparing silver pastes mainly include epoxy resin, polyacrylic resin, polyurethane, polymethacrylate, etc. [ 22,23,24,25]. Due to the low heat endurance of those polymers, the operating temperature of silver pastes derived from those polymer adhesives is usually lower than 200 °C, which cannot meet the demand of some flexible electronics for high temperature manufacturing process and high work temperature. Therefore, it is very urgent to develop polymer-based silver pastes with good heat resistance and suitable forming temperature. As a polymer with the best comprehensive performance, polyimide is widely used in the field of electronic packaging. It is considered as an ideal alternative material for preparing high-temperature silver pastes. Nguyen prepared an Ag nanowire–polyimide composite by the solution blending method and expounded the charge transport mechanism in thermoplastic thermostable materials [26]. Li prepared an Ag nanowires/PI composite and used it to fabricate the highly sensitive flexible pressure sensor [27]. Tung-Lin prepared the conductive silver/photosensitive polyimide (PSPI) nanocomposites by homogeneously mixing the PSPI precursor with silver nanoflakes [28]. The resultant nanocomposites possess excellent heat resistance and high conductivity.
As mentioned above, polyimide/silver pastes possess superior over-all performance, especially high thermal resistance. However, the reported polyimides used for preparing silver pastes usually have poor solubility in organic solvent; even the precursor polyamide acid (PAA) can only be dissolved in finite strongly polar aprotic solvents including N,N-dimethylacetamide, N,N-dimethylformamide, 1-methyl-2-pyrrolidinone, m-cresol. Those solvents are not suitable for the preparation of silver pastes because of the following disadvantages: hazard to human body, corrosivity to common polymer substrates and mask, lack of volatility. Moreover, the electrical conductivity of the reported polyimide/silver pastes is relatively low. In addition, there are almost no reports about the rheological properties of polyimide/silver pastes, which have a key effect on the printing accuracy of silver pastes.
In this article, a fluorinated PAAs is synthesized using 4,4′-(hexafluoroisopropylidene) diphthalic anhydride (6FDA) and 3,4′-diaminodiphenylether (3,4′-ODA) as monomer in the diethylene glycol monobutyl ether (DB) solvent. Furthermore, the prepared PAA resin is mixed with silver powder and ground to obtain silver pastes. The influences of the composition of silver pastes on its rheological and conductive properties are studied in detail. Consequently, the obtained silver pastes show an excellent heat resistance, electrical conductivity and outstanding thixotropy, which is very useful for fabricating the fine circuit and possess a great potential in high temperature resistant flexible electronic field.
## 2.1. Materials
Nano silver powder (average length of 100 nm) and flake silver powder (average length of 5 μm) were purchased from SINO-PLATINUM METALS CO., LTD and used without pre-treatment. The microscopic morphology of the two silver powders mentioned above is shown in Figure 1. 6FDA, 3,4′-ODA and 4-phenylethynyl phthalic anhydride (PEPA) were supplied by Changzhou Sunlight Pharmaceutical Co., Ltd. 3,4′-ODA was used directly. 6FDA and PEPA should be dried at 160 °C for 12 h prior to use. The defoaming agent (BYK-066N) and flatting agent (BYK-333) were obtained from BYK (China). Diethylene glycol monobutyl ether (DB) was purchased from Aldrich and used without further purification.
## 2.2. Synthesis of Fluorinated Polyamide Acid (FPAA) Resin
FPAA resin was prepared from 6FDA, 3,4′-ODA and PEPA in DB solvent by polycondensation reaction as shown in Scheme 1, in which the PEPA was used as a reactive capping agent. Specifically, the synthesis of FPAA resin with solid content of 40 wt% was described. In a 250 mL of three-necked flask equipped with a mechanical stirrer, 3,4′-ODA (5.2279 g, 12.7351 mmol) was dissolved in DB (46.19 mL), 6FDA (2.5000 g, 11.4616 mmol) was then added with mechanical stirring to the solution in one portion. The polycondensation was proceeded at room temperature for 3 h to produce a uniform viscosity resin, in which a stoichiometric amount of PEPA (3.0556 g, 14.0086 mmol) acting as reactive capping agent was added gradually. The mixture was stirred mechanically for 8 h at room temperature to yield a viscous FPAA resin solution, which was sealed and stored at 5 °C.
## 2.3. Preparation of Nano Silver Pastes
Firstly, the prepared FPAA resin and nano-silver powder were mixed by intensively mechanical stirring in the beaker. Secondly, the BYK-333 and BYK-066N were added in obtained slurry acting as flatting agent and defoaming agent respectively. Thirdly, the mixture was finely ground using a three-roller grinder to pulverize the agglomerated particles formed by the nanosilver powder and further facilitate the uniform mixing of each component, to fully obtain highly dispersed nano silver pastes noted as FPI-NSAg-X, where X is $70\%$, $75\%$, $80\%$, $83\%$, $85\%$ representing the solid percentage content of silver powder in the cured paste. As control group, the silver paste based on flake silver powder as conductive filler was prepared in the above manner and not as FPI-FAg-$83\%$. The detailed compositions of different silver pastes are listed in Table 1.
## 2.4. Measurements
Thermogravimetric analysis (TGA) was performed on TA Q50 thermal analysis system from 40 °C to 760 °C at a heating rate of 20 °C/min under nitrogen atmosphere.
The electrical conductivity of cured silver paste was measured by four-point probe tester (ST2258C, Suzhou Jingge Electronic Co., Ltd., Suzhou, China). The preparation method of the sample to be tested was as follows: the conductive patters were fabricated on the polyimide films (Kapton-H, Wilmington, DE, USA) through screen printing technology and were further treated at different high temperature ranging from 200 °C to 350 °C. The dimensions of the conductive patters were 2 cm × 2 cm.
Rheological behaviors of the formulated silver pastes were carried out on a MCR 92 rheometer (Anton Parr, Graz, Austria) equipped with stainless steel parallel plates with a diameter of 50 mm and gap of 1.0 mm. All rheological properties tests were performed at 20 °C and silver paste need to stand still for 5 min before each test. The shear viscosity tests were conducted with shear rates of 0.01–1000 s−1 at a frequency of 1 Hz. The three interval thixotropic test (3ITT) was performed to simulate the screen-printing process with specific test parameters of 0.1 s−1 shear rate for 200 s, 100 s−1 for 80 s, and 0.1 s−1 for 300 s.
Microstructure and the morphology of cured silver paste were examined using Sigma 300 field-emission scanning electron microscope (Zeiss, Oberkochen, Germany).
Resolution of printed conductive pattern and the dispersion of silver pastes were characterized by measuring dimension and morphology of pattern using a DM4 B light microscope (Leica, Wetzlar, Germany).
Adhesion of silver pastes to PI film (Kapton-H) was evaluated by measuring the change of the electrical conductivity of the cured pattern before and after the pulling off experiment.
## 3.1. Dispersion Homogeneity
As multiphase composite system, the dispersion of nano silver powder in polymer binder has a crucial effect on the conductivity, rheology and uniformity of silver pastes. Especially for nano silver pastes, the agglomeration of silver nanoparticles is a very common phenomenon, which is very detrimental to the dispersion of silver powder. In order to break the agglomerated silver nanoparticles and improve the dispersion homogeneity, the mixed nano silver paste was grinded with different gaps by a three-roller grinder. The dispersion of nano silver paste was characterized by high resolution light microscope. Figure 2 show the optical micrograph of nano silver paste (FPI-NSAg-$83\%$) treated by three-roll grinder with different roll gaps. It can be found that the grind method can effectively break the agglomeration of nanoparticles and improve the dispersion uniformity. The number and size of nano agglomerations decrease as roll gaps reducing. When the roll gap is reduced to 5 μm, there is no obvious agglomeration in the field of view. Furthermore, the dispersion uniformity of nano silver paste remains stable within 3 months after static storage. There are no agglomerated particles were found, as shown in Figure 3. It can be explained that the surface of the nano silver powder is coated with polyamic acid resin, which reduces the surface energy of the nano silver powder, thus hindering the occurrence of re-agglomeration.
## 3.2. Electrical Conductivity
To evaluate the electrical conductivity of obtained nano silver paste, the conductive patters with dimension of 2 cm × 2 cm were fabricated on the polyimide films (Kapton-H) through screen printing technology and were further treated at different high temperature ranging from 200 °C to 350 °C. Then, square resistance (Rs) and thickness of conductive patters were measured using four probe tester and steps instrument respectively. The volume resistivity of silver paste was calculated according to Equation [1], where ρv is volume resistivity, W is the thickness of the corresponding conductive patterns. [ 1]ρv=Rs×W Figure 4 depicts the Rs of cured patterns with dimension of 2 cm × 2 cm, which prepared from FPI-NSAg-$83\%$ silver paste with different curing temperature. Because the imidization temperature of PAA resin usually exceeds 200 °C, the curing temperature of obtained silver paste is higher than the conventional conductive pastes such as epoxy conductive pastes, acrylate conductive pastes, polyurethane conductive pastes, etc. As shown in Figure 4, with the increase of curing temperature, the Rs of the conductive patterns decreases significantly, especially when the curing temperature increases to 300 °C, the Rs decreases to 22.6 mΩ/square. The thickness of pattern is 20 μm and the calculated volume resistivity reaches 4.52 × 10−7 Ω·m. When the curing temperature is further increased, the Rs decreases slowly. As we all known, the diluent and the water produced from imidization reaction will volatilize from pastes as the curing temperature increase, so the mass percentage of silver powder in silver paste increase. Additionally, high curing temperature will promote the in-plane orientation and densification of polyimide molecular chains [29,30,31]. The two reasons mentioned above will reduce the distance between the silver powder and make the silver powder closely connected, which contribute to reducing the resistivity value of the conductive pattern. When the curing temperature reached 300 °C, the imidization reaction and volatilization of diluent in silver paste were completed, the mass percentage of silver powder increased to the highest. Further increasing the curing temperature can also promote the densification of conductive pattern. However, the contribution of this process to elevating the conductivity is negligible comparing the imidization reaction and volatilization of diluent before 300 °C. This speculation can be further confirmed by the microstructure of conductive pattern at different curing temperatures as showed in Figure 5. With the increase of curing temperature, the distance between silver powder particles decreases and the stacking density of silver powder increases. Additionally, when the curing temperature reaches above 300 °C, the nano silver powder partially melts. All of those will contribute to improve the electrical conductivity of the printed pattern at curing temperature of 300 °C.
In order to elucidate the effect of nano silver powder content on the conductivity of the silver paste, silver pastes with different content of nano silver powder were prepared as shown in Table 1. The conductive patterns were printed on PI film and cured at 300 °C for 60 min. The measured Rs and calculated ρv is shown in Figure 6. As expected, the Rs and calculated ρv gradually descend as the nano silver content increases. When the content of silver nanoparticles increases to $83\%$, the Rs of the silver paste decreases to 22.6 mΩ/square and remain basically stable with further increase of silver content. It is well known that the conductive network of printed pattern becomes denser as the silver content increases. When the silver content reaches $83\%$, the density of conductive network in the printed pattern is saturated and the conductivity will not be further improved as the silver content further increase. This explanation can be further confirmed by electron microscopic photographs of conductive patterns as shown in Figure 7. The content of silver and fluorine element in different cured silver paste was measured through energy spectrum and the result was depicted in Figure 7f. As expected, the fluorine content decreases with the increase of silver content. Figure 8 compares the conductivity of silver paste based on flake powder and nano silver powder respectively. It can be found that the silver pastes show the similar Rs regardless of flake silver powder and nano silver powder, when the mass fraction of silver powder is fixed at $83\%$. This phenomenon can be explained that the volume of silver powder in the paste has reached saturation, and the dense conductive network has been formed. As is well known, the flake silver powder can provide a larger contact area. However, the nano silver powder has a higher specific surface area, which leads to a higher volume fraction of silver than flake silver powder-based paste at the same mass fraction. Under the combined action of the above two factors, these two silver pastes have similar conductivity.
## 3.3. Rheological Properties
Rheological properties of silver pastes play a key role in determining the performance of the screen-printing process. Silver pastes with accommodative rheological properties will possess excellent screen printable properties, which can contribute to print thinner lines with higher thickness and smoother edges. Moreover, appropriate rheological properties can be conducive to inhibit occurrence of defects and bubbles in the printing process, thereby improving the conductivity of the printed pattern. Two rheological test modes were used to investigate the rheological properties of silver pastes on a parallel plate rheometer.
Figure 9a shows the viscosity of nano silver paste with different silver content at continuously shear rates from 0.01 s−1 to 1000 s−1. Obviously, the viscosity of all silver pastes decreases gradually with the increase of shear rate, which is consistent with the typical rheological characteristics of pseudoplastic fluids. As the content of silver increases, the viscosity of silver pastes gradually increases, which is due to the reduction of free mobile phase. Figure 9a compares the viscosity shear rate curves of nano silver paste and flake silver paste. It is evident that the nano silver paste possesses a higher viscosity than the flake silver paste; since the nano silver has larger specific surface area, this leads to more resin adsorbed on the surface of nano silver powder. Subsequently, the number of free resin molecules remaining in silver paste is correspondingly reduced. In addition, the high surface energy of nano silver results in graduating of stronger interaction forces in silver paste. All these factors mentioned above will cause higher viscosity of FPI-NSAg paste than FPI-FAg paste. Figure 9b shows the optical micrograph of printed conductive lines with different silver paste. It can be found that printed lines originated from FPI-NSAg-$70\%$ and FPI-NSAg-$75\%$ show large width of 184 μm and 170 μm, respectively, which are far beyond the design width of 120 μm. This is because viscosities of FPI-NSAg-$70\%$ and FPI-NSAg-$75\%$ are too low, so that strong flow diffusion occurs after printing on the PI films. Accordingly, as the content of nano silver increases, the printing line width gradually decreases. However, the line printed using FPI-NSAg-$85\%$ shows zigzag edges, and the consistency of line width is reduced. This deterioration results from the poor leveling property and high viscosity of FPI-NSAg-$85\%$ caused by high silver content. Comparatively speaking, the printed lines from FPI-NSAg-$80\%$ and FPI-NSAg-$83\%$ show high resolution and smooth edges, the width of printed line is near the design width of 120 μm.
In order to clarify the flow characteristics of silver paste in different stages of the screen-printing process, a three interval thixotropic test (3ITT) was performed with specific test parameters of 0.1 s−1 shear rate for 200 s, 100 s−1 for 80 s, and 0.1 s−1 for 300 s. This test can approximately reflect the rheological changes of silver paste at typical three stages of screen-printing including loading of paste, printing of paste and leaving from silk screen. As shown in Figure 10a, it can be concluded that the viscosity of all silver pastes shows a change of first decreasing and then increasing during a complete 3ITT test process, showing obvious thixotropic characteristics. Moreover, the viscosity of nano paste is higher than the flake silver paste in the first and third stage, which is agreement with the Figure 9a. Generally speaking, the recovery ratio of viscosity in the third stage relates to the first stage, which has a great influence on the size and uniformity of the printing lines, and is usually used to characterize the elasticity of paste [10,20]. However, the prepared silver pastes were partially thrown out during the second stage because of the high shear rate. This can be verified with the decrease of viscosity of paste during the second stage as shown in Figure 10a. It causes a different amount of silver paste between the gaps in the first stage and third stage, so the recovery ratio cannot completely reflect the elasticity. According to the reported literature [10], the viscosity ratio of the third stage (η3rd) to the end of the second stage (η280s) is used to delineate the elasticity of silver paste. The result is shown in Figure 10b. The η3rd/η280s of FPI-NSAg silver paste is higher than that of FPI-FAg silver paste, which indicates nano silver powder is contributes more to enhancing the strength and elasticity of silver paste than flake silver powders. Therefore, the nano silver paste shows an enhanced resistance to strain. Additionally, the prepared nano silver pastes possess outstanding thixotropic characteristics, which can be proved by rapid increase of viscosity after the end of second stage. The good thixotropic property is conducive to improve the printing resolution and obtain printing patterns with high thickness and fine width. Figure 10c shows the micrograph of the conductive microstructure of FPI-NSAg-$83\%$ paste silk-screened on PI film. The printed minimum line width and distance are close to 120 μm, which is in accordance with the design size. Additionally, there is no break and dimensional deviation in the entire printed conductive microstructure array. The high dimensional accuracy and smooth edges indicate that the obtained FPI-NSAg-$83\%$ paste has good printing adaptability and high printing resolution.
## 3.4. Thermal Endurance
Figure 11 depicts TGAs curve of nano silver paste (FPI-SAg-$83\%$) under nitrogen atmosphere. The thermal weight loss before 300 °C is due to the volatilization of diluent in the paste and the release of water molecule produced by the imidization reaction. This phenomenon further explained the above viewpoint that the appropriate curing time of nano silver paste is 300 °C to obtaining high conductivity. Figure 11 also describes the TGA curve of the cured silver paste (FPI-SAg-$83\%$) under nitrogen atmosphere. It indicates that the obtained nano silver paste shows an outstanding thermal resistance with $5\%$ weight loss temperature higher than 500 °C, which results from the excellent thermal stability of fluorinated polyimide.
## 3.5. Mechanical Properties of Printing Pattern
The conductive patterns with dimension of 2 cm × 2 cm were fabricated by screen printing silver paste (FPI-NSAg-$83\%$) on the polyimide films (Kapton-H) through screen printing technology and were further treated at 300 °C for 60 min. In order to evaluate the adhesive strength of conductive patterns and flexible PI substrate, an adhesive tape (3M 600#) was attached to the surface of conductive patterns then peeled quickly at nearly 90°. The Rs changes after above test were measured using four probe testers, which can reflect the adhesion strength of conductive patterns on PI substrate. Figure 12 shows the Rs of conductive pattern after different times peeling off test. It can be found that there is no evident reduction of Rs with the increase of pull-out test times, which means that the conductive pattern has outstanding adhesion strength on the PI film substrate. This mainly due to the good interface compatibility and strong intermolecular force between fluorinated PAA resin and PI substrate.
## 4. Conclusions
In summary, we successfully synthesized fluorinated polyamic acids (FPAA) with excellent solubility in non-amide solvent. The nano silver paste was prepared by mixing the obtained FPAA resin with nano silver powder. The dispersion of nano silver paste was improved by three-roll grinding process with a gap of 5 μm. The obtained nano silver paste possesses an excellent thermal resistance with $5\%$ weight loss temperature higher than 500 °C. The volume resistivity of cured nano silver paste achieves 4.52 × 10−7 Ω·m, when the silver content is $83\%$ and the curing temperature is 300 °C. Additionally, the nano silver paste has a thixotropic performance. The printed lines from FPI-NSAg-$80\%$ and FPI-NSAg-$83\%$ show high resolution and smooth edges and the width of the printed line is near the design width of 120 μm.
## Figures, Scheme and Table
**Figure 1:** *SEM photographs of nano silver powder (a) and flake silver powder (b).* **Scheme 1:** *The synthetic route to FPAA resins and FPI.* **Figure 2:** *The optical micrograph of nano silver pastes grinded with different roll gaps. (a) unground, (b) ground with 80 μm, (c) ground with 40 μm, (d) ground with20 μm, (e) ground with 10 μm, (f) ground with 5 μm.* **Figure 3:** *The optical micrograph of nano silver pastes grinded with a roll gap of 5 μm before (a) and after (b) static storage 3 months.* **Figure 4:** *The Rs of cured silver pastes with different curing temperature.* **Figure 5:** *The SEM photographs of conductive pattern at different curing temperatures. (a) 200 °C, (b) 250 °C, (c) 300 °C, (d) 320 °C, (e) 350 °C.* **Figure 6:** *The Rs and ρv of cured silver pastes with different silver content.* **Figure 7:** *The SEM photography of cured silver pastes with different silver content. (a) $70\%$, (b) $75\%$, (c) $80\%$, (d) $83\%$, (e) $85\%$, (f) The content of silver element and fluorine element in different cured silver paste by EDS analyses.* **Figure 8:** *The Rs of cured nano silver paste and flake silver paste.* **Figure 9:** *(a) The viscosities of nano silver pastes at continuously shear rates. (b) The optical micrograph of printed conductive lines.* **Figure 10:** *(a) The 3ITT curves of silver pastes. (b) The viscosity ratio of η3rd to η280s of silver pastes. (c) The micrograph of the conductive microstructure of FPI-NSAg-$83\%$ paste silk-screened on PI film.* **Figure 11:** *TGAs curves of nano silver paste (FPI-SAg-$83\%$) and cured silver paste under nitrogen atmospheres.* **Figure 12:** *Rs of conductive pattern after different times peeling off test.* TABLE_PLACEHOLDER:Table 1
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---
title: Colorimetric Determination of Glucose in Sweat Using an Alginate-Based Biosystem
authors:
- Sandra Garcia-Rey
- Eva Gil-Hernandez
- Lourdes Basabe-Desmonts
- Fernando Benito-Lopez
journal: Polymers
year: 2023
pmcid: PMC10007516
doi: 10.3390/polym15051218
license: CC BY 4.0
---
# Colorimetric Determination of Glucose in Sweat Using an Alginate-Based Biosystem
## Abstract
Glucose is an analyte of great importance, both in the clinical and sports fields. Since blood is the gold standard biofluid used for the analytical determination of glucose, there is high interest in finding alternative non-invasive biofluids, such as sweat, for its determination. In this research, we present an alginate-based bead-like biosystem integrated with an enzymatic assay for the determination of glucose in sweat. The system was calibrated and verified in artificial sweat, and a linear calibration range was obtained for glucose of 10–1000 µM. The colorimetric determination was investigated, and the analysis was carried out both in the black and white and in the Red:Green:Blue color code. A limit of detection and quantification of 3.8 µM and 12.7 µM, respectively, were obtained for glucose determination. The biosystem was also applied with real sweat, using a prototype of a microfluidic device platform as a proof of concept. This research demonstrated the potential of alginate hydrogels as scaffolds for the fabrication of biosystems and their possible integration in microfluidic devices. These results are intended to bring awareness of sweat as a complementary tool for standard analytical diagnosis.
## 1. Introduction
Glucose is the major energy source for cells, and its levels are regulated in blood by homeostasis [1]. Therefore, glucose monitoring is of high importance in order to detect a great variety of disease conditions, such as hypoglycemia and diabetes [2]. However, blood is still the gold standard biofluid for clinical diagnosis, including the determination of sugar-related alterations and diseases. Due to the invasiveness involved, research of less invasive biofluids, such as tears, saliva, sweat and urine, has been recently prioritized [3,4].
Among these biofluids, sweat has taken the lead due to its high potential. Since sweat is generated by every individual and is easily accessible, sweat arises as a potential alternative diagnostic tool for blood analysis for glucose determinations. Despite its clinical applicability, glucose sweat determination also provides a useful tool in the sports field, since it can be used to prevent health issues that may arise during exercise, as well as to improve the performance of athletes. Glucose levels in sweat are established to be between 10 µM–1000 µM, with the physiological range being 60 µM–110 µM [5,6].
Different materials have been recently developed for glucose sensing, which comprise a varying range of sensing platforms that go from materials with enzymatic activity to nanofibers. In fact, Yu et al. immobilized a fluorescent carbon quantum dots film with glucose oxidase and a cellulose acetate complex into the tip of an optical fiber, and they were able to detect glucose of 0.01–0.1 µM, obtaining a limit of detection of 0.026 µM [7]. Another different approach for glucose sensing developed by Wiorek et al. was based on the fabrication of an epidermal patch for the determination of glucose in sweat, and they were able to detect glucose in a linear calibration range of 10–200 µM [8]. The same year, Cao et al. developed a paper-based microfluidic approach for the determination of glucose, which was integrated with an electrochemical biosensor based on reduced graphene oxide-tetraethylene pentaamine [9]. Following this approach, they were able to detect glucose in a linear range of 0.1–25 mM, with a detection limit of 25 µM.
Although electrochemical detection offers a higher sensitivity [10], colorimetric detection offers a simple, easy-to-read and cost-affordable approach for the determination of biomarkers, as it allows the performance of qualitative determinations by naked eye or the integration of optical detectors for quantitative measurement [11]. However, the real applicability of sweat glucose determination towards the sports field relies on the integration of these biosystems into wearable microfluidic platforms that allow the performance of the devices to be carried out in field. In this regard, the coupling of the biosensors with smartphones has become the main approach due to their computing power, portability, cost-effectiveness, ease of operation, large memory, adequate battery, powerful computation capability, large data storage, portable power supply and convenient user interface [12].
In this regard, several microfluidic platforms have been developed for the measurement of glucose with a colorimetric approach. For example, He et al. fabricated a thermo-responsive, textile/paper-based, microfluidic analysis system by combining biocompatible polyurethane, cotton fabric and a paper-based colorimetric sensor, which was coupled with a smartphone, for the quantitative determination of sweat glucose, obtaining a limit of detection of 13.49 µM [13]. Also coupled to a smartphone, Xiao et al. fabricated a PDMS device for the colorimetric detection of sweat glucose [14]. This platform allowed 5 parallel determinations in a single measurement, measuring sweat glucose in a linear range of 0.1–0.35 µM, with a limit of detection of 30 µM. Another common approach for sweat glucose determination is the development of multiplexed analysis, in which glucose is detected together with other relevant sweat biomarkers. In this regard, Choi et al. fabricated a multilayer microfluidic device coupled with a smartphone for the colorimetric determination of chloride, glucose and lactate across physiologically relevant ranges [15]. Moreover, this platform integrated capillary bursting valves in the microfluid layer for the sequential filling of the reservoirs, thus allowing sequential sampling of sweat.
Recently, alginate hydrogels have proven to be appropriate scaffolds for the detection of metabolites in sweat, with a high potential to be integrated into wearable microfluidic platforms since they are non-toxic, biocompatible, biodegradable and affordable materials. In this regard, in previous research, we developed a proof-of-concept alginate bead biosystem for the detection of lactate in sweat, obtaining a linear calibration range for lactate of 10–100 µM [16]. Lactate was detected at 13 min, and the biosystem was validated with real sweat samples. Moreover, we have also demonstrated the stability of an alginate-based biosystem integrated with an enzymatic reaction for the detection of glucose and lactate, in which the sensing capabilities of the material were maintained for at least 10 days [17]. Other approaches for glucose sensing involving alginate hydrogels include enzymatic immobilization in alginate-based microfibers [18], luminescent cupper nanoclusters in alginate [19] or electrodepositable alginate membranes for the amperometric determination of glucose [20].
Based on these results, we have developed a proof-of-concept alginate-based biosystem integrated with an enzymatic assay for the detection of glucose in sweat through a colorimetric approach. For the calibration of the biosystem, black and white (B/W) analysis and analysis in the red, green and blue color code (RGB) were investigated. Moreover, to demonstrate the potential of this biomaterial, a proof-of-concept poly(methyl methacrylate) (PMMA) device was fabricated for the verification of the alginate biosystem. This research aims at improving the available tools for the determination of biomarkers in sweat, reinforcing the relevance of this biofluid as a potential alternative for diagnostic analysis.
## 2.1. Artificial Sweat
Artificial sweat was prepared by dissolving NaCl 60 mM (≥$99.5\%$, Sigma-Aldrich, Madrid, Spain) and urea 60 mM ($99\%$, Sigma-Aldrich, Madrid, Spain) in distilled water [16]. Solutions of artificial sweat with D-(+)-glucose (≥$99.5\%$, Sigma-Aldrich, Madrid, Spain) 10, 20, 40, 50, 60, 80, 100, 150, 250, 500, 600, 750 and 1000 µM were made, and the final pH was adjusted to 6.5. The solutions were stored between 2–8 °C until use.
## 2.2. Fabrication of the Biosystem
The alginate beads were fabricated by mixing 5 µL of glucose oxidase (GOx) 0.8 mg mL−1 (Aspergillus niger, Sigma-Aldrich, Madrid, Spain), 5 µL of horseradish peroxidase (HRP) 0.04 mg mL−1 (Sigma Aldrich, Madrid, Spain) and 1.5 µL of tetramethylbenzidine (TMB) (Sigma Aldrich, Madrid, Spain) dissolved in dimethyl sulfoxide (DMSO, Sigma-Aldrich, Madrid, Spain) with 30 µL of sodium alginate (Sigma-Aldrich, Madrid, Spain) $1.5\%$ (w/v) in distilled water. TMB was prepared by dissolving 10.7 mg in 1 mL of DMSO.
For the formation of the beads, 25 µL of the mix were taken and dropped into a 400 mM CaCl2 solution (≥$93.0\%$ Sigma-Aldrich, Madrid, Spain). The beads were immediately formed. Afterwards, the newly formed beads were washed with distilled water for 3 min before being wiped.
## 2.3. Calibration and Verification of the Biosystem for Glucose Sensing in Plate
For the calibration of the biosystem, a 96-well white plate (Non-Treated Surface, Thermo Fisher Scientific, Madrid, Spain) was used. After placing one bead per well, 150 µL of the glucose solutions in artificial sweat were added to the alginate beads. Four different beads were measured for each glucose concentration that was tested ($$n = 4$$).
The experiments were recorded with a 64 MP camera (Sony IMX682 $\frac{1}{1.73}$”, f/1.89, PDAF, Tokyo, Japan) in a white chamber under the same light conditions. Images at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 16, 19, 25, 30, 35 and 40 min were subtracted from the video and were analyzed afterwards with the image processing program ImageJ (1.53t 24 August 2022, NIH), both in the grey scale and in the RGB color code. For each specific time, the same image was used for the four colorimetric analyses and was modified accordingly with ImageJ. For the grayscale analysis, the image was turned into an 8-bit image, and the black-and-white value (B/W value) of each bead was measured, where 0 stands for black and 255 for white. To make the interpretation easier, the measured values were subtracted from 255, so that the more color, the higher the value. For the RGB analysis, the red, green and blue values of each bead were measured and recalculated as before. It needs to be noted that RGB [0,0,0] stands for white, while RGB [255,255,255] stands for black. For the B/W, R, G and B analysis, the whole bead was measured, without including the surrounding media.
For the verification of the biosystem, which was also done in plate, unknown glucose concentrations were spiked in artificial sweat. Then, 150 µL of those solutions were added to individual alginate beads. After the colorimetric assay was performed and the colorimetric signal was measured, the obtained values were used to determine the glucose concentration of those solutions by using the calibration curves previously obtained. Three individual beads were fabricated and measured for each glucose concentration ($$n = 3$$). Moreover, the accuracy and the precision of the obtained values were discussed, attending to the standard deviation (SD) of the measured black and white (B/W) values, compared to the real glucose concentration. The entire process is summarized in Figure 1.
## 2.4. Alginate Biosystem for Glucose Sensing in the Device with Real Sweat
The biosystem was applied with real sweat in a microfluidic platform, which was fabricated by a lamination process. The different layers of the device were, from bottom to top, 229 µm-thickness PSA 8939 (ARcare® 8939, Adhesive Research, Limerick, Ireland), 0.175 mm-thickness PMMA (ME30-SH-000116, clear, Goodfellow, Microplanet Lab., Barcelona, Spain), 244 µm-thickness PSA 90880 (ARseal™ 90880, Adhesive Research, Limerick, Ireland), 4.0 mm-thickness PMMA (ME303040, clear, Goodfellow, Microplanet Lab., Barcelona, Spain) and PSA 90880. PSA layers were designed with CorelDRAW Graphics Suite X7 and cut using a Graphtec cutting Plotter CE6000-40 (CPS Cutter Printer Systems, Barcelona, Spain). PMMA layers were designed with CorelDRAW Graphics Suite X7 and cut with a CO2 Laser System (VLS2.30 Desktop Universal Laser System, VERSA Laser, Vienna, Austria).
Before closing the microfluidic platforms with the top PSA layer, alginate beads were placed in the reservoirs of individual devices. The sweat sample was loaded into a syringe, and a positive flow of 10 µL min−1 was applied, using a syringe pump (Harvard Apparatus Pump 11 Elite, Madrid, Spain) to drive the flow into the device. The syringe was connected to the device through transparent silicone capillary tubing (ID 1 mm, OD 3 mm, Fisher Scientific, Spain) and PMMA female Luer-Loks (ChipShop, Jena, Germany), which were placed on a circular-shaped PSA 90880 piece surrounding the inlet. Three different devices were used for the verification of the biosystem in the device ($$n = 3$$). After the colorimetric analysis of the images, the calibration curve was used for the determination of the glucose concentrations of the sample. This result was compared with a commercially available glucometer (Freestyle Freedom Lite, Abbot, Madrid, Spain), using Freestyle Lite test strips for glucose determination.
## 3.1. Characterization and Working Principle of Alginate Beads
Glucose detection was carried out by integrating an enzymatic assay consisting of GOx, HRP and TMB into an alginate scaffold. A total of 17 µL of the enzymatic mix were taken for the fabrication of the alginate biosystem. The resulting beads were spherical, transparent and had a diameter of 2.4 ± 0.1 mm ($$n = 10$$).
The working principle of this biosystem for glucose detection goes in line with our previous research, where we were able to detect lactate in sweat at 13 min using an alginate-based detection biosystem [16]. Likewise, when glucose is added to the alginate beads, it diffuses inside the biosystem due to the porosity of the alginate hydrogel. This phenomenon can be better understood taking into consideration the mesh size of the alginate biosystem, which has been previously reported to be 6–14 nm [21]. Once inside, the GOx catalyzes the oxidation of the glucose, yielding gluconic acid and H2O2. Then, this H2O2 is used by the HRP as the electron donor for the oxidation of the TMB, which is oxidized to a diimine, and it can undergo either a one- or two-electron oxidation. The first colored product, the resulting product of the one-electron oxidation, consists of a charge-transfer complex formed by the diamine, namely, its oxidized diimine product and the radical cation, both species existing in equilibrium. This product absorbs light at 370 nm and 652 nm, respectively, which provides the material with its characteristic blue color. Moreover, it can undergo a further two-electron oxidation, yielding a diamine, which absorbs visible light at 450 nm, generating an orange/yellow-colored product [22].
## 3.2. Colorimetric Analysis of the Biosystem
For the calibration of the biosystem, alginate beads integrated with the enzymatic mix were placed in individual wells of a 96-well white plate, and 150 µL of solution with glucose of 10, 20, 40, 60, 80, 100, 250, 500, 750 or 1000 µM in artificial sweat were added. A blank solution was used as a control, which consisted of artificial sweat without glucose. Afterwards, the biosystem was analyzed for 40 min, and the B/W and RGB values of the beads were measured using image analysis software (see Section 2.3).
Figure 2A shows the measured B/W values of the beads for glucose of 10–1000 µM for 40 min. Higher glucose concentrations yielded higher measured values, which was indicative of more TMB that was being oxidized due to a higher amount of glucose inside the beads; thus, the beads showed a higher-intensity blue color. Moreover, as the assay continued operating, more glucose continued entering the alginate beads, which led to a higher-intensity blue color of the beads for each glucose concentration, until the saturation of the system. Figure 2B shows real images of an alginate bead throughout 40 min, after 80 µM of glucose solution was added to artificial sweat. As can be observed, the color intensity started from the outer surface of the bead towards its center, which was due to the added sweat solution not covering the whole bead. The yellowish color surrounding the beads was an indication that part of the reaction was taking place outside the beads. Due to the porosity of the alginate scaffold, while glucose entered the bead, part of the components of the enzymatic mix diffused outside the biosystem, yielding an amount of glucose to be oxidized in the surrounding medium of the bead. However, this did not affect the colorimetric assay since only the spherical shape of the bead was considered to carry out the colorimetric analysis of the biosystem.
In order to evaluate the best colorimetric sensing approach for our biosystem, we also investigated the RGB values of the beads for glucose of 10–1000 µM over 40 min (see Section 2.4). As expected, the red analysis (Figure 3A) offered the least accurate results since the colorimetric change of the beads was not towards the red. In fact, the different glucose concentrations that were analyzed with this approach did not show an appropriate resolution among them, which led to similar red values for different glucose concentrations. Surprisingly, however, the green analysis (Figure 3B) showed the best results, while the blue analysis (Figure 3C) was also discarded. It can be assumed that, since it was the blue color intensity of the beads that could be observed by the naked eye to increase over time, the blue analysis would lead to a better colorimetric analysis. However, although the glucose concentrations were better resolved than with the red analysis, large error bars were obtained when the blue values were measured. A possible explanation was that the development of the reaction was not only yielding the first oxidation product of the TMB (blue), but also reaching the second oxidation state (orange/yellow) within the bead. This statement was demonstrated when the green analysis of the alginate biosystem was carried out. In fact, it was this approach that offered the best resolution among the different glucose concentrations, as well as the lowest error bars.
This could be explained due to the two oxidations that the TMB can undergo, since the first oxidation yields a blue-colored product, while the second oxidation yields a yellow-colored product, with both oxidation states being reversible [22]. If the TMB would have undergone just the first oxidation, thus yielding the blue colored product, the blue analysis would have been a more efficient analysis approach. Therefore, although we could only appreciate the blue color of the beads with the naked eye, the colorimetric analysis of the green values demonstrated that, in fact, a small fraction of the TMB was in the two-electron oxidation state. Consequently, the mix coloration, i.e., the blue-colored product of the TMB with a smaller fraction of the yellow-colored product, led to a more accurate analysis in the green channel.
## 3.3. Calibration and Verification of the Alginate Biosystem for Glucose Sensing in Plate
Since the B/W and G analysis offered the best resolution and the lowest errors between the tested glucose concentrations, we decided to proceed with these two approaches to evaluate the most appropriate one for the calibration of the biosystem. For both analyses, the detection time was stablished at 13 min since this was when the glucose concentrations can be discriminated among them for the first time. In fact, the calibration distribution followed the same trend for both detection approaches, as can be observed in Figure 4.
Figure 4A shows the calibration curve for the B/W analysis of the beads for glucose of 10–1000 µM at 13 min, which is described by Equation [1]. This data strengthens our previously reported results for lactate sensing using an alginate bead biosensor [16], reinforcing the potential of this biosensing material for analytical determinations. Since the glucose solutions tested covered a wide range of concentrations, the calibration curve was divided into two different calibrations curves for a better interpretation of the data, one for the lowest glucose concentrations and another one for the highest glucose concentrations. Following this approach, we obtained a linear calibration range for glucose of 10–100 µM at 13 min (Figure 4B), while a logarithmic calibration was obtained for glucose of 100–1000 µM (Figure 4C) due to the saturation of the system. Similarly, Figure 4D shows the calibration curve for the G analysis of the beads for glucose of 10–1000 µM at 13 min, which is described by Equation [2]. The calibration followed the same trend as for the B/W analysis, which was able to detect glucose of 10–100 µM in a linear range at 13 min, while glucose of 100–1000 µM could be detected in a logarithmic approach. [ 1]$y = 376.0$+13.4−376.01.0+(x502.4)0.4[2]$y = 270.2$+60.7−270.21.0+(x257.3)0.9 The limit of detection (LOD) and limit of quantification (LOQ) for the biosystem were also calculated using the equations LOD=3 SD/m and LOQ=10 SD/m, respectively, where SD is the standard deviation of the blank and m, the slope. An LOD and LOQ of 3.8 µM and 12.7 µM, respectively, were obtained for the B/W analysis and of 7.1 µM and 23.7 µM, respectively, for the G analysis. Therefore, although both the B/W and G colorimetric analyses offered a good approach for glucose determination, the B/W analysis provided a lower LOD and, thus, a better resolution of the biosystem.
In fact, similar LODs were achieved with this biosystem when compared to electrochemical approaches. For example, Oh et al. [ 23] fabricated a stretchable and skin-attachable electrochemical sensor for detecting glucose and pH in sweat, obtaining a limit of detection of 1.3 µM for glucose. Later, Shu et al. [ 24] developed a noninvasive, fiber-material-based, wearable electrochemical sensor to continuously monitor glucose and obtained a limit of detection of 3.28 µM for glucose. Therefore, the alginate biosystem developed here shows a good operational approach and sensitivity, as it is able to compete with previously reported results to be applied for glucose monitoring in sweat.
Verification of the biosystem for the B/W analysis was also carried out. Glucose solutions of 50, 150 and 600 µM in artificial sweat were prepared, and 150 µL were added to the beads. After 13 min, their B/W value was measured, and their real concentrations were calculated using the calibration curves described in Figure 4B,C, obtaining a concentration of 41 ± 4, 135 ± 26 and 584 ± 34 µM, respectively ($$n = 3$$). Comparing the measured concentrations with the real ones, accuracy and precision of at least $83\%$ and $81\%$, respectively, were obtained for this biosystem. Considering the deviation of the measured values, more precise determinations could be achieved when measuring higher glucose levels compared to the precision of the measurement for glucose of 600, 150 and 50 µM, with a precision of $94\%$, $81\%$ and $90\%$, respectively. Similarly, a better accuracy ($97\%$) was achieved for the determination of higher glucose concentrations (see Section 2.3). These results demonstrated the potential of this biosensing material to be used as a new tool for sweat sensing, enhancing the possibility of this non-invasive biofluid to become a complement to standard analytical determinations.
## 3.4. Integration and Application of the Biosystem in a Microfluidic Platform with Real Sweat
Ideally, to get the most out of sweat analysis, detection biosystems should be integrated into microfluidic wearable platforms, which allow sweat biomarker determination to be carried out in the field, at the time and place needed [25]. Therefore, to further demonstrate the potential of this approach in the sports field, the applicability of the alginate biosystem for glucose sensing was carried out in a microfluidic platform prototype with a real sweat sample (Figure 5). It needs to be pointed out that, at this state of investigation, the microfluidic platform was not developed to be implemented as a wearable device, but rather as a microfluidic platform for sweat analysis with the integrated biosystem and as a possible laboratory instrument. It was fabricated by lamination (see Section 2.4) and had a total of five layers of PMMA and PSA, as shown in Figure 5A. The bottom PSA layer, which provided the device with a white color, was added to give a better contrast and thus, to enhance the colorimetric analysis. The device was 25 × 14 × 4.5 mm and consisted of a 2 mm diameter input reservoir, a 400 µm microfluidic channel, a 4 mm diameter sample reservoir and five 100 µm air channels (Figure 5B). These were added to the design in order to avoid the formation of air bubbles inside the microfluidic platform, which allowed the air to flow out of the device while sweat flowed towards the sample reservoir. Figure 5C shows the dimensions of the microfluidic PMMA layer.
The alginate beads integrated with the enzymatic mix were placed in individual microfluidic platforms prior to closing the device with the top PSA layer. To demonstrate the applicability of the biosystem, 50 µL of sweat were collected from the forehead of a healthy individual after 45 min of indoor cycling. The sample was diluted two times in order to have enough volume for the accomplishment of the assays (enough volume to cover the bead). Afterwards, the sweat was flowed through the microfluidic devices at 5 µL min−1 to simulate the low sweat rate of the eccrine glands [26].
Since it was unknown whether the glucose concentration of the sample was in the lower or higher concentration range of the calibration curve, the calibration curve that covered the glucose range of 10–1000 µM was chosen. Equation [1] was used for the calculation of the real glucose concentration at 13 min, obtaining a value of 27 ± 5 µM ($$n = 3$$). Figure 5D shows real images of the detection of glucose inside the device at 0, 13 and 20 min, in which the increasing color intensity of the beads is appreciable as an increase of the opacity of the bead. The truly blue color of the bead is not appreciable by the naked eye in this experiment, mainly due to the low concentration of glucose in the sweat sample. Although glucose concentration has great variability among individuals [6], the glucose value measured in this research goes in line with previously reported results. For example, Choi et al. [ 15] measured glucose in sweat from devices mounted on the forearm across 9 human trials, obtaining glucose concentrations between 4 and 40.4 μM.
The measured concentration was compared with a commercially available glucometer (see Section 2.4). Nevertheless, the glucometer range was 1100–27,800 µM, which was much higher than the tested glucose concentrations of the real sweat sample; thus, it did not yield a valid result. However, the fact that it was possible to obtain a measurable result with the biosystem, using real sweat in a microfluidic device prototype, demonstrated the applicability of this alginate biosystem as a proof-of-concept for the determination of glucose in real field scenarios. The deviation of the measured concentration could be due to the lower sweat volume that entered the well of the device compared to the 150 µL that were added during the plate study, permitting a lower amount of glucose to be available to enter the bead. Therefore, further research needs to be done before integrating this biosystem in a wearable microfluidic platform.
## 4. Conclusions
We have developed an alginate-based biosystem integrated with an enzymatic assay for the determination of glucose in sweat. Sweat was detected at 13 min in a linear calibration range for glucose of 10–1000 µM, reinforcing our previous research, in which lactate in sweat was calibrated and detected in a similar alginate biosystem [16]. Moreover, the sensing capabilities of this material could be improved by integrating TiO2 nanotubes, which has been reported to provide the alginate with a higher hydrophilic character, thus allowing faster detection times [17]. Therefore, this demonstrates the potential of alginate hydrogels as scaffold materials for the fabrication of biosystems.
The colorimetric analysis of the biosystem was also investigated in both the B/W and the RGB color codes. Surprisingly, although the color change was towards the blue, observable by the naked eye, the measured B values offered poor resolution and showed high errors. Nevertheless, an appropriate calibration of the biosystem was achieved with the B/W and the G values.
Although further research needs to be done in this field, since this technology is not mature enough to reach the potential end users, the alginate hydrogel biosystem developed in this research has a great potential to be integrated in wearable platforms for sports applications, as demonstrated when integrating the biosystem in a microfluidic device platform prototype. The final device should be flexible to allow correct placement on the skin, biocompatible and user-friendly. Other challenges, such as sample collection and integration of a wireless communication system, would need to be addressed [27,28]. This research is widening the available tools for the fabrication of wearable technology towards the detection of biomarkers in sweat.
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|
---
title: Turmeric Herb Extract-Incorporated Biopolymer Dressings with Beneficial Antibacterial,
Antioxidant and Anti-Inflammatory Properties for Wound Healing
authors:
- Piyachat Chuysinuan
- Chalinan Pengsuk
- Kriengsak Lirdprapamongkol
- Thanyaluck Thanyacharoen
- Supanna Techasakul
- Jisnuson Svasti
- Patcharakamon Nooeaid
journal: Polymers
year: 2023
pmcid: PMC10007553
doi: 10.3390/polym15051090
license: CC BY 4.0
---
# Turmeric Herb Extract-Incorporated Biopolymer Dressings with Beneficial Antibacterial, Antioxidant and Anti-Inflammatory Properties for Wound Healing
## Abstract
Bacterial infection and inflammation caused by excess oxidative stress are serious challenges in chronic wound healing. The aim of this work is to investigate a wound dressing based on natural- and biowaste-derived biopolymers loaded with an herb extract that demonstrates antibacterial, antioxidant, and anti-inflammatory activities without using additional synthetic drugs. Turmeric extract-loaded carboxymethyl cellulose/silk sericin dressings were produced by esterification crosslinking with citric acid followed by freeze-drying to achieve an interconnected porous structure, sufficient mechanical properties, and hydrogel formation in situ in contact with an aqueous solution. The dressings exhibited inhibitory effects on the growth of bacterial strains that were related to the controlled release of the turmeric extract. The dressings provided antioxidant activity as a result of the radical scavenging effect on DPPH, ABTS, and FRAP radicals. To confirm their anti-inflammatory effects, the inhibition of nitric oxide production in activated RAW 264.7 macrophages was investigated. The findings suggested that the dressings could be a potential candidate for wound healing.
## 1. Introduction
Wound dressings are critical tools in the healing process of particularly chronic wounds arising from tissue injuries that heal slowly [1]. The characteristic properties of dressings play key roles in the treatment of chronic wounds. Dressings that are non-toxic and can protect wounds from contamination and further trauma and accelerate wound healing are needed [1,2]. Wound dressings must absorb excess exudates while maintaining moist environments at wound sites and have appropriate oxygen permeability [1,2,3]. Active dressings are the latest type of modern wound dressing produced based on biomaterials, which intrinsically show biocompatibility, biodegradability, and non-toxicity. Active dressings are usually incorporated with biological substances such as growth factors and drugs, and antibacterial agents to combat infections and enhance wound healing, especially chronic wounds [4]. Active dressings should have antioxidant properties to prevent excessive oxidation, to regulate inflammation, and hence to support wound repair [5,6]. The selection of dressing materials is essential for achieving wound repair in an orderly and timely manner. Biopolymers and synthetic polymers are used in fabricating currently available wound dressings in a variety of forms, such as films, hydrocolloids, fibers, foams, and hydrogels [2,7,8]. Hydrogels are potential dressing materials given that their interconnected porous structures mimic the physicochemical properties of tissue environments and absorb aqueous fluids [9]. Meanwhile, open-cell foams accumulate exudates and transmit moisture vapor and oxygen [1,9]. Highly porous dressings facilitate cell growth and subsequent tissue regeneration [5]. Porous materials with the ability to form gels in contact with fluids were thus of interest in the present study. Carboxymethyl cellulose (CMC) is one of the most extensively applied polysaccharide-based biopolymers used as a matrix in the fabrication of dressings because of its biocompatibility, biodegradability, and non-toxicity [2]. Given the high capacity for water absorption and good compatibility with the skin of CMC, it is physiologically harmless and inexpensive, and is a potential matrix in dressing materials [10,11]. In addition, it is hydrophilic, facilitates the formation of polysaccharide–protein complexes, and improves the mechanical properties of protein-based materials by blending with polysaccharides [12]. Silk sericin (SS) is a natural proteinous polymer known as a degumming waste product of silk cocoons commonly discarded in wastewater from textile plants [13,14]. Due to environmental pollution caused by the disposal of SS, a large volume of SS eliminated from silk manufacturing plants should be reused and converted into value-added products that contribute to sustainability. SS is composed of 18 types of amino acids and exhibits a hydrophilic nature, biocompatibility, and biodegradability [15,16]. Similar to silk fibroin, SS has been widely used as a promising sustainable biomaterial in various fields relevant to biomedical and pharmaceutical applications [17]. Especially in wound care, SS is an interesting and suitable material because of its moisture absorption, antioxidation, antityrosinase, and antibacterial activities [18]. Zhao et al. blended SS with chitosan and electrospun the materials to prepare porous wound dressings [16]. The results confirmed that the SS/chitosan dressings were compatible with fibroblasts and exhibited antibacterial properties [16]. SS-incorporated methacrylic-anhydride-modified gelatin hydrogel dressings promoted the adhesion of L929 cells and proliferation of HaCaT and HSF cells, establishing an appropriate environment for re-epithelialization; no inflammatory reaction was found, as reported in the study of Chen et al. [ 18]. SS promoted the proliferation of keratinocytes and fibroblasts, which are involved in cytokine production and re-epithelialization and in the production of extracellular matrix proteins for wound healing [18].
Since infections delay wound healing, the loading of medicinal herb extracts that have antibacterial, antioxidant, and anti-inflammatory properties in dressings has attracted considerable interest regarding the need to accelerate wound healing. Curcuma longa L. (turmeric), of the ginger family (Zingiberaceae), has been generally used as a safe and active drug to treat many chronic diseases. Turmeric contains several bioactive compounds, mainly including curcumin, demethoxycurcumin, bisdemethoxycurcumin, diterpenes, triterpenoids, and sterols [19]. Turmeric shows anti-inflammatory activity, a healing capacity, and antioxidant activity, and inhibits the growth of Gram-positive and Gram-negative bacteria, yeasts, and molds [20,21]. As a result, turmeric has been researched in various fields, including delivery systems, tissue engineering, and modern medicine [22,23,24].
To prevent bacterial infections without the use of antibiotics, and to simultaneously reduce oxidative stress and inflammation and support wound healing, turmeric extracts with different concentrations were loaded in dressings based on a combination of non-toxic, biocompatible, biodegradable, renewable, and cost-effective polysaccharide- and protein-based biopolymers. The physico-chemical, thermal, and mechanical properties, the release profiles, and the particular biological properties relevant to the regulation of wound healing of turmeric-incorporated CMC/SS dressings were investigated.
## 2.1. Extraction of Silk Sericin from Silkworm Cocoons
SS was extracted from Thai hybrid Bombyx mori silkworm cocoons (the local name is Leung Pairoj) with a slightly modified autoclave method [25]. Silkworm cocoons were collected after silk production and were supplied from Sakon Nakhon province in the northeastern region of Thailand. Prior to the extraction process, cocoons were cut into small pieces and washed with tap water twice for contaminant removal. Cocoon pieces were then placed in a drying oven at a temperature of 105 °C for 24 h. Dried cocoons were mixed with deionized water in a ratio of $\frac{1}{30}$ (w/v). The mixture was placed in an autoclave (Tomy, SX-500, Tokyo, Japan) at a temperature of 121 °C for 30 min. The autoclaved mixture was filtered to obtain an SS solution. The solution was then frozen at −20 °C for 12 h and lyophilized in a freeze-drier at −50 °C under vacuum pressure for 48 h for the preparation of a dry SS powder.
Extraction of SS under pressure in an autoclave was successfully confirmed by using an attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FTIR) system (Nicolet170-SX, Thermo Nicolet Ltd., Waltham, MA, USA). The chemical structure of the SS powder was analyzed over a wavenumber ranging from 4000 to 400 cm−1 at a resolution of 4 cm−1 for 64 scans at room temperature.
## 2.2. Fabrication of Turmeric-Loaded Carboxymethyl Cellulose/Silk Sericin Dressings
CMC solution at a concentration of $2\%$ w/v was prepared by dissolving sodium CMC (Mw, 250,000; degree of substitution 0.7; Sigma-Aldrich, St. Louis, MO, USA) in deionized water and stirring it at room temperature. After the CMC solution was homogeneously obtained, SS powder was added to the CMC solution in a weight ratio of 1:1, and the mixture was continuously stirred until a homogeneous CMC/SS blended solution was obtained. For plant extract loading, turmeric (T; Code No. 4593-D420724, Thai-China Flavours and Fragrances Industry, Phra Nakhon Si Ayutthaya, Thailand) was added to the CMC/SS solution at concentrations of $1\%$, $2\%$, and $3\%$ w/v. The CMC/SS and T-loaded CMC/SS solutions were crosslinked by an esterification reaction using $30\%$ w/w citric acid (CA) 1-hydrate (analytical grade, Mw of 210.14 g/mol, Kemaus, New South Wales, Western Australia) in an aqueous solution at a temperature of 80 °C for 18 h. Finally, the crosslinked solutions were added to 24-well plates, frozen at a temperature of −20 °C, and lyophilized at −50 °C under vacuum pressure. Dried CMC/SS and T-CMC/SS dressings formed after 24 h of lyophilization.
## 2.3. Morphological Analysis
CMC/SS dressings with various concentrations ($1\%$, $2\%$, and $3\%$T) were morphologically analyzed through scanning electron microscopy (SEM; JEOL, JSM-IT-500HR, Peabody, MA, USA) compared with pure CMC and SS dressings. Cylindrical samples were cut into small pieces and coated with gold by using a sputtering device prior to SEM observation. The pore sizes of the samples (300 randomly selected pores) were determined from SEM images by using ImageJ software.
## 2.4. Chemical Composition Analysis
The chemical composition of the CMC/SS and T-CMC/SS dressings was characterized using ATR–FTIR (Nicolet170-SX, Thermo Nicolet Ltd., Waltham, MA, USA). FTIR analysis was performed over a wavenumber range of 4000–400 cm−1 at a resolution of 4 cm−1 for 64 scans at room temperature.
## 2.5. Mechanical Test
The mechanical properties of the dressings were tested under compressive force. The diameter and thickness of cylindrical samples were determined prior to applying compressive force with a universal testing machine (Instron 5966, Norwood, MA, USA). The samples were compressed to $80\%$ of their initial thickness at a crosshead speed of 2 mm/min and load cell of 50 kN. The mean values of compressive modulus and strength at $50\%$ displacement were averaged from 10 specimens tested for each condition.
## 2.6. Thermal Transition Behavior and Stability Analysis
The thermal transitions of dressings were investigated by using a diffraction scanning calorimetry (DSC) system (NETZSCH DSC 204F1 Phoenix, Selb, Germany), with a heating rate of 10 °C/min and temperature ranging from 25 °C to 400 °C. The thermal stability of the dressings was analyzed by using a simultaneous thermal analysis (STA) system (NETZSCH5 STA 449F3, Selb, Germany). STA analysis was performed in a nitrogen atmosphere from 25 °C to 600 °C at a heating rate of 10 °C/min.
## 2.7. Water Absorption Study
The ability of the dressings to absorb water was investigated by immersion in the phosphate-buffered saline (PBS) solutions (pH 7.4; tablets; Mw 58.44 g/mol; Gold Biotechnology, St. Louis, MO, USA). Each cylindrical sample with a diameter of 14 mm and height of 5 mm was placed in a polystyrene bottle containing 50 mL of PBS solution. The samples were placed in an orbital shaker for 48 h. The orbital shaker was set at a temperature of 37 °C, and the shaking speed was set at 90 rpm. The sample was removed, excess water at the surface was blotted with filter paper, and the samples were weighed. Wet weight was recorded at each time (Wt). At each time point, the water absorption of each type of sample was determined according to the initial dry weight (Wi) with the following equation:Water absorption (%)=Wt−Wi Wi×100
## 2.8. Determination of Encapsulation Efficiency (EE)
The capacities of CMC/SS dressings that encapsulated turmeric extract were determined by using an indirect quantification method. T-CMC/SS dressings were added to a sodium citrate solution at a concentration of $5\%$ w/v. The mixtures were stirred at room temperature until the dressings were completely dissolved in the solution and separated through centrifugation at room temperature for 10 min. The supernatant was collected and we determined the amount of encapsulated turmeric extract at 425 nm by using a UV–Vis spectrophotometer (GENESYS 10S, Menlo Park, CA, USA). The EE was calculated with the following equation:EE (%)=TeTi×100 where *Te is* the amount of turmeric extract encapsulated in the dressing and *Ti is* the initial amount of turmeric extract added to the dressing. The experiment was performed in triplicate.
## 2.9. Controlled Release and Kinetics Study
Cylindrical dressings with various concentrations of turmeric loading with a diameter of 14 mm and thickness of 5 mm were separately placed in a polystyrene bottle containing 50 mL of PBS solution as a releasing medium at pH 7.4. All experiments were carried out at 37 °C for 48 h with agitation at 90 rpm in an orbital shaker. At each immersion time, 1 mL of sample solution was withdrawn and used in determining the amount of turmeric released. Meanwhile, an equivalent amount of fresh PBS solution was replaced to maintain the sink condition. The amount of turmeric released at each time point was determined using the UV–Vis spectrophotometer at a wavelength of 425 nm. The absorbance of the detected extract was converted into an extract concentration according to the calibration curve of turmeric in a PBS solution, which was prepared by using a series of turmeric concentrations (0–50 mg/L). Results as % cumulative release as a function of immersion time were calculated using the following equation:Cumulative release (%)=MtM0×100 where *Mt is* the cumulative release at time t and M0 is the initial concentration of a loaded extract. Four replicates were analyzed for each sample type, and results were presented as mean ± standard deviation (SD).
The release mechanism of T-CMC/SS dressings ($1\%$, $2\%$, and $3\%$T loading) was investigated by fitting the turmeric release results to different drug release models, including the Higuchi and Korsmeyer–Peppas models [26,27]. The best fit was indicated by the determination of correlation coefficient (r) values. To understand the release kinetics, we fitted the release data of up to $60\%$ cumulative release to mathematical models. The Higuchi equation is one of the most well-known controlled release kinetic equations involving the diffusion mechanisms of drugs released from drug delivery systems [26,28]. The Higuchi equation can be represented by the following equation:Mt=kt0.5 where *Mt is* the amount of released turmeric at time t, k is the Higuchi diffusion constant, and t is the investigation time. According to the Higuchi equation, the percent cumulative release at time t was plotted against the square root of time (t0.5).
The release data were fitted using the Korsmeyer–Peppas model, which is a well-known kinetic model for drug release from a polymeric system [26,28]. The dissolution mechanism is represented in the following equation:MtM∞=ktn where Mt/M∞ is the fraction of turmeric release at time t, k is the Korsmeyer–Peppas rate constant, and n is the release exponent or diffusional exponent. The n value indicates the release mechanism according to the calculated slope of the logarithm plot of Mt/M∞ and time (t) [28,29]. In cylindrical samples, n < 0.45 indicated Fickian diffusion, 0.45 < n < 0.89 represented anomalous transport, and n > 0.89 indicated a case II transport mechanism (zero-order kinetic) [29,30].
## 2.10. In Vitro Antioxidant Characterization
To prepare a sample solution for antioxidant assays, the T-CMC/SS dressings with a diameter of 14 mm and thickness of 5 mm were immersed in 20 mL of methanol for 2 h in a water bath with shaking.
## 2.10.1. DPPH Radical Scavenging Assay
The free radical activity of T-CMC/SS dressings was determined by using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay, as previously described [31]. First, 1 mL of a sample solution was mixed with 3 mL of DPPH solution (0.1 mM in methanol). The mixture was incubated for 30 min in the dark. Finally, the absorbance of the reaction mixture was measured at 517 nm with a microplate reader. Radical scavenging activity (%) was calculated as follows. DPPH radical scavenging activity (%)=AControl−ASampleAControl×100
## 2.10.2. ABTS Radical Scavenging Activity
The ability of T-CMC/SS dressings to scavenge 2,2′-azinobis-(3-ethyl-benzothiazoline-6-sulfonate) (ABTS) radicals was determined by using the ABTS assay. First, an ABTS+ stock solution was prepared by mixing 2 mL of ABTS (7 mM) and 2 mL of potassium persulfate (4.95 mM), and the resulting solution was left in the dark at room temperature for 16 h. The ABTS+ working solution was prepared by diluting an ABTS+ stock solution with methanol, and the absorbance at 734 nm was 0.70 ± 0.02. The sample solution (100 μL) and ABTS+ working solution (200 μL) were mixed and incubated at room temperature for 2 h in the dark. The absorbance was measured at 734 nm, and the ABTS radical scavenging activity was calculated according to the following equation:ABTS radical scavenging activity (%)=AControl−ASampleAControl×100
## 2.10.3. Ferric Reducing Antioxidant Potential (FRAP) Assay
The antioxidant potential of T-CMC/SS dressings to reduce Fe3+ into Fe2+ was evaluated by using the slightly modified FRAP assay. A FRAP solution was prepared as a mixture of 33.3 mM ferric chloride solution: 9.9 mM tripyridyl-s-triazine: 300 mM acetate buffer (pH 3.6) in a 10:1:1 ratio. The sample solution (100 µL) was mixed in the FRAP solution (200 µL) and incubated at room temperature for 2 h. Then, the absorbance at 593 nm was measured in a microplate reader. The standard curve was generated using Trolox. FRAP antioxidant activity was expressed as milligrams of Fe2+ equivalent per gram of sample.
## 2.11. Antibacterial Test
The antibacterial activity of T-CMC/SS dressings was investigated against two pathogenic bacteria (Gram-positive and Gram-negative) by using the disk diffusion assay of the US Clinical and Laboratory Standards Institute, as previously described [32]. Bacterial suspensions (106 CFU/mL) of *Staphylococcus aureus* (ATCC25923) and *Escherichia coli* (ATCC25922) were spread over agar plates and grown overnight at 37 °C. Dressings with a thickness of 2 mm were cut into circular disks (diameter of 14 mm). The sample disks were sterilized by exposure to UV light for 30 min prior to testing for bacterial inhibition by turmeric released from the T-CMC/SS dressings. CMC/SS samples without turmeric were used as controls. Each sample disk was placed on the bacterial agar plate and incubated at 37 °C for 24 h. Subsequently, the diameters of clear zones around the sample disks were measured. The normalized width of the antimicrobial halo (NWhalo) of each sample disk was determined by applying the following equation according to [32]:NWhalo=(Diz−D2)D where *Diz is* the diameter of the inhibition zone (mm) after incubation for 24 h and D is the diameter of a sample disk (mm). Four replicates were tested for each sample.
## 2.12. In Vitro Cytotoxicity Test
An indirect cytotoxicity test was performed on T-CMC/SS dressings in accordance with the ISO10993-5 standard test method [6,29]. Briefly, the dressings (20 mg) were sterilized under UV radiation for 30 min and then immersed in 0.2 mL of phenol red-free DMEM cell culture medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with $10\%$ v/v fetal bovine serum and $1\%$ penicillin/streptomycin, in a 96-well tissue culture polystyrene plate (TCPS). They were incubated for 24 h to produce sample extracts for cytotoxicity and anti-inflammatory tests. RAW 264.7 cells (mouse macrophage cell line, ATCC TIB-71, Rockville, MD, USA) were seeded in a phenol red-free medium at 30,000 cells/100 µL/well in a separated 96-well TCPS and cultured at 37 °C for 24 h to allow cell attachment onto the well surface. Then, 25 µL of a sample extraction was added to each well, and the cells were further incubated for 24 h. Given that RAW 264.7 cells weakly attached to the plate, a modified MTT assay was used in determining the viability of the cells. After the treatment, the wells were mixed with an MTT solution (25 µL) and further incubated for 4 h. Then, 100 µL of lysis solution ($20\%$ SDS in 10 mM HCl) was added to lyse cells, and formazan crystals formed by the mitochondrial activity of living cells were solubilized. The plate was left in the dark at room temperature for 2 days. The absorbance at 550 nm was measured and subtracted from the absorbance at 650 nm. The absorbance of untreated control cells was collected upon $100\%$ cell viability. Three replicates were investigated for each sample.
## 2.13. Anti-Inflammatory Assay
Anti-inflammatory effects of the T-CMC/SS dressings were determined according to their performance in inhibiting nitric oxide production in RAW 264.7 cells. A cell suspension (100 µL) in a phenol red-free medium was seeded into 96-well TCPS at 30,000 cells/well and cultured at 37 °C for 24 h. Then, the cells were treated by adding 25 µL of sample extract, incubated for 3 h, and stimulated with 10 µg/mL lipopolysaccharides (LPSs) from E. coli (Sigma Aldrich, Saint Louis, MO, USA) for 24 h. The culture medium in each well was collected and used in determining the nitric oxide level with a Griess reagent (Promega, Madison, WI, USA) [6]. Briefly, 50 µL of the collected medium was mixed with 100 µL of Griess reagent and incubated for 20 min at room temperature in the dark. The absorbance was measured at 540 nm, and the concentration of nitrite was calculated using a sodium nitrite standard curve (0–100 µM).
## 2.14. Statistical Analysis
A one-way ANOVA was used in comparing the means of different data sets, and $p \leq 0.05$ was considered statistically significant.
## 3.1. Microstructure
CMC was successfully fabricated into three-dimensional (3D) porous dressings by chemical crosslinking with citric acid (CA). A highly interconnected porous structure showing a uniform opened cellular microstructure was observed, as shown in Figure 1a. The pore sizes of the CMC dressings ranged from 70 μm to 480 μm, and the mean pore size was determined at around 216 ± 62 μm. Figure 1b shows the maintained microstructures of the CMC dressings after blending CMC with SS. However, it was obvious that the pore sizes of the CMC/SS dressings were smaller than those of the CMC dressings, having a pore size distribution in the range of 30–280 μm after blending with SS. The mean pore sizes of the dressings decreased to 121 ± 39 μm in the presence of SS. It can be suggested that the reduced pore sizes of the CMC dressings with the presence of SS were probably due to the formation of a polysaccharide–protein complex [12,33]. For instance, hydrogen bonding between the -COOH group of the CMC polysaccharide and the -NH2 and -COOH groups of the SS protein led to the increased entanglement of biopolymer chains and consequently decreased their free volume. After the incorporation of turmeric extract at different concentrations, changes in morphology and pore dimension were not significant. Figure 1c–e show that the turmeric-loaded dressings had highly porous microstructures with the same pore size (approximately 20–300 μm) as the CMC/SS dressings without turmeric extract. The result suggests that the turmeric loading did not intensively impact the microstructure of the porous CMC/SS dressings. Moreover, an increase in the turmeric amount did not significantly alter the pore sizes of the dressings. A highly interconnected porous structure with opened pores was maintained, which is suitable for absorbing large amounts of exudates, facilitating cell migration and oxygen and other nutrients’ transfer, thus improving cell growth and tissue regeneration [17]. In addition, the pore sizes of the T-CMC/SS dressings (30–300 μm) were in the same range as those of commercially available foam dressings (around 32–1000 μm) [34].
## 3.2. Chemical Compositions
Figure 2 shows the FTIR spectra of fabricated CMC/SS dressings with and without turmeric loading compared with the spectra of the neat materials used. In the study, the stability of the dressings in an aqueous environment was enhanced by crosslinking CMC-based dressings with citric acid (CA), given that CMC is a hydrophilic polymer. CA is a naturally occurring carboxylic acid and was used as a crosslinking agent for CMC through an esterification reaction. The spectrum of CMC without crosslinking showed strong absorption peaks at 1584 cm−1 and 1407 cm−1, assigned to the asymmetric and symmetric stretching of carboxylate (COO−) groups, respectively. The peak at 1315 cm−1 corresponded to CH-O-CH2 stretching. A broad absorption band at a range of 3600–3000 cm−1 was due to –OH stretching [35]. After crosslinking with CA, an additional peak was found at a wavenumber of 1718 cm−1 in the spectrum of CA-x-CMC that was attributed to the formation of ester linkages during the crosslinking reaction. The presence of an ester bond confirms the occurrence of crosslinking. The peak intensity of the –OH stretching vibration of CMC at 3600–3000 cm−1 obviously increased after crosslinking, indicating that intramolecular and intermolecular interactions formed during esterification. The absorption peak at 1584 cm−1 in the spectrum of CA-x-CMC was stronger than that of CMC because of the formation of the carboxylate anion (COO− group) during crosslinking [35,36]. The findings confirm that the crosslinking reaction between CMC and CA was successfully achieved, when compared with the spectra of neat CMC and CA. In the FTIR spectrum of SS, characteristic peaks of SS were found at 3293 cm−1, which was associated with N-H stretching vibration and overlapped with the band of –OH stretching at 3600–3000 cm−1. In addition, C=O in the amide group and N-H bending were found at 1614 and 1511 cm−1, respectively [15]. This means that the autoclave method is an effective and eco-friendly method for extracting SS from silk cocoons. The spectrum of CMC/SS dressings showed the characteristic peaks of CMC and SS. The peak at a wavenumber of 1584 cm−1 was due to the stretching vibration of C=O in CMC, whereas the vibrations of N-H stretching, C=O stretching of amide groups, and N-H bending in the chemical structure of SS were found at 3293 cm−1, 1614, cm−1, and 1511 cm−1, respectively. The intensity of the broad band at 3600–3000 cm−1 showed that the free hydroxyl groups decreased relative to that of the CMC dressings. This decrease indicates intermolecular interactions between polysaccharides and proteins, such as hydrogen bonding between CMC and SS [37]. The molecular interaction between CMC and SS contributed to the stability of the porous CMC/SS dressings [17]. The finding also relates to the reduction in the CMC dressings’ pore sizes with SS blending. The FTIR spectra of the T-CMC/SS dressings were observed in the same peak positions as the spectrum of the CMC/SS dressings, but a peak appeared at 1628 cm−1, which is a characteristic position of aromatic moiety C=C stretching in turmeric extract [20]. It can be suggested to be due to the existence of the turmeric extract loaded in the dressings.
## 3.3. Thermal Properties
DSC analysis was used in investigating the thermal stability of the T-CMC/SS dressings compared with that of the CMC and SS dressings. As illustrated in Figure 3a, all sample types showed an endothermic transition in a temperature range of 50–150 °C, as a typical result of water evaporation in hydrophilic polymers. Another endothermic peak was found at around 150–225 °C that was attributed to the thermal decomposition of the CMC and SS biopolymers. In addition, an obvious exothermic transition was observed at around 250–375 °C in all dressings and was attributed to the degradation temperature (Td) of the materials. Blending CMC with SS enhanced the thermal stability of CMC. The Td of CMC/SS dressings appeared at 293.6 °C, whereas the Td of the CMC and SS dressings were found at 286.5 and 280.5 °C, respectively. After the turmeric extract was loaded, a change in Td was not observed, indicating that the presence of the turmeric extract in the CMC/SS dressings did not influence the thermal transition behavior of the dressings. The thermal stability of dressings maintained under thermal conditions during sterilization was confirmed by TGA and DTG results. In Figure 3b, the thermogram of the CMC dressings shows three regions of weight loss, including (I) the evaporation of free water and moisture trapped inside the porous structures, ranging from 40 °C to 125 °C; (II) the occurrence of the evaporation of water bound in the cellulosic structure and the decomposition of crosslinked CA at around 135–245 °C [36,38]; and (III) a large region showing the pyrolytic decomposition of the polymer backbone (Td) at 245–365 °C. At this stage, the mass loss of the polymer was found at around $42.75\%$. This result agrees with the DTG curves in Figure 3c. Three major endothermic peaks appeared in the CMC dressings’ curve. SS dressings showed a TGA curve similar to the CMC dressings’ curve, composed of three main decomposition stages. The desorption of bound water was observed up to 120 °C. The thermal decomposition regions of the SS component were found at 160–270 °C and 250–305 °C, with $11.37\%$ and $46.68\%$ weight loss, respectively. In the curve of the CMC/SS dressings, the addition of SS slightly increased Td to 306.1 °C, compared with 302.9 °C in CMC (Figure 3c). SS blending can slow down the thermal decomposition of dressings, in contrast to the use of individual polymers. In the T-CMC/SS dressings, the TGA curves exhibited an additional decomposition step (IV) in a temperature range of 350–450 °C, involving the decomposition of the incorporated turmeric extract. The DTG curves in Figure 3c agree with the TGA curves. T-CMC/SS dressings revealed four dominant peaks. The maximum decomposition temperatures of turmeric loaded in CMC/SS dressings at concentrations of $1\%$, $2\%$, and $3\%$ were found at 398.5 °C, 397.0 °C, and 401.3 °C, respectively. This finding means that the step height of the TGA curves and the peak area of the DTG curves increased with the concentration of turmeric loading. Moreover, the degradation temperature of turmeric incorporated into a polymer matrix was higher than that of pure turmeric extract (280 °C). The probable reason was the interaction of extract molecules with CMC and SS biopolymers through hydrogen bonding, protecting the turmeric compound from thermal degradation better than a free compound. This phenomenon was also found in a previous study [39]. According to DSC, TGA, and DTG thermograms, the CMC/SS dressings could be used as a protective carrier of turmeric extract to maintain its bioactivity under thermal conditions, especially during the sterilization process (i.e., 121 °C by using autoclave method).
## 3.4. Mechanical Properties
Biopolymers typically have low mechanical properties compared with synthetic polymers, and this property is a limitation in various applications. In this study, SS with intrinsic stiffness was selected for blending with CMC in order to enhance the mechanical performance of the dressings. Figure 4a shows the compressive stress–strain curves of representative dressings. All types of samples exhibited the same mechanical behavior under compressive force, which is a typical curve of porous polymeric materials. The slopes of curves in the elastic region were calculated to determine the materials’ stiffness (an inset in Figure 4a). The mechanical properties of the CMC dressings in terms of compressive modulus and strength significantly improved after blending with SS. As shown in Figure 4b,c, CMC/SS dressings had the compressive modulus and strength of 3.3 ± 0.7 MPa and 1.2 ± 0.3 MPa, respectively, whereas the modulus and strength of pure CMC dressings were 1.2 ± 0.1 and 0.23 ± 0.02 MPa, respectively. The SS plays a key role in enhancing the elastic modulus and mechanical strength of CMC dressings by functioning as a reinforcing additive. The improvement in the mechanical properties found in the CMC/SS dressings was in agreement with the formation of the polysaccharide–protein complex. The incorporation of turmeric extract did not remarkably influence the compressive modulus of the CMC/SS dressings (Figure 4b). Meanwhile, an increase in turmeric content significantly reduced the compressive strength of the dressings from 1.2 ± 0.3 MPa to 0.5–0.8 MPa, as presented in Figure 4c. It can be suggested that the turmeric compound in the dressings could act as a plasticizer in the polymer network, leading to an improvement in the dressings’ softness. Softness and flexibility are characteristics of the ideal dressing as it can be removed without causing pain and trauma to the wound. In addition, the mechanical and structural stability of the dressings are substantiated in comfortable clinical trials and they are capable of frequent changes [40].
## 3.5. Water Absorption Behaviors
After the dressings were immersed in PBS solutions, all types of samples showed fast water uptake at an initial time of 10 min and subsequently formed hydrogels by the first hour of immersion, as shown in Figure 5. Pure CMC dressings gained the highest amount of water at around $2500\%$, and the addition of SS, forming CMC/SS dressings, led to a slight reduction in water uptake (~$2000\%$). As a result of turmeric extract loading, T-CMC/SS dressings exhibited reduced water absorption to around 1000–$1750\%$. The finding agrees with the dressings’ porous microstructure, as previously mentioned. It was also found that the fluid absorption capacity was 10–20 times the sample weight, while their structural stability was maintained at an equilibrium point for 48 h of investigation without disintegration. The result showed the same range as the absorption ability of commercially available alginate dressings (15–20 times), as previously reported [40], which indicates that these are suitable dressings for highly exuding wounds. The high water absorption ability of dressings has been reported to aid granulation tissue in a moist environment [5]. The formation of hydrogels also promoted a soothing effect by reducing the temperature of the wound. Hence, wetted dressings increased the healing rate in contrast to dry dressings, because the healing of wounds without inflammation takes place in a moist environment [1].
## 3.6. Release Behaviors and Kinetics
CMC/SS dressings were expected to serve as an active delivery device that enables the controlled release of bioactive compounds such as loaded turmeric extract and to prevent the risk of an overdose effect of antibiotics. CMC/SS dressings loaded with turmeric extract at $1\%$, $2\%$, and $3\%$ w/v showed encapsulation efficiency of 80 ± $8\%$, 85 ± $5\%$, and 96.8 ± $0.7\%$, respectively. It is suggested that the highly porous CMC/SS dressings were able to encapsulate high amounts of turmeric extract, indicating a potential carrier of hydrophilic compounds. Such high loading capacity was due to the good compatibility between the hydrophilic biopolymer matrix and water-soluble turmeric extract. In other words, the turmeric compound was well embedded in the network of crosslinked biopolymers. Figure 6 shows the similarity of the release behaviors among $1\%$, $2\%$, and $3\%$T-CMC/SS dressings. By immersion in a PBS solution for 48 h, each type of dressing exhibited a typical burst release up to $40\%$ in the initial first hour of investigation, followed by the gradual increase in turmeric release at a long immersion time. It was noticed that the initial burst release matched the high water uptake rate (Figure 5). The high diffusion capacity of the dressings might induce the burst release of the turmeric extract. Moreover, it was due to the fast release of the turmeric compound weakly bound at the dressings’ surfaces. After 6 h of immersion, the dressings reached an effective level in a therapeutic concentration range of 60–$80\%$ until soaked for 24 h. For an extended time, the turmeric extract released from all dressings slightly increased, followed by sustained release over 48 h. At 48 h of immersion, turmeric release was observed at 90–$100\%$ in all cases. An increased amount of turmeric loading raised the released turmeric concentration in the medium at all time points. The release behavior of turmeric extract from CMC/SS dressings was found in an equivalent manner to that reported in a previous study of guava leaf extract-loaded alginate/gelatin hydrogels, which displayed an initial fast release within 30 min, followed by gradual release after 1 h of immersion in a PBS solution [31]. In addition, the release study of curcumin-loaded polycaprolactone dressings demonstrated controlled release behavior over 48 h of investigation [41].
To investigate the release mechanism of turmeric extract incorporated in CMC/SS dressings, release kinetics models such as the Higuchi and Korsmeyer–Peppas models were applied to find the best fit with the turmeric release behavior (Figure S1). Release parameters, such as the release constant (k) and release exponent (n), were determined and used in identifying the release mechanism, whereas the r2 coefficient of determination over 0.95 was considered to indicate the goodness of fit of the release model [42]. As seen in Table 1, the turmeric extract released from all formulations showed the best fit for the Higuchi and Korsmeyer–Peppas models, as indicated by the r2 values higher than $0.97\%$. The extract released from the dressings followed the Higuchi model, which is typically used to describe the controlled release of water-soluble molecules based on a Fickian diffusion mechanism [43,44]. The turmeric extract ($1\%$, $2\%$, and $3\%$T) loaded in the dressings did not present a significant difference in Higuchi constant (k), ranging from 0.56 to 0.64, indicating that the diffusion rate of all formulations was not influenced by the turmeric loading content. According to the Korsmeyer–Peppas model, the release exponent (n) values can be used in characterizing the release mechanisms of polymeric materials. The dressings with different $1\%$, $2\%$, and $3\%$T loading had n values of 1.89, 0.94, and 0.93, respectively. The n values above 0.89 indicated that the release of the turmeric compound from CMC/SS dressings was controlled by the mechanism of case II transport, which refers to first-order kinetics, indicating that the release is controlled by the swelling of the matrix [30]. Thus, it can be suggested that the release behavior of T-CMC/SS dressings was driven by the diffusion mechanism and swelling capacity. This finding was supported by the high water absorption capability as the dressings tended to gain high water content and consequently became swollen hydrogels at the initial period of immersion in PBS solution. The obtained data supported the hypothesis that the T-CMC/SS dressings with the in situ formation of a hydrogel in contact with aqueous conditions would be appropriate for healing highly exudating wounds, and they could prevent the wound from dehydration. Subsequently, such moist environments induced the release behavior of the turmeric compound through the diffusion and swelling mechanism of the dressings. The ability of the dressing to provide a predictable and sustainable release rate plays a key role in the antibacterial function to combat infections.
## 3.7. Antibacterial Activity
Infection caused by bacterial contamination is known as a fundamental problem in chronic wounds, since it prolongs the inflammatory phase of wound healing, leading to a delay in wound repair [45]. For optimal wound repair, the elimination of bacterial contamination is necessary in the wound environment. Various antibiotics and synthetic antibacterial agents such as silver nanoparticles (AgNPs) have been widely researched for this purpose. For instance, riclin-AgNPs-based hydrogels as antibacterial and anti-inflammatory wound dressings were presented [46]. Even though AgNPs provided a broad antibacterial spectrum against both Gram-positive and Gram-negative bacterial strains, individual use of AgNPs was not responsible for the anti-inflammatory function. The AgNPs were therefore combined with ruclin to promote inflammation regulation. In addition, common side effects and the overusage of antibiotics become a severe problem for health. Thus, plant-based compounds and herbal medicinal extracts are recommended by the World Health Organization (WHO) as a safe and effective alternative to synthetic drugs [45]. In the present study, turmeric, extracted from a typical medicinal herb, was used as an antibacterial agent and as an antioxidant and anti-inflammatory agent.
The performance of the loaded turmeric extract in the CMC/SS dressings, inhibiting the growth of tested bacteria, is reported in Table 2, in comparison with that of CMC and CMC/SS dressings as controls. The bacterial growth inhibition activity of the dressings was determined by the halo diameter around the disk-shaped sample (Figure S2). The growth-inhibitory effect of the T-CMC/SS dressings was markedly observed in both bacterial strains S. aureus and E. coli. The NWhalo values of T-CMC/SS, ranging from 0.24 to 0.39, were correlated with the % turmeric loading of the dressings, in a concentration-dependent manner. The CMC and CMC/SS dressings did not exhibit antibacterial activity. Turmeric extract released from the dressings in the therapeutic concentration range of 60–$80\%$ within 24 h of immersion, as previously reported in Figure 6, was responsible for the antibacterial function of the dressings. The dressings with $1\%$–$3\%$T loading exhibited potent antibacterial property, which is a desirable characteristic in active dressings to protect wounds from infection with pathogen microorganisms and to achieve effective wound healing.
## 3.8. Antioxidant Activity
A complicated healing process occurs via the excessive production of free radicals in response to tissue damage, since the free radicals destroy proteins, lipids, and the extracellular matrix (ECM) [46]. Apart from antibacterial properties, the fabricated active dressings were required to provide an antioxidant function to reduce free radicals and support fast wound healing. As a result, turmeric extract was selected, since it is an effective natural compound having various biological properties. In the current study, three different antioxidant assays, namely the DPPH, ABTS, and FRAP assays, were conducted to assess the in vitro antioxidant potential of T-CMC/SS dressings. The DPPH and ABTS free radical scavenging assays are based on the quenching of stable colored free radicals, indicating the potential scavenging ability of the test samples. As shown in Table 3, the DPPH and ABTS free radical scavenging activities of T-CMC/SS dressings were parallelly increased with the % turmeric loading, and the highest DPPH and ABTS free radical scavenging activities obtained by $3\%$T-CMC/SS dressings were 13 ± $2\%$ and 24 ± $1\%$, respectively. To support the results of the DPPH and ABTS assays, a FRAP assay was also carried out. Similarly, the T-CMC/SS dressings displayed FRAP activity in a concentration-dependent manner, and the highest FRAP activity of 3.7 ± 0.4 mg Fe2+ equivalents/mg sample was obtained by the $3\%$T-CMC/SS dressings, contrary to the pristine dressings, which exhibited negligible radical scavenging capacity. The results are consistent with the report by Fan Y. et al. [ 47], as the DPPH radical scavenging property of turmeric extract increased with increasing concentrations. The effective antioxidant characteristics of turmeric extract were due to the presence of hydroxyl moieties, double bonds, and carbonyl groups [20]. It showed that the fabricated T-CMC/SS dressings could serve as an active antioxidant dressing by using a natural turmeric extract. This is a vital function in controlling the normal physiology of wound healing by maintaining low levels of wound oxidative stress [47].
## 3.9. In Vitro Cytotoxicity and Anti-Inflammatory Activity
RAW 264.7 macrophage-like cells were treated with the sample extracts of CMC, CMC/SS, and $1\%$, $2\%$, and $3\%$T-CMC/SS dressings at various concentrations (1.25, 2.5, 5, 10, and 20 mg/mL) for 24 h, and the cytotoxicity of the sample extracts was evaluated by using the MTT assay. The results showed that both CMC and CMC/SS dressings were not toxic to the cells at all tested concentrations, as the viability of the treated cells was greater than $80\%$ (Figure 7a). The polymer matrices used in fabricating the dressings are not toxic and are biocompatible with the tested cells. In the presence of turmeric extract, the sample extracts of $2\%$T- and $3\%$T-CMC/SS dressings at concentrations of 10 and 20 mg/mL exhibited significant cytotoxic effects ($p \leq 0.05$), indicating an overdose of turmeric to the cells. Therefore, the concentration range of 1.25–5 mg/mL was considered a non-toxic dose for the sample extracts of $1\%$–$3\%$T-CMC/SS dressings and was further used in evaluating the anti-inflammatory effect of the dressings in LPS-stimulated RAW 264.7 cells.
Nitric oxide (NO), produced by cells, is an inflammatory mediator that is crucial for wound repair. However, excessive NO production causes the dysfunction of tissue cells [47,48,49]. Therefore, the regulation of NO production is another significant role of suitable active dressings for wound healing. LPS stimulation markedly increased the nitric oxide production in RAW 264.7 cells to a level of 50 ± 2 µM, compared with 4 ± 2 µM in untreated control cells (Figure 7b). The sample extracts of $1\%$T-CMC/SS dressings decreased LPS-induced nitric oxide production, in a dose-dependent manner, and 37 ± 3 µM was observed at the highest tested concentration (5 mg/mL). Interestingly, the sample extractions of $2\%$T- and $3\%$T-CMC/SS dressings, at all tested concentrations, significantly reduced nitric oxide production to 2–6 µM, which is lower than 15.1 ± 0.7 µM observed in vermelhotin-treated cells. The results demonstrated that the anti-inflammatory effects of $2\%$ and $3\%$T-CMC/SS dressings were greater than those of $1\%$T-CMC/SS dressings. Meanwhile, there was no significant difference in the effects of $2\%$T- and $3\%$T-CMC/SS dressings. It could be suggested that the turmeric content at $2\%$ w/v was the optimal loading for the CMC/SS dressings to express effective anti-inflammatory properties. The $2\%$T-CMC/SS dressings are recommended as optimal active dressings with multifunctional features (antibacterial, antioxidant, and anti-inflammatory properties) for wound healing applications.
## 4. Conclusions
T-CMC/SS dressings were fabricated based on the use of biodegradable and renewable biopolymers through a green, facile, economic methodology. The T-CMC/SS dressings showed highly interconnected porous structures having pore sizes of around 30–300 μm with uniformity. The mechanical properties and thermal stability of the dressings were improved by blending CMC polysaccharide and SS protein. The dressings could be effective carriers of turmeric extract, as shown by the higher than $80\%$ encapsulation efficiency. The dressings provided excellent water absorption capability for 10–20 times based on the weight of the dressings, which are appropriate to absorb high levels of wound exudate. Simultaneously, the dressings were able to form a hydrogel structure in contact with an aqueous solution, leading to the formation of moist dressings. Turmeric extract was released from the CMC/SS dressings in a controlled release manner over 48 h of investigation, including an initial burst release of up to $40\%$ after the first hour, followed by gradual release at up to 60–$80\%$ after 24 h of investigation. The turmeric release behaviors were best fitted with the Higuchi and Korsmeyer–Peppas models. This evidenced that the diffusion rates of T-CMC/SS dressings were not influenced by the turmeric loading concentration, and the release behavior of turmeric extract from the dressings was controlled by the diffusion mechanism and the swelling of the dressings. The antioxidant activity of the dressings presented a concentration-dependent manner, given that the highest antioxidant activity was found in the case of $3\%$T-CMC/SS dressings. In addition, the $3\%$T-CMC/SS dressings exhibited the highest antibacterial activity against E. coli and S. aureus bacterial strains within 24 h of incubation, compared to other dressing types. The in vitro cytotoxicity test showed that the biopolymer matrices and the turmeric extract used for the fabrication of dressings were not toxic to RAW 264.7 macrophages. Importantly, the turmeric extract released from T-CMC/SS dressings was able to inhibit nitric oxide production by the activated RAW 264.7 cells, indicating anti-inflammatory properties. In particular, $2\%$–$3\%$T-CMC/SS dressings exhibited highly efficient antibacterial, antioxidant, and anti-inflammatory activities. In combination with the specific physical properties of the T-CMC/SS dressings, including the highly porous microstructure, good thermal stability, enhanced mechanical properties, and high water absorption capability, it is recommended that the proposed dressings (the optimal $2\%$T-CMC/SS formulation) could be a potent candidate for use as bioactive dressings suitable for wound healing purposes. Significantly, turmeric extract could act as a multifunctional bioactive agent that is beneficial for would healing.
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|
---
title: COVID-19 Detection Using Photoplethysmography and Neural Networks
authors:
- Sara Lombardi
- Piergiorgio Francia
- Rossella Deodati
- Italo Calamai
- Marco Luchini
- Rosario Spina
- Leonardo Bocchi
journal: Sensors (Basel, Switzerland)
year: 2023
pmcid: PMC10007577
doi: 10.3390/s23052561
license: CC BY 4.0
---
# COVID-19 Detection Using Photoplethysmography and Neural Networks
## Abstract
The early identification of microvascular changes in patients with Coronavirus Disease 2019 (COVID-19) may offer an important clinical opportunity. This study aimed to define a method, based on deep learning approaches, for the identification of COVID-19 patients from the analysis of the raw PPG signal, acquired with a pulse oximeter. To develop the method, we acquired the PPG signal of 93 COVID-19 patients and 90 healthy control subjects using a finger pulse oximeter. To select the good quality portions of the signal, we developed a template-matching method that excludes samples corrupted by noise or motion artefacts. These samples were subsequently used to develop a custom convolutional neural network model. The model accepts PPG signal segments as input and performs a binary classification between COVID-19 and control samples. The proposed model showed good performance in identifying COVID-19 patients, achieving $83.86\%$ accuracy and $84.30\%$ sensitivity (hold-out validation) on test data. The obtained results indicate that photoplethysmography may be a useful tool for microcirculation assessment and early recognition of SARS-CoV-2-induced microvascular changes. In addition, such a noninvasive and low-cost method is well suited for the development of a user-friendly system, potentially applicable even in resource-limited healthcare settings.
## 1. Introduction
COVID-19 is an infectious respiratory disease caused by SARS-CoV-2, a coronavirus discovered in the city of Wuhan, China, in 2019 [1]. Since then, the virus has spread rapidly to other countries around the world, causing a global health and economic crisis. According to data from the World Health Organization (WHO), more than 664 million cases and more than 6.6 million deaths have been recorded as of January 2023 [2]. The rapid spread and the difficulties of treating patients with SARS-CoV-2 infection have led to the development of several diagnostic methods for the early recognition and treatment of patients with COVID-19. Except for the molecular test, based on reverse transcription-polymerase chain reaction (RT-PCR), which remains the reference diagnostic tool, low-cost and easy-to-perform procedures, and tests have also been proposed. Among these, the analysis of the photoplethysmogram (PPG) signal as a means for the early recognition of patients with COVID-19 in the hospital setting has been suggested.
COVID-19 infection typically presents with symptoms such as weakness or fatigue with fever, dry cough and shortness of breath. In severe infection, the symptomatology may progress to serious complications such as pneumonia, acute respiratory distress syndrome (ARDS), requiring intubation and emergency treatment. The virus binds to upper respiratory tract epithelial cells primarily through the ACE-2 receptor, which is highly expressed in adult nasal epithelial cells. The virus then undergoes replication and propagation within the upper respiratory tract, triggering the immune response responsible for the onset of typical symptomatology. If the immune response is not sufficient to contain the spread of the infection, lower respiratory tract (pulmonary alveoli) involvement and progression to acute respiratory distress syndrome (ARDS) occurs in severe cases [3]. Infected lung cells release a storm of cytokines (CS) that triggers an exaggerated host immune system response that can culminate in widespread cellular damage. As previously observed in other clinical conditions such as sepsis [4,5], the body’s immune response results in endothelial dysfunction that can induce microvascular damage, coagulation alterations, and consequently contribute to organ dysfunction [6]. In this regard, it has been reported that in COVID-19 patients, systemic microcirculatory changes accompanied by endothelial dysfunction correlate with the severity of ARDS [7]. The role of endothelial dysfunction is important considering that it has been associated with poor prognosis in the acute phase and with persistent symptoms, such as chest pain and fatigue, during the long COVID-19 period (4 weeks or more after onset infection) [8]. Therefore, an analysis of microcirculation and endothelial damage may play a key role in both the clinical course of COVID-19 and the evaluation of the long-term effects of this clinical condition. This evaluation could allow the development of new tools for monitoring patients to reduce the number of severe cases requiring intensive care units. In this context, the use of devices such as the pulse oximeter may be a valuable solution. The pulse oximeter is a non-invasive optical device based on the technique of photoplethysmography that allows the measurement of blood volume changes in a peripheral district, usually the fingertip or earlobe. The definition of the anatomical site where measurement is performed is a key point in the acquisition protocol, since perfusion characteristics vary according to the measurement location [9,10]. This device is commonly used for the estimation of heart rate and for the measurement of blood oxygenation (SpO2). In addition to these commonly monitored parameters, it is known that the characteristic components of the pulse oximeter waveform (PPG) are associated with specific circulatory functions [11,12]. In this perspective, a detailed analysis of the PPG waveform could provide important information on the microcirculatory function abnormalities and enable early recognition of patients with SARS-CoV-2 infection.
In our previous work, Rossi et. al [13], we investigated the feasibility of using the photoplethysmographic signal through a multi-exponential model to recognise patients hospitalised with COVID-19 and the severity of the disease itself. The photoplethysmographic signal was evaluated in 93 subjects with the aim of discriminating between healthy controls and COVID-19 patients of different severity. Using the parameters of the mathematical model, three different classifiers (Bayesian, SVM and KNN) were trained and tested, validating the results obtained by the leave-one-subject-out method. In this work, we will use the same dataset used in that study by proposing a different method for the analysis of the PPG signal. In particular, this article presents a new method for PPG signal pre-processing and a custom deep learning model that, starting from PPG signal analysis only, performs classification between COVID-19 patients and control subjects. Regarding the pre-processing phase, we developed a method that analyzes waveform morphology. Specifically, we adopted a Template Matching approach that performs a pulse-by-pulse comparison with a reference signal. With regard to the deep learning model, we developed a convolutional neural network architecture, a type of model that is finding increasing application in the field of biosignal analysis [14]. The method proposed in this paper, based only on the pulse oximeter signal, could be applied as a first assessment tool for the identification of COVID-19-induced microcirculatory alterations. Moreover, since the pulse oximeter is a low-cost device, as well as already widely used in hospital settings, the introduction of such a method would not imply any additional costs and could also be applied in healthcare settings with limited resources, such as those in underdeveloped countries and territorial emergency. Furthermore, due to the easy usage and the non-invasiveness of the device, this method could be useful in the development of a clinician-friendly system that could potentially be applied to other clinical conditions that have an impact on peripheral circulation, such as hypertension or sepsis.
The purpose of this study was to define a method, based on deep learning approaches, for the identification of COVID-19 subjects from the analysis of the raw PPG signal only. In addition, comparison with the results obtained with the different procedure [13] applied on the same sample of patients is a further objective of this study. In the Section 2, we reported the main studies that, similar to ours, have adopted a template-matching method for analysing the PPG signal or have used an artificial intelligence method applied to the photoplethysmogram. Our method is described in detail in Section 3. In particular, we described the data acquisition aspects, the implementation details of the pre-processing algorithm and the architecture of the neural network, together with the strategy adopted for the model training. The obtained results are reported in Section 4, while a discussion of them and a comparison with other work is given in Section 5, where limitations and future developments of the present study are highlighted.
## 2. Related Methods
There are many techniques that can be used to analyze the PPG signal. In this sense, knowing the performance and characteristics of different methods can contribute to optimising the treatment of patients. In this section, we report the main studies resulting from the literature review which, similarly to our method, adopted a template-matching approach for processing or an artificial intelligence approach for analysing the PPG signal.
Proposed template matching techniques differ from each other both in the method of reference signal (template) generation and in the metric used in the pulse-by-pulse comparison. Sukor et al. [ 15] derived a reference template by averaging the individual pulses of a PPG segment. The authors compared all pulses with the template by evaluating the Euclidean distance and the ratio between the amplitudes of the two signals. Acceptability thresholds for the two metrics were determined heuristically. Orphanidou et al. [ 16] and Karlen et al. [ 17] used Pearson’s correlation coefficient as a metric for the Template Matching. Orphanidou et al. derived the reference signal as the average of pulses in a PPG segment and then evaluated the correlation of each pulse with the template. The average correlation coefficient over the segment was then used as a metric for selecting good samples by imposing heuristic thresholds obtained from applying the method to different PPG sensors. Karlen et al., conversely, assessed the quality of each pulse by calculating the correlation between consecutive pulses, imposing a threshold for the correlation coefficient of 0.99. Considering a maximum number of consecutive pulses equal to 10 the assumption is that clean pulses taken from a short time interval are more or less equal to each other, unless they are corrupted with artefacts. Li et al. [ 18] used dynamic time warping (DTW) to match each beat to a template. By calculating the correlation and by using a signal clipping algorithm, the authors derived 4 features, which were used to train a multilayer perceptron with the goal of identifying good and bad-quality pulses. The DTW technique was also used in the study of Papini et al. [ 19]. The authors compared the morphology of PPG pulses with an adaptive template obtained by DTW barycenter averaging several beats, to consider physiological differences among individual pulses. The quality index of each sample was evaluated by taking into account the mean square error of dissimilarities between pulse and template. We recently proposed a PPG pre-processing method that required the generation of an ideal synthetic signal [20]. In particular, 3-s windows of the signal were compared with the reference signal by calculating the correlation coefficient. The use of a synthetic signal allows for very selective sample selection but it has the limitation of not accounting for the morphological variability of the waveform among the subjects.
Several studies used PPG waveform analysis applied to the study of cardiovascular disease. Nayan et al. [ 21] analyzed a set of 20 features extracted from the PPG signal using machine learning approaches for classification between healthy and COVID-19 subjects. The considered characteristics included amplitudes and time intervals of the main morphological features of the waveform: pulse onset, systolic peak, diastolic peak and dichrotic notch. The authors evaluated the performance of different classifiers, such as discriminant analysis (DA), k-nearest neighbour (KNN), decision tree (DT), support vector machine (SVM) and artificial neural network (ANN). The results obtained showed that ANN performed best in discriminating the two classes, achieving $95.45\%$ of accuracy on the test set and $84.62\%$ of accuracy on the validation set. Praveen et al. [ 22] used a feature vector extracted from the PPG signal to train three machine learning models (Random forest, Gradient boost, Xgboost) to classify blood pressure into 4 different stages of hypertension. Other approaches involve the use of deep learning methods to analyze the raw PPG signal, without requiring the process of feature selection and extraction from the data. Paviglianiti et al. [ 23] trained several neural networks to infer arterial blood pressure starting from photoplethysmogram (PPG) and electrocardiogram waveforms, obtaining good results on the estimation of diastolic and systolic pressure. Mahmud et al. [ 24] proposed a new approach for predicting the severity of hypoxia using deep learning applied to the PPG signal. This method is an alternative to the traditional application of the pulse oximeter, which, having a high sensitivity in detecting oxygen degradation, often has a high rate of false positives that could lead to desensitization of healthcare operators.
## 3.1. Data Acquisition
Data acquisition was carried out at S. Giuseppe Hospital in Empoli, Italy. A total of 183 subjects were recruited for the study, including 93 subjects affected by COVID-19 and 90 control, healthy subjects not affected by the target disease. The COVID-19 group included RT-PCR-positive subjects admitted to the hospital with medium to high disease severity, identified by the need for treatment with a high-flow nasal cannula (HFNC) or noninvasive ventilation (NIV). Subjects in the control group were recruited from the hospital’s healthcare staff including healthy subjects not affected by COVID-19 or by other cardiovascular diseases. Only subjects older than 18 years and of white Caucasian ethnicity were included. The inclusion criterion on ethnicity resulted from the fact that as shown in recent studies, many factors can influence the PPG waveform, and among them one of the most significant is the skin color [25,26]. All participants accepted informed consent before being enrolled into the study.
The patient cohort recruited for the study was the same as the one used in the work of Rossi et al. [ 13] except for the number of control patients, which was increased to balance the number of subjects with COVID-19. Among the covid group, $64\%$ of subjects were men and $36\%$ were women, while in the control group, men accounted for $37\%$ of subjects and women for $63\%$. The mean and standard deviation of age were (65.93 ± 17.75) for septic subjects and (43.99 ± 11.16) for control subjects.
For each subject, the protocol consisted of the acquisition of the photoplethysmographic trace using a finger pulse oximeter. The acquisition took place under resting conditions and for a duration of at least 5 min. The measurement site was the index finger of the right hand for all subjects involved. The acquisition system consisted of a finger pulse oximeter connected to the Mindray ePM-10 monitor, commonly used in the hospital for continuous monitoring of patients’ vital parameters. A Raspberry Pi 3 device, connected to the monitor using a network connection and an HL7 (Health level seven) protocol, was used to store the waveforms. Data were acquired with a 60 Hz sampling frequency and stored as standard HL7 messages. In the first decoding step, PPG waveform values were extracted from the HL7 message for each subject. Then the signals were stored with a progressive numerical code so as to eliminate any identifying data that could trace back to the patient.
## 3.2. PPG Quality Assessment
The PPG waveform is susceptible to various forms of noise. Among these, one of the most common is the presence of motion artefacts that distort the shape of the signal. In this study, we developed an algorithm for the evaluation of PPG signal quality based on waveform morphology. A PPG pulse is characterised by a rising phase (anacrotic phase), which represents the systolic phase of the heart, and a falling phase (catacrotic phase), which represents the diastolic phase. A valley, called a dichrotic notch is often present in the catacrotic phase of the waveform, and is associated with aortic valve closure and good arterial function [27]. The use of morphological features to assess PPG signal quality has been widely used in the literature. One of the most common methods is a pulse-by-pulse comparison with a reference signal, which is called Template Matching. In this work, we implemented a Template Matching method by deriving, from each acquisition, a patient-specific reference pulse. This pulse was then compared with the entire PPG signal through the calculation of the Pearson correlation coefficient. The good quality portions of the signal were selected by imposing a threshold for the correlation coefficient.
## 3.2.1. Template Calculation
In our study, each patient-acquired signal was processed to obtain a specific reference pulse. Each PPG acquisition was normalised to have values between −1 and 1, and then the signal was filtered with a Butterworth bandpass filter with cutoff frequencies of 0.5 and 5 Hz [15]. The filtering allowed the preservation of spectral components related to cardiac activity, thus, facilitating subsequent identification of systolic peaks. From the filtered PPG, the lower and upper envelope of the signal were calculated to identify the position of the pulse onset and systolic peaks (Figure 1a).
This allowed the segmentation of each individual pulse of the signal, identified as the waveform between two consecutive onsets. At this stage we provided limits to the pulse duration imposed by natural cardiovascular physiology so that only those peaks that met the physiological limits were considered for template calculation. Specifically, the limits imposed include minimum and maximum values for the systolic phase (SP), that is the rising wave between the pulse onset and the systolic peak, and limits for the pulse wave duration (PWD). The acceptable values for the duration of the systolic phase were in the range of 0.08 to 0.49 s, as described in the study of Fisher et al. [ 28]. The constraints for PWD were calculated, as described in Equation [1], by imposing a minimum mean heart rate of 40 bpm and a maximum mean heart rate of 180 bpm, considering that the subject was in a resting state during acquisition. [ 1]PWDmin=60×FsHRmax;PWDmax=60×FsHRmin With regard to the pulse duration, we also derived a PWD reference value by calculating the median of the width of the pulses. Samples with PWD that differed from the median value of more than $30\%$ were not considered for template calculation. The selected pulses were then aligned on the systolic peak, as shown in Figure 1b. The reference point for alignment was calculated as the mean of the position of the systolic peak of all pulses. To obtain pulses of the same length, we performed truncation of the longer samples and constant-value padding at the beginning or end of the signal for the shorter samples. Once the samples were aligned, we obtained the template by calculating the median of the pulse waveforms (Figure 1c). The implemented algorithm for template calculation is summarised in Figure 2
## 3.2.2. Quality Assessment
Once the reference template was obtained for each patient, signal quality was assessed by calculating the Person’s correlation between the template and each segmented pulse of the patient acquisition. Each pulse was rated of acceptable quality if it correlated with the template equal to or greater than 0.8. The threshold for the correlation coefficient was determined empirically by visual inspection of the waveforms. Therefore, we stored all portions of the signal that contained consecutive pulses labelled as being of good quality. As a result, we obtained PPG samples of varying lengths associated with the same subject. Among these, we only selected for further analysis those samples with a minimum duration of 30 s. The minimum duration of 30 s was chosen experimentally, considering the need to select a waveform of the longest possible duration and the need to have as much data as possible available for training the neural network. As a result of the preprocessing algorithm, we obtained 336 PPG samples. Specifically, 186 samples from 81 patients of the control group, while a total of 150 samples from 84 patients from the covid group.
## 3.3. Dataset Construction
The selected PPG samples were then divided into a training set and test set. As a result of the pre-processing algorithm, multiple PPG samples could be associated with each patient. The division of the available data was done to ensure that data from a specific patient was present in only one of the two sets. The assignment of subjects to the training or test set was done completely randomly. The training set was used for neural network model development, while the test set was used for model performance evaluation. Since the PPG samples can have variable durations, we segmented the test samples to obtain a fixed set of PPG segments on which to perform neural network performance evaluation. Segmentation was performed by deriving for each PPG sample all possible 30-s duration windows from the onset points of individual pulses. The number of samples and the number of subjects in each set of data are reported in Table 1.
## 3.4. Neural Network Architecture
The design, training and testing of the neural network were implemented in Python using the Tensorflow and Keras frameworks. All experiments were conducted on a computer with an Intel i9-11900 2.5 GHz processor and 48 GB RAM within the Microsoft Windows 10 Pro operating system (Lenovo Italy S.R.L., 20054 Milano, Italy). The model structure used in this study is an architecture based on a convolutional neural network (CNN). CNN architectures are made of 3 main layers: the convolution layer, the pooling layer and the fully connected dense layer. The convolution layers and pooling layers compose the first block of the model, which is devoted to featuring extraction from the input data. The last block of the architecture consists of a fully connected network formed by dense layers, and is responsible for associating the extracted features with the desired output. Our custom model consists of 4 feature extraction blocks (CONV Block), each comprising a 1D Convolution layer, ReLu activation and a Max Pooling layer. The first two CONV Blocks have a number of filters equal to 64, while in the last two, the number of filters is 128. All convolution layers have a kernel size of 11 and all Max Pooling layers have a filter width of 4 and stride size of 2. The fully connected network includes a first dense layer with 100 units, followed by a layer with 50 units. For both layers, we included the dropout method with a rate of 0.2 as a regularization strategy to prevent model overfitting. The output layer contains two nodes with softmax activation, as we want to discriminate between two classes. As input, the model takes 30-s PPG segments normalised to have values in the range [−1, +1]. The detailed description of the proposed architecture is shown in Figure 3.
Regarding the complexity of the proposed model, we analyzed some of the most commonly used metrics to assess the complexity of artificial neural networks: the number of trainable parameters, the number of Floating Point Operations (FLOP) and the inference time. As for the first metric, our model has 1,614,532 trainable parameters. With regard to the number of FLOP, this metric represents the total number of calculations (for example, additions or multiplications) that the model has to perform to process an input sample. Each layer of the model involves performing a number of operations that depend on the structure of the layer itself, e.g., the number of FLOP for a one-dimensional convolutional layer depends on the number of filters, the kernel size, the number of input features and the output size. For our architecture, we estimated the number of floating-point operations equal to 236.74 MFLOP using the TensorFlow Python API. Finally, the inference time represents how long it takes to process an input and produce the output. This parameter depends on the available hardware and, in particular, on the number of Floating Point Operations per Second (FLOPS). This measure can be obtained from the CPU specification and, in our case, is 3.2 × 105 MFLOPS. The inference time was then calculated by dividing the number of FLOP required from the model by the number of operations per second supported by the CPU, yielding an inference time of 0.74 ms.
## 3.5. Model Training
When working with neural networks, three sets of data are usually used for training, validation and testing of the model, respectively. At the same time, to evaluate the generalization ability of the model, cross-validation is typically adopted. There are several ways to validate a model, in this case, we adopted 5-fold cross-validation. Validation data were derived from the training set by performing stratified group sampling, where each group contains PPG samples related to a specific patient. In this way, we obtained 5 sets of PPG samples containing data from different subjects, thus, permitting evaluation of the robustness of the method with respect to data variation. In each iteration, 1 of the 5 groups constituted the validation set, and the other 4 were used to train the model. The same architecture, previously described in Figure 3, was used for each cross-validation iteration.
Our architecture takes 30-s PPG segments as input examples. Therefore, a segment of the desired duration was derived from each sample in the training set. The selection of that segment was made during the training process by considering a 30-s window that had as its starting point the onset of one of the individual pulses that constitute the waveform. At each iteration, the selected window was different; thus, the model was trained with many different portions of a signal from the same patient. The range of values assumed by each input data was between −1 and 1.
Regarding the selection of the training hyperparameters, a trial-and-error approach was used, evaluating the trend of the learning curves and the performance obtained by the model on the validation and test set. The investigated parameters were batch size, learning rate, optimiser, loss function and the number of epochs. The chosen parameters for the final version of the model are summarised in Table 2.
Given the limited amount of data available, after validating our method in cross-validation, we re-trained the model using all the data in the training set, assuming to improve the performance due to the utilization of more data.
## 4. Evaluation Results
The model was evaluated in the training phase by considering the average performance obtained on the cross-validation sets and then on the data selected for the test set. In both cases, the evaluated metrics were: accuracy, sensitivity, specificity and precision. Areas under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve were also measured.
The cross-validation process produced 5 different models. Each model differed from the other in the subjects used in training and validation, thus, permitting assessment of the robustness of the method with respect to the physiological variability of the subjects. The average performance of our architecture on the validation sets resulted in an accuracy of $79.01\%$, a sensitivity of $80.02\%$, a specificity of $76.57\%$ and a precision of $74.95\%$.
Then the performance of each model was evaluated on the test set data. All models showed consistent performance on test data, as shown in Table 3. In addition to the average performance of the models, we evaluated an “ensemble” approach, previously used in our other work [29], in which all models were combined in the prediction process. In this method, for each test sample, all models were consulted and the class obtaining the majority of votes was considered as the final prediction.
As we expected, the combined use of the 5 models resulted in an increase in performance over that achieved by a single one. Similarly, we evaluated the performance on the test set after using all the training set data to train the neural network (hold-out validation). The obtained results are summarised by the ROC Curve and the PR Curve shown in Figure 4.
These curves show the ability of a model to classify binary outcomes for each possible cutoff value applied to the classifier’s predictions. Specifically, the ROC curve is generated by plotting a model’s false positive rate against the true positive rate, while the PR curve plots the true positive rate (recall or sensitivity) against the positive predictive value (precision). With a threshold equal to 0.5, our model achieved an accuracy of $83.86\%$, a sensitivity of $84.30\%$, a specificity of $83.45\%$ and a precision of $82.46\%$. The total number of predictions for each class is described in the confusion matrix, shown in Figure 5.
The results obtained in the hold-out validation confirm our hypothesis that more data available for model implementation could lead to improved performance.
The most significant parameter for our study is sensitivity, which identifies the percentage of COVID-19 samples correctly identified. This parameter reached a good value of $84.30\%$. However, since multiple 30-s PPG windows are associated with each subject, to assess the true percentage of correctly classified subjects, we performed the test on the individual patient. In this case, we evaluated the number of correctly identified PPG samples for each patient. Therefore, each subject was considered to be correctly classified if most of his/her signal samples were associated with the right class. In this testing modality, our method correctly classified 25 of the 32 patients assigned to the test set, corresponding to an accuracy of $78\%$, sensitivity of $75\%$ and specificity of $81\%$.
## 5. Discussion
In this study, we evaluated the possibility of using the PPG signal to identify patients infected with COVID-19. Specifically, we presented a new template matching method for PPG signal pre-processing and we developed a CNN deep learning-based model for analyzing the photoplethysmographic signal acquired with a common pulse oximeter. Data acquisition was carried out at S. Giuseppe Hospital in Empoli, using the Mindray multiparameter monitor commonly used in the Intensive Care Unit, thus, simulating a real application of the developed method. The collected data were divided into training set and test set, which were, respectively, used for classifier training and performance evaluation. To assess the robustness of the classifier with respect to variation in the subjects used for performance evaluation, we initially implemented cross-validation and then performed hold-out validation. In the hold-out validation, our model showed good performance in the classification between COVID-19 patients and control subjects by achieving an accuracy of $83.86\%$ and a sensitivity of $84.30\%$ on the test data. Observing the ROC curve related to our classifier (Figure 4), it can be seen that the curve reaches a plateau. This means that as the threshold applied to the model’s predictions increases, there is no corresponding improvement in the performance of the classifier. Therefore, it can be deduced that there are some patients whom the model fails to classify. We can hypothesise that this performance may be due to unimpaired microcirculation in these subjects.
Overall, the obtained results confirmed the presence of microvascular changes due to SARS-CoV-2 infection and the potential of photoplethysmography as a tool for microcirculation assessment. This method, based only on samples of the PPG signal, seems to be suitable for a rapid screening procedure, which could provide the clinician with an early warning signal and allow, for example, the use of specific diagnostic procedures. Moreover, given the noninvasiveness and wide use of this device, especially in the hospital setting, this method could be a tool for the evaluation of microcirculatory changes that does not introduce additional costs. The results obtained allow us to compare the usefulness of using deep learning approaches versus other methods based on feature extraction from the photoplethysmogram. In the study of Nayan et al. [ 21] a set of features extracted from the PPG signal was used to classify COVID-19 patients using Machine learning approaches. Similar to our study, the classifiers were trained by implementing a 5-fold cross-validation on the training data and then evaluated on the test set. In particular, the best performing classifier was a feed-forward multilayer perceptron network, which achieved consistent performance on both the validation set and the test set, in contrast to other classifiers that had significantly lower performance on the validation set. The authors obtained excellent results achieving more than $90\%$ accuracy on the test set and $84.62\%$ accuracy on the validation set. Although our work yielded lower performance than the method described by Nayan et al. we believe it still has advantages. Differently from that study, our method does not require the process of extracting and selecting morphological features from the PPG signal, but processes 30-s windows of the raw PPG signal. This could be particularly advantageous in the case of signals acquired under uncontrolled conditions, such as those acquired from multi-parameter monitors in Intensive Care Units, for which the extraction of PPG features could be challenging. In our previous study, Rossi et al. [ 13], the PPG features were derived by fitting the waveform with a 3-exponential model. The model parameters were then used in ML approaches to identify COVID-19 patients with different severity. Since this study is based on the same data set, the aim of this study is to compare the obtained results with those achieved using the exponential photoplethysmogram model. Given the limited number of subjects enrolled in the study and, consequently, the limited amount of data available for training the neural network, our work focused on the classification between control healthy subjects, indicated as group 0, and covid subjects, regardless of severity, identified as a group [1, 2] in our previous study. In that study, the comparison between group 0 and group [1, 2] was performed in three different ways utilising the Bayesian Classifier with the Leave-One-Subject-Out (LOSO) validation method. The classifier was trained both with features extracted from a single beat and with features averaged over two consecutive beats. The classification of the patient was then obtained based on the majority of the classifications of the single or pairs of cycles. Furthermore, performances were evaluated by considering a single feature vector per patient, obtained by averaging the characteristics over the entire acquisition. The best performance was obtained using the average feature vector, resulting in an accuracy of $70\%$, sensitivity of $68\%$ and specificity of $74\%$ in the classification of subjects. Although the methods are not directly comparable, as they were validated using different methods, we are interested in comparing the performance of the two approaches in terms of correctly classified subjects. In this respect, we can observe that the method proposed in this work, based on a deep learning model, performed better in classifying individual subjects, achieving an accuracy of $78\%$, a sensitivity of $75\%$ and a specificity of $81\%$.
## Study Limitations and Future Developments
Overall, the findings confirm the potential of the proposed method for the early assessment of microcirculation alterations in COVID-19 patients. However, it is necessary to consider some limitations of this work that open the way for further investigation and development of the implemented method. The main limitations are related to the dataset used. The data for training and evaluation of the model were all acquired in the same hospital. To assess the generalization ability of the model, we plan to evaluate its performance on at least one other database. In addition, we hypothesise that a greater number of data available may improve the performance of the model, as well as allow for an evaluation of performance on a larger population. Finally, the two groups of subjects enrolled in the study, although balanced in number, are biased in terms of gender and age. Investigating the influence of these two parameters on the performance of our method will be our further goal. In this regard, we are aware that interpretability of the model is a fundamental requirement for applying this method in the medical field. For this reason, further validations will be necessary to make the model explainable and consequently improve the clinician confidence in using this method. Finally, given the potential shown in this work by the photoplethysmographic technique in the evaluation of microcirculatory alterations, in our future work, we are interested in exploring the use of PPG imaging since optical imaging techniques may also allow a description of the spatial distribution of peripheral blood flow [30,31].
## 6. Conclusions
In this study, we evaluated the possibility of using the PPG signal for the screening and classification of patients with COVID-19. Specifically, we developed a custom convolutional neural network model that discriminates between Covid patients and control subjects by analyzing only the PPG signal. The proposed method achieved interesting results in terms of accuracy ($78\%$), sensitivity ($75\%$) and specificity ($81\%$) on the test set data. Overall this study confirms that PPG signal may be used for the screening of patients with COVID-19 and the assessment of microcirculatory alterations. Moreover, these results are important because acquiring the photoplethysmographic trace is simple, noninvasive and inexpensive. In this regard, this method could be used to develop a user-friendly system that could represent an initial assessment tool for the clinician, applicable even in clinical settings with limited resources. Further studies with a larger sample size of patients and with data from other databases, as well as an evaluation of the interpretability of the model, will be needed to assess the effectiveness of the proposed method.
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|
---
title: 'Development, Implementation, and Process Evaluation of Bukhali: An Intervention
from Preconception to Early Childhood'
authors:
- Catherine E. Draper
- Nomsa Thwala
- Wiedaad Slemming
- Stephen J. Lye
- Shane A. Norris
journal: Global Implementation Research and Applications
year: 2023
pmcid: PMC10007644
doi: 10.1007/s43477-023-00073-8
license: CC BY 4.0
---
# Development, Implementation, and Process Evaluation of Bukhali: An Intervention from Preconception to Early Childhood
## Abstract
The Healthy Life Trajectories Initiative, an international consortium developed in partnership with the World Health Organization, is addressing childhood obesity from a life-course perspective. It hypothesises that an integrated complex intervention from preconception, through pregnancy, infancy and early childhood, will reduce childhood adiposity and non-communicable disease risk, and improve child development. As part of the Healthy Life Trajectories Initiative in South Africa, the Bukhali randomised controlled trial is being conducted with 18–28-year-old women in Soweto, where young women face numerous challenges to their physical and mental health. The aims of this paper were to describe the intervention development process (including adaptations), intervention components, and process evaluation; and to highlight key lessons learned. Intervention materials have been developed according to the life-course stages: preconception (Bukhali), pregnancy (Bukhali Baby), infancy (Bukhali Nana; birth—2 years), and early childhood (Bukhali Mntwana, 2–5 years). The intervention is delivered by community health workers, and includes the provision of health literacy resources, multi-micronutrient supplementation, in-person health screening, services and referral, nutrition risk support, SMS-reminders and telephonic contacts to assist with behaviour change goals. A key adaption is the incorporation of principles of trauma-information care, given the mental health challenges faced by participants. The Bukhali process evaluation is focussing on context, implementation and mechanisms of impact, using a mixed methods approach. Although the completion of the trial is still a number of years away, the documentation of the intervention development process and process evaluation of the trial can provide lessons for the development, implementation, and evaluation of such complex life-course trials.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s43477-023-00073-8.
## Introduction
Childhood obesity remains a global public health concern, given that adiposity in childhood is a risk factor for poorer health trajectories and the development of non-communicable diseases (NCDs), which contribute significantly to the health burdens in low- and middle-income countries (LMICs) (Wang et al., 2016). The importance of healthy weight and reducing NCD risk amongst women preconception has been highlighted, with evidence showing that pre-pregnancy obesity is predictive of obesity in children (Heslehurst et al., 2019; Woo Baidal et al., 2016). The promotion of healthy behaviours to reduce NCD risk in young women of child-bearing age not only has the potential to improve women’s own health but, can also increase the probability of a healthier pregnancy and baby, should they become pregnant (Fleming et al., 2018; Stephenson et al., 2018). This includes the reduction in obesity risk, both of women and their offspring (Wells et al., 2020). However, despite the attention being given to the preconception period, preconception health remains both understudied and under-resourced in LMICs (Dean et al., 2014; Mason et al., 2014). Added to this are concerns regarding the developmental trajectories and outcomes of young children in LMICs (Britto et al., 2017; McCoy et al., 2022). Furthermore, taking a bio-social life-course perspective (Hanson & Aagaard‐Hansen, 2021), investments in the preconception period should be followed up during pregnancy and infancy and into early childhood. However, there are no published trials of a continuum of interventions through these periods of the life-course.
## Healthy Life Trajectories Initiative
In response to these identified gaps in research, the Healthy Life Trajectories Initiative (HeLTI), an international consortium developed in partnership with the World Health Organization (WHO), is addressing childhood obesity from a life-course perspective. HeLTI hypothesises that an integrated complex intervention, comprising a continuum of care from preconception, through pregnancy, infancy and early childhood will reduce childhood adiposity and the risk for NCDs, and improve child development. This hypothesis is being tested in four randomised controlled trials in Canada (Dennis et al., 2021), China (Wu et al., 2021), India (Kumaran et al., 2021), and South Africa (Norris et al., 2022). HeLTI aims to establish a programme of research to generate evidence that will inform national policy and decision making around preconception health as an intervention opportunity. This evidence focusses on optimising young women’s physical and mental health in order to establish healthier trajectories for themselves and future offspring, and to offset health risks, including obesity.
While there have been substantial efforts to harmonise primary and secondary outcomes and measures, and interventions across the four countries, interventions have been developed to be contextually relevant within each setting. One intervention component that was harmonised across the four countries was the incorporation of training in the Healthy Conversation Skills approach for intervention teams. This approach is intended to maximise health care practitioners’ skills to support and empower patients through the process of behaviour change (Barker et al., 2011; Lawrence et al., 2016).
## HeLTI South Africa—Bukhali
As part of HeLTI South Africa, the Bukhali randomised controlled trial is being conducted in Soweto, a densely populated, predominantly low-income, urban, and multilingual setting in Johannesburg. Bukhali means smart or powerful in isiZulu (commonly spoken language in Soweto), with the catchphrase of ‘Living your best life’. Extensive epidemiological research in Soweto has identified young women (18–28 years) as the target population for this trial, given the high proportion ($67\%$) of young women who are overweight or obese during pregnancy (Wrottesley et al., 2020). In addition, the Birth to Thirty cohort study in Soweto showed that if a girl was obese at 5 years old, she had a 42 times greater risk of being an obese adult (Lundeen et al., 2016), and that overweight and obesity increase substantially from childhood ($10\%$ at 8 years) to early adulthood ($43\%$ at 22 years) amongst females (Nyati et al., 2019). Furthermore, unhealthy diet (Sedibe et al., 2014, 2018; Wrottesley et al., 2017), high sedentary behaviour (Micklesfield et al., 2017; Prioreschi et al., 2017), physical inactivity in late adolescence (Hanson et al., 2019) have been identified as risks for NCDs amongst young women in Soweto. The prevalence of antenatal depression ($27\%$) and anxiety ($15\%$) have been noted as additional risks for young women in this setting (Redinger et al., 2018).
Formative qualitative work conducted for HeLTI highlighted the various socioeconomic challenges faced by young women in Soweto, as well as the environmental, social, and structural constraints (e.g. normalisation of obesity, safety concerns) that make it difficult for them to prioritise their own health when making choices regarding their physical activity and dietary behaviour in Soweto. This qualitative work also drew attention to the complex family dynamics experienced by young women in Soweto, and their need for mental health support (Cohen et al., 2020; Draper et al., 2019; Ware et al., 2019). Additional qualitative work affirmed the reality of these challenges, and highlighted the relative low priority of health, and the “preconception knowledge gap” amongst young women in this setting (Bosire et al., 2021; Draper et al., 2020).
Findings from the Bukhali trial pilot data (~ 1600 women aged 18–25 years) have further emphasised how young women in Soweto are exposed to numerous risks in their social and economic environment, including social injustice, gender-based violence and other traumatic events, limited educational and employment opportunities (Ware et al., 2021). Using an adapted social vulnerability index, $27\%$ of the sample were classified as socially vulnerable; this increased to $44\%$ for women with one child, and $64\%$ for women with more than one child (Ware et al., 2021). In this sample of women, $33\%$ of women were classified as food insecure, $20\%$ were at risk of being food insecure, and $44\%$ were overweight/obese (Kehoe et al., 2021). Less than half of the women reported any leisure time physical activity, although $86\%$ of women met physical activity guidelines (based on self-report), but this was typically accumulated through transport- or work-related activity (Prioreschi et al., 2021). Poor quality sleep was reported by a third of the women in this sample, with $19\%$ and $15\%$ were classified as having depression and anxiety respectively, and $24\%$ were at risk for harmful alcohol use (Draper et al., 2022a, 2022b).
## Intervention Development and Implementation
While the trial protocol and intervention strategies have previously been presented, the primary aim of this paper is to more comprehensively describe the following for Bukhali: [1] the intervention development process, [2] intervention components, and [3] the process evaluation. Furthermore, drawing on these descriptions, this paper aims to highlight key lessons learned thus far. We utilised information gathered from investigators and documented processes and study data to detail the intervention.
For all the HeLTI countries, the interventions were developed to address childhood obesity (excess adiposity) and informed by the UK Medical Research Council (MRC) Guidelines for Complex Interventions available at the time of intervention development (Craig et al., 2008), theoretically grounded in behaviour change principles, and set out four objectives: [1] optimise health, [2] optimise nutrition, [3] optimise mental health, and [4] promote early childhood development. The original logic model guiding intervention development is presented in Supplementary Fig. 1 (Norris et al., 2022)(reproduced with permission) and an overview of the intervention is presented in Supplementary Fig. 2, which highlights the community health worker (CHW) approach, as well as adaptations to the intervention (discussed below under “Intervention adaptations”), which are highlighted in red.
The Bukhali trial and intervention details have been published elsewhere (Draper et al., 2022b; Norris et al., 2022), along with the findings and learnings from the pilot implementation of Bukhali (Draper et al., 2020). In brief, the Bukhali intervention is delivered individually to participants by ‘Health Helpers’, equivalent to CHWs in South Africa. The application of relevant constructs from the Consolidated Framework for Implementation Research (CFIR) (Damschroder et al., 2009) to the Bukhali intervention is provided as Supplementary Table 1. A ‘pragmatic attitude’ has been adopted in the trial design to maximise applicability to usual care settings (Treweek & Zwarenstein, 2009), placing it closer to the pragmatic end of the ‘pragmatic-explanatory continuum’ (Thorpe et al., 2009). This pragmatic inclination can help strengthen the external validity of complex intervention evaluations and provide valuable insights on the feasibility of such interventions; but this external validity needs to be balanced with internal validity (Minary et al., 2019). In the Bukhali trial, potential biases that can threaten internal validity (Spieth et al., 2016) are addressed within the trial design (Norris et al., 2022) in order to ultimately strengthen claims about the intervention’s impact. Furthermore, the process evaluation embedded within the trial (described later) help to explicate the mechanisms of this impact (Minary et al., 2019).
The Bukhali trial is being implemented to align with ‘real world’ conditions, to maximise the opportunity to inform policy and practice in the South African public health sector, and to facilitate later community-wide implementation and scale-up of intervention strategies within the primary health care system. For this reason, the Health Helpers share similar qualifications and salary levels to CHWs in South Africa, although they are hired specifically for the trial. All Health Helpers are women between the ages of 23–43 years (mean age = 31.9 ± 5.21; median age = 30.93) (Draper et al., 2022b). Intervention delivery includes the provision of health literacy materials with health screening (obesity, anaemia, depression, diabetes, hypertension), feedback and referral, and services and behaviour change through individual sessions in person or telephonically. These services include free HIV and pregnancy testing, and a free curriculum vitae (CV) printing service. Participants also receive multi-micronutrient supplementation as part of the intervention, and these are delivered by a team of drivers who take the supplements to participants’ homes if they cannot be given in-person by Health Helpers.
## Intervention Materials
Intervention materials have been developed according to the life-course stages: preconception (Bukhali), pregnancy (Bukhali Baby), infancy (birth—2 years), and early childhood (Bukhali Mntwana, ‘child’ in isiZulu, 2–5 years). The content of these materials is outlined in Table 1, and selected images are provided in Supplementary Fig. 3. For each stage, content experts have been engaged to provide input on relevant content, and a graphic designer has assisted with the design of materials. Also, young women test groups were involved to promote greater acceptability and value. For the preconception and pregnancy materials, a specialised health curriculum designer was contracted to compile these materials, and for these phases Health Helpers received a manual, and participants received a resource manual. Table 1Intervention content across the trial phasesPhaseHH resourcesParticipant resourcesHealth checksDietician sessionsFocus and key topicsPreconception—BukhaliHealth Helper’s ManualBukhali Resource BookBMI, BP, Hb, free HIV and pregnancy testingAt least 1 session for obese participantsFocus: Optimising physical and mental health of young women, and build a foundation for early childhood development Body composition and body shapeNon-communicable diseases Healthy eating Physical activity and fitness Sedentary behaviour and screen time Sleep HIV Contraception Sexually transmitted infections Emotional healthPregnancy—Bukhali BabyBukhali Baby Health Helper’s ManualBreastfeeding support materialsBukhali Baby Resource BookBMI, BP, Hb, free HIV testing, ultrasound, GDM screeningAt least 1 session for overweight and obese participantsFocus: Optimising physical and mental health of young women, and build a foundation for nurturing care Healthy and safe pregnancy Partner and family support Care for child development MomConnect SMS service Healthy eating Physical activity Preparing for delivery Accessing Child Support Grant Pregnancy milestones Preparing for breastfeedingBirth—12 monthsCounselling cardsCare for Child Development resourcesRoad to Health BookEarly years movement guidelinesBMI, BP, Hb, free HIV and pregnancy testingAt least 1 session for obese participants (mother)Focus: Promoting nurturing care and establishing a healthy growth and development trajectory for the child Road to Health Book pillars: Good nutrition to grow and be healthy Love, play and talk for healthy development Protection from preventable childhood diseases and injuries Healthcare for sick children Special care for children who need extra care Healthy movement behaviours: Physical activity Sedentary behaviour Screen time Sleep12–24 monthsHealth Helper’s ManualRoad to Health BookEarly years movement guidelinesRoad to Health BookEarly years movement guidelinesBMI, BP, Hb, free HIV and pregnancy testingAt least 1 session for obese participants (mother)Focus: Optimising the healthy growth and development of the child Road to Health Book pillars Includes feeding guidelines and responsive feeding support Healthy movement behaviours24–60 months—Bukhali MntwanaHealth Helper’s ManualRoad to Health BookEarly years movement guidelinesRoad to Health BookEarly years movement guidelinesGrowth chartDiary sheets: screen time, food, sleep, beveragesActivity handoutsMother:BMI, BP, Hb, free HIV and pregnancy testingChild:Height, weight, developmental milestone checksAt least 1 session for overweight and obese participants (child)Focus: Optimising the body composition and development of the child (transition to obesity prevention focus with the child) Child development: fine and gross motor, cognitive, social emotional, numeracy, language and literacy Child growth: monitoring height and weight Child healthy behaviours: physical activity, sedentary behaviour, screen time, sleep, dietary behaviour Maternal well-being: self-awareness, emotional awareness, stress and coping, self-care, values Activity @ home: encouraging connection and responsive caregiving Continued alignment with Road to Health Book pillars, e.g. immunisations, deworming, feeding, illnessHH health helper, BMI body mass index, BP blood pressure, Hb iron, HIV human immunodeficiency virus, SMS short message service The infancy intervention materials were informed by the South African child health record. the Road to Health Book (South African National Department of Health, n.d.-a), the WHO/UNICEF ‘Caring for the child’s healthy growth and development’ materials and the Nurturing Care Framework (World Health Organization et al., 2018). Additional components related to support for responsive caregiving and responsive feeding based on the WHO/UNICEF Care for Child Development (World Health Organization & UNICEF, 2012) approach and nationally developed Side-by-Side materials (South African National Department of Health, n.d.-b), were added to the intervention package for children up to the age of two. Health Helpers received a manual, and counselling cards, structured around the Road to Health Book knowledge pillars (nutrition, love, protection, healthcare, and extra care), that guide them through their interactions with mothers and their children.
All the materials have been developed as the trial has progressed, which has enabled a flexible and somewhat iterative approach in terms of adjusting the content and format of the materials for each new stage in response to feedback from the intervention team and participants. Within this flexibility, structure is provided to the sessions in terms of key outcomes to be addressed through the provision of ‘cue cards’ to assist with risk screening, management and referral, and to prompt Health Helpers in their interactions with participants. These follow a ‘traffic light’ system to provide guidance, with green indicating normal range or no risk, orange indicating some or elevated risk, and red indicating high risk and prompting an appropriate referral. An example of these cue cards for blood pressure, iron (Hb), diabetes (HbA1C), body mass index (BMI), and knowledge of human immunodeficiency virus (HIV) status in the preconception stage is provided in Supplementary Fig. 4.
## Control Arm
The control arm components were intended as standard of care ‘plus’ offered through the public health system, assuming that standard of care would be insufficient to engage participants for the full length of the trial (5–7 years if they became pregnant). These components are delivered telephonically by a trained call centre team, with 6-monthly in-person visits to promote contact and reduce attrition. Control participants are also offered free HIV and pregnancy testing, and the same CV printed service as intervention participants. The control component covers non-health specific topics relevant to young women, in order not to contaminate the intervention, but attempts to partially control for the attention given to intervention participants (Norris et al., 2022). Qualitative findings, thus, far from the process evaluation (discussed further below) indicate that this attention is generally well received by control participants.
## Intervention Dosage
Supplementary Figure 5 outlines the dosage of the components of the intervention and control arms (combining birth—60 months into ‘early childhood’). This figure provides details for the: number of health literacy resources provided; allocations of multi-micronutrient supplement (per month, intervention only); in-person sessions, including health checks for intervention (including free HIV and pregnancy testing, CV printing) and services offered for control (only free HIV and pregnancy testing, CV printing); and telephonic contacts. The in-person visits ideally take place every 6 months and these have proved to be crucial to promote adherence and engagement of both intervention and control participants. For intervention participants, these in-person visits are a valuable opportunity to reinforce (and repeat, if necessary) health literacy messages and support provided by Health Helpers.
## Intervention Adaptations
With regard to the various adaptations that have been made to intervention delivery (intervention arm) over the course of the trial, first, the preconception sessions were originally designed to be facilitated in community-based peer group sessions (facilitated by a Health Helper), but these proved not to be feasible in the pilot phase (Draper et al., 2020). Second, delivering in-person sessions in participants’ homes also proved not to be feasible, and individual session shifted to be largely telephonic. This happened in early 2020, which then enabled the intervention team to pivot more quickly during the early stages of the COVID-19 pandemic, when in-person sessions were not possible. The supplementation component of the intervention was allowed to continue during COVID-19 lockdowns. Third, adaptations to the implementation of Health Conversation Skills have had to be made, after finding that the approach could not be delivered as intended in this trial setting, and that contextual adaptations to the approach were necessary. These include simplifying the goal setting planning tool, adaptations for multi-lingual settings and where literacy levels may be low, adapting training to an ongoing trial setting, and incorporating a trauma-informed perspective to health behaviour change (Draper et al., 2022b). Where possible, these adaptations have been incorporated into the delivery of the preconception, pregnancy, and infancy components, and specifically into the intervention materials for the early childhood phase.
Fourthly, in mid-2021, a registered dietician joined the intervention team to provide additional nutritional support for participants who were overweight or obese (especially during pregnancy). These nutrition support sessions were also offered in-person and telephonically, and further details regarding the evaluation of this nutritional support are provided below as part of the process evaluation. Lastly, in 2021, based on the learnings and experience of the intervention team regarding the extent and salience of mental health challenges experienced by participants, along with findings from the process evaluation (Draper et al., 2022b), specific attention was directed towards understanding trauma experienced by participants, and by the Health Helpers as well. This was done to ultimately identify ways in which mental health support could be provided for participants and Health Helpers. Two discussion sessions (~ 2 h in total) about trauma was facilitated with the intervention team (Health Helpers, dietician, and drivers), key issues from this discussion, and steps taken in the trial to further address mental health challenges and trauma are explained below (including recruitment of a social worker on the trial, discussed below under ‘Solutions’), with selected quotes to illustrate these issues in Table 2.Table 2Intervention team’s perspectives on trauma in the Bukhali trialDefinition of Trauma“I think it’s something that is deep, that is embarrassing you, that you can’t even explain it, something that is deep and eating you inside, even when you explain it to another person, you feel that the person does not know what you are talking about, that is how deep it is, that you can’t even explain it”“…it’s something that causes you bitter…experience that is emotional that you cannot exactly move on, you keep going back to it, it’s something that you are stuck with”“…it’s painful and deep…it affects our emotions as well and then it makes you bitter, you can’t even allow things flowing in your mind”“It’s inside of you, it’s you actually…it’s inside and it’s killing you and it’s not good…it’s broken your mind, when you think about it…when you want to go on, you cannot go on”“Your character, your behaviour and character, it’s like it’s changing it. I think that is why you have that bitterness, stress, and you are angry”Experiences of Trauma“As Africans, our background are differ from persons to persons, so some of the things, you find that you think they are normal, you understand, because when you grew up, the man of the house next door was beating the women, you understand, so it’s normal, so, but as time goes by, you find that no, what was happening, it is affecting you as well, as you are growing up”“I think we are not taught that’s it’s trauma…so as long as you don’t see it as a problem, you deny it, almost, so you go through life just in denial”“…people think that other people’s traumas are bigger than yours, and people have bigger problems than you, and you are getting eaten alive bit by bit”“Don’t cry… I think our culture taught us that we must not take trauma very serious, we must take trauma seriously if we say [name] was raped, wow, that’s traumatic, and if they point a gun at you, at least you are still alive…come on, two guns, that is traumatic, that is a trauma, and they say, yeah, we know, but at least she is still alive”“Sometimes people don’t identify your trauma, and in most cases, society has always taught you, be positive, so when this has happened, be positive”“For black people I don’t think so, sorry for saying black people, but I think that we come from different backgrounds, like just growing up, it’s not, they don’t teach you with the intention that this is how you deal with trauma, they don’t, you just learn along the way, you have to just move on from it, you can’t stay in that space forever, a lot of people, especially us blacks, a lot of people grew up in situations where you have, you have uncles that were going insane every night, and when you go outside in your underwear and they are chasing you around, so these are things you just grow up with and you leant to get through, because no one has taught you, you actually have to deal with this…you grow up with resentment towards certain people or certain situations because you haven’t dealt with it”“You are never taught, that okay, this which I am going through, it’s not okay to cope with things in this manner, you are never taught, no one ever goes, you know what, talk it out, yell it out, cry, scream…”“we always know that we have to be strong for our families…I’m the eldest at home, I know that I always have to be strong, no matter what the situation, I cannot just leave out of the blue, everybody will die…it’s killing me, but there are people that I have to look out for”“It’s seriously not okay, not to attend to trauma, because I think it slightly kills you inside, because it builds anger”“you end up lashing out, releasing it on other people”Participants’ Trauma“I always feel bad…the reason why I feel bad is because you cannot help that woman…I don’t know how to put it, but it doesn’t feel good to know that I cannot go this route with you, I want to, I really want to go with you, but I just cannot, sometimes interventions close me up in this seat that I am in, yes, I want to go over the chair and do something with you, but I can’t, so for me, I wish we can open up an NGO”“I think for me, I think our participants need people that they can open up to, people who can then, after opening up, to get assistance, because as I always say to you, it’s very painful to open up a wound and not be able to help the person heal the wound”“I think also, and also, there are people, they are just like use, the participants, they have their own traumas, as a result, we don’t care, we deal about these things every day, I mean grow up, these things happen in life, so just learn to deal with it, so I think also, the health system, it, I don’t know it it’s how they are trained, or it’s how these things are taught, even at school, university levels, that yeah, this is how life should go, so I just don’t know”“And then now I have to go back to work, wipe my tears, be calm and then listen to other people’s problems and be like, okay yes, okay, healthy eating, okay yes, stress, okay”Solutions“…I don’t trust you [referring to psychologist] so much, you are not so connected to me, that I must trust you. So the thing, what is happening with participants is that they are not so formal, but when they come here, they feel so comfortable”“…instead of just talking, because talking sometimes is just not enough. So I think with the counselling thing, they need to know that they [counsellor] have gone through it and they have dealt with it on their own, can they relate to the type of people here in South Africa”“Not having an outlet, not having a trust environment, it’s very important because it’s one thing for a person to go, here’s a number, I understand completely, Health Helpers are not trained to be counsellors, but you talk to someone for 18 months, you go through them, their life and everything, and then at the end of the day, here is a number, call this number, and then that person does not have 2 s for you, or in the case where you mentioned who had mental health issues, and then the nurse goes no…[You are talking too much.] Exactly”“I think having a social worker onsite will actually be a good idea because from the little that I have seen coming, like working here is that people tend to trust us more than they would actually trust that referral that we give them… I feel like they would actually come here and have that session done than go anywhere else because Bukhali has built trust with them from when they were recruited…”“..that’s why we are saying face to face is much better because how do you trust someone, you are just calling, you going to say it’s [name], you don’t know her or who the friends are, who they are going to tell about your problems. I think so many issues around trust, is the other reason why they don’t call”“…she is crying, so I explain to her, should I refer so that you can speak to someone, and then she says no, I don’t want to talk to other people, I want to talk to you. So I tell her that I am not a counsellor and all of that, and then she says that then we should not continue with it, you see. So I think that if there is a person onsite, I don’t know what else we can do, but a person that is going to be here, when I take her to that person, she will have the trust that okay, if I trust her, then I can trust the other one as well”Intervention team includes Health Helpers, dietician and drivers
## Participants’ Definition and Experiences of Trauma
Trauma was understood and resonated as a concept but was defined in more experiential and emotional terms, rather than drawing on a clinical or academic definition. The stigma of mental health was acknowledged, and linked by some to culture. They felt that mental health and trauma were not taken seriously in their context, and their experience was that trauma was normalised in their context, although they could see it from a different perspective looking back on the trauma in their upbringing. They explained how trauma was often minimised in their context, e.g. being told that other people have more trauma, and that they should be positive. Many felt the expectation to be “strong” while experiencing trauma, particularly for their family, especially if they were an older or oldest sibling, or they were the head of the household (including for women). Many spoke of not being taught how to deal with trauma, and not knowing how to cope. Unhealthy ways of dealing with trauma mentioned included “blocking off”, denial, hiding how they felt from others, or convincing themselves that “I can handle this”. Healthy ways of dealing mentioned included self-awareness, and talking about their experiences. The consequences of not dealing with it in a healthy way mentioned were anger, lashing out at others, and depression.
Health Helpers and the dietician were all able to easily recount stories of trauma disclosed to them by participants, and some noted how the content of the intervention (e.g. healthy diet) often do not align with the reality of participants’ circumstances. This leaves them feeling sad, “bad”, and helpless because they are aware that they cannot change a participants’ situation. They find it hard to keep a clear line between personal and professional when dealing with participants, and mentioned that participants sometimes trigger their own trauma. Encouragingly, it was expressed that Health Helpers are seen as a trusted source of support, and that participants are more comfortable opening up to them, often making them the first person to hear of traumatic events experienced by participants.
## Participants’ Recommended Solutions
There was agreement that the local referral systems do not work for participants with mental health challenges and/or trauma. The extent to which these referrals can be relied upon was a key issue. Also, phone consultations (often offered by non-governmental organisations) were not perceived to work. They were not convinced of the value of counselling and therapy in their setting, as they mentioned that talking is not enough in their context, since it does not change the situation causing the challenges and/or trauma for participants. However, they agreed that an onsite counsellor is needed for the trial, but they stressed the importance of the counsellor (or psychologist, social worker etc.) needs to be able to relate to participants, and that they should be trustworthy. They believed that Bukhali (and by extension, the research centre) was perceived to be trustworthy by participants, and that this trust could be extended to someone offering services onsite, and therefore, as part of Bukhali.
## Actions Taken
Following on from these discussions, in March 2022, steps have been taken to provide additional mental health support for Health Helpers in their role. This support will incorporate principals of trauma-informed care, taking into consideration that experiences of trauma can negatively influence behaviour change (Marks et al., 2021). The provision of onsite counselling is also being investigated, bearing in mind that such services need to be feasible, accessible and sustainable. The need for such services has emerged from the experiences of the intervention team who have experienced challenges with referring high-risk participants to over-subscribed and poorly mental health services in the public sector. While there are some free, non-governmental mental health services available for participants in Soweto, these often are delivered telephonically and/or online, and are difficult for young women to access.
## Process Evaluation
Following guidance from the UK MRC on process evaluation (Moore et al., 2015), the Bukhali process evaluation is focussing on context, implementation, and mechanisms of impact, using a mixed methods approach. Regarding context, this refers to participants’ family, community, and social environment, as well as their socioeconomic circumstances. Of particular interest are the life circumstances and lived experiences, and how this might influence their experience of the trial, their understanding of health and health behaviours, and their agency regarding their health and health behaviours. Contextual factors that have been identified thus far include logistical challenges of participating in the trial, exposure to trauma, community influences on perceptions and beliefs, social support and the role of families and partners, and other socio-ecological factors influencing their decision making. Ultimately, we hope to understand how these, and other emergent, contextual factors influence how the intervention is received, implemented, and whether it is effective at changing specified outcomes. Of interest as well is how the intervention might influence the context, especially in the long term. For example, does participation in the trial shape the home environment, or the participants’ interpersonal relationships?
In terms of implementation, key questions and considerations include how delivery is achieved and supported, the fidelity of delivery, the dose of delivery, intervention adaptations, and reach of the intervention. Most of these have been discussed above; intervention reach and dosage are captured on the HeLTI data management system, REDCap (Harris et al., 2009, 2019). Regarding fidelity, a protocol for monitoring fidelity has been developed, drawing on the NIH Behaviour Change Consortium Treatment Fidelity Framework. While this framework was found to provide a good foundation for reporting intervention fidelity, some context-specific challenges were identified with applying this framework in a LMIC setting (Soepnel et al., 2022). In particular, two fidelity components, intervention receipt and enactment, were identified as difficult to uphold and monitor in this setting, given that they are centred around the trial participants rather than the individuals delivering the intervention. Intervention receipt refers to the extent to which participants understand and perform cognitive strategies and skills addressed by the intervention during delivery. Intervention enactment refers to the extent to which these strategies skills can be transferred to real-life situations (Soepnel et al., 2022).
With respect to mechanisms of impact, details regarding the pathway to impact are presented in the Bukhali logic model (Supplementary Fig. 1) as well as the intervention overview (Supplementary Fig. 2). Specifically, these include the combination of multiple health screenings, referral and management, health literacy, social and behaviour change support, and multi-micronutrient supplementation delivered by Health Helpers. Furthermore, the process evaluation is capturing data on how participants responding to and interacting with the intervention (including Health Helpers, dietician, social worker), how the intervention is working (including unexpected ways in which it is working), and any unexpected consequences of the intervention (and the control arm). Supplementary Table 2 provides details of the completed, ongoing, in progress, and planned process evaluation activities, along with the aim, methods and status of each activity, and process evaluation framework component/s that apply to the activities.
## Key Lessons Learned
The contextualisation of behavioural changes in young women’s lived experiences in Soweto has been a critical learning so far in the Bukhali trial. Furthermore, mental health has emerged as a more salient priority than physical health, and the role of trauma is becoming explicitly acknowledged and dealt with in the trial. Intervention delivery is also no longer only focussed on shifting health behaviours, but encompasses the broader role of support that Health Helpers—as CHWs—can offer young women over the sustained period of a multi-stage trial. An additional learning relating to context is that the salience of certain contextual realities may only emerge as a trial progresses, which can impact not only on trial outcomes (e.g. behaviour change), but also on ongoing recruitment and retention of participants. This can also apply to various approaches and frameworks used in implementation and evaluation, which may need contextual adaptations to be used in a way that is most relevant and helpful.
Another key area of learning in the Bukhali trial has been the balance of external and internal validity, in terms of maintaining the required flexibility and making necessary adaptations, and a rigorous trial design. The existence of these on a continuum in complex interventions has been well captured by Minary et al [2019], who intersect this continuum with the continuum of process, mechanisms and effects, and the continuum of efficacy studies and implementation research. They acknowledge that this framework need not be rigidly applied, and that a range of methods may be appropriate for the evaluation of complex interventions (Minary et al., 2019). In light of other learnings mentioned above, the added complication of differing contexts lends further weight to the argument against rigid application of such a framework.
## Conclusions
In South Africa, HeLTI provides a unique opportunity to apply a bio-social life-course perspective to intervening with young women and children to promote the establishment of healthy trajectories and offset childhood obesity. Although the completion of the trial is still a number of years away, from an implementation science perspective, the ongoing documentation (and publication) of the intervention development process and process evaluation of the trial can provide short-term learning and benefits in terms of the development, implementation, and evaluation of the Bukhali trial. The application of a framework such as the CFIR helps evaluate and understand what works where and why, which can facilitate learning across contexts (Damschroder et al., 2009); this could have relevance for similar trials in other LMIC settings. Furthermore, applying an implementation science lens to the Bukhali trial, currently focussed on evaluating efficacy (phase 2 trial), provides a critical foundation for a future focus on effectiveness (phase 3 trial). When the effectiveness of the Bukhali intervention is evaluated within the public health system, implementation will be critical area for investigation.
Although the evaluation of the implementation of the Bukhali trial is ongoing, a limitation of this evaluation is that it is unlikely that we are fully capturing the complexity of the intervention, since we are not able to evaluate all components of the intervention implementation at the same level of detail. Related to this, we have selected certain aspects of implementation on which to focus our evaluation efforts, based on the apparent salience of these aspects of implementation. The long-term nature of the trial (and subsequent blinding) makes it difficult to continually compare the results of the outcome evaluation with the evaluation of the implementation of the intervention.
Despite these limitations, lessons regarding the trial complexity are relevant for trialists, especially for those working across multiple stages of the life course. Our findings, thus, far underscore the critical role of process evaluation, and particularly the inclusion of qualitative methods to explore lived experiences of participants, as well as the more nuanced aspects of context, mechanisms of impact and implementation. Furthermore, the pragmatic attitude adopted in this trial, and the use of process evaluation findings throughout the trial process can provide ongoing insights into the ways in which intervention strategies adopting a CHW approach can strengthen health systems. This is highly relevant in LMIC settings where public sector systems are overburdened and fragile, and where CHWs are a more feasible and acceptable approach for optimising the physical and mental health of young women at community level.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (PDF 34 KB)Supplementary file2 (PDF 704 KB)Supplementary file3 (PPTX 2802 KB)Supplementary file4 (PPTX 486 KB)Supplementary file5 (PDF 1028 KB)Supplementary file6 (DOCX 23 KB)Supplementary file7 (DOCX 22 KB)
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|
---
title: 'Health equity related challenges and experiences during the rapid implementation
of virtual care during COVID-19: a multiple case study'
authors:
- Simone Shahid
- Sophie Hogeveen
- Philina Sky
- Shivani Chandra
- Suman Budhwani
- Ryan de Silva
- R. Sacha Bhatia
- Emily Seto
- James Shaw
journal: International Journal for Equity in Health
year: 2023
pmcid: PMC10007658
doi: 10.1186/s12939-023-01849-y
license: CC BY 4.0
---
# Health equity related challenges and experiences during the rapid implementation of virtual care during COVID-19: a multiple case study
## Abstract
### Background
Virtual care quickly became of crucial importance to health systems around the world during the COVID-19 pandemic. Despite the potential of virtual care to enhance access for some communities, the scale and pace at which services were virtualized did not leave many organizations with sufficient time and resources to ensure optimal and equitable delivery of care for everyone. The objective of this paper is to outline the experiences of health care organizations rapidly implementing virtual care during the first wave of the COVID-19 pandemic and examine whether and how health equity was considered.
### Methods
We used an exploratory, multiple case study approach involving four health and social service organizations providing virtual care services to structurally marginalized communities in the province of Ontario, Canada. We conducted semi-structured qualitative interviews with providers, managers, and patients to understand the challenges experienced by organizations and the strategies put in place to support health equity during the rapid virtualization of care. Thirty-eight interviews were thematically analyzed using rapid analytic techniques.
### Results
Organizations experienced challenges related to infrastructure availability, digital health literacy, culturally appropriate approaches, capacity for health equity, and virtual care suitability. Strategies to support health equity included the provision of blended models of care, creation of volunteer and staff support teams, participation in community engagement and outreach, and securement of infrastructure for clients. We put our findings into the context of an existing framework conceptualizing access to health care and expand on what this means for equitable access to virtual care for structurally marginalized communities.
### Conclusion
This paper highlights the need to pay greater attention to the role of health equity in virtual care delivery and situate that conversation around existing inequitable structures in the health care system that are perpetuated when delivering care virtually. An equitable and sustainable approach to virtual care delivery will require applying an intersectionality lens on the strategies and solutions needed to address existing inequities in the system.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12939-023-01849-y.
## Background
Virtual care quickly became of crucial importance to health systems around the world during the COVID-19 pandemic, and its increased use is likely to persist in health care delivery following the end of COVID-19 public health measures. In part because many complex issues must be considered when implementing virtual care such as patient privacy, reimbursement models, new workflows, and technology procurement, the challenge of promoting health equity in uses of virtual care is often overlooked [1]. This problem was made worse during the COVID-19 pandemic, where organizations were forced to implement virtual care in rapid time frames with little support [2]. In this paper we examine whether and how health equity was considered by four health and social service organizations while rapidly implementing virtual care during the first wave (January 2020 to July 2020) of the COVID-19 pandemic in Ontario, Canada, and generate insights for strategies to promote health equity in uses of virtual care.
We define virtual care as “any interaction between patients and/or members of their circle of care, occurring remotely, using any forms of communication or information technologies, with the aim of facilitating or maximizing the quality and effectiveness of patient care” [3, 4]. This involves using information and communication technologies to exchange information during the diagnosis, management, and prevention of disease and illness [5]. Used in complement with or in lieu of in-person encounters, virtual care encompasses a wide array of modalities including phone calls, video conferencing, remote monitoring, asynchronous messaging (e.g., email, texting) and the use of a patient portal. Virtual care also goes by a variety of different terms, such as eHealth, telemedicine, teleconsultation, telecare, and telehealth. The idea of leveraging digital health and virtual care technologies to alleviate existing health system issues have been well documented in the literature [6–8]. Digital health technologies have the potential to increase access to and improve quality of care, lead to time and cost savings [6, 9], and contribute to a more efficient health care system [10]. However, despite the potential of virtual care to enhance access for some communities, health informatics and digital health research has documented risks associated with virtual care initiatives pertaining to health equity [1, 10–13]. In some cases, virtual care initiatives have contributed to widening disparities between structurally marginalized communities and more privileged communities, indicating that the former is less likely to benefit from some virtual care initiatives while the latter is more likely to benefit [11, 13, 14].
In Canada, like many countries around the world, health systems do not serve everyone equitably; this is evidenced by the different access to and outcomes of health care experienced by various population groups [15–18]. Inequities in health status can be attributed to many factors, much of which surface as a result of the historical and ongoing manifestations of colonialism, neoliberalism, and white supremacy [15]. Historical practices of the segregation of Indigenous peoples from the larger population, provision of substandard care to racialized communities, and contemporary experiences of prejudice and discrimination due to health care racism have led to lingering feelings of distrust in health care providers and the larger health care system [19–21]. The impact of social and structural processes of marginalization (e.g., white supremacy, settler colonialism, systemic racism, ableism, ageism, etc.) facilitate control over, exploitation of, and harm to disadvantaged groups and individuals [22]. In recognition of the influence of these intersecting systems that confer advantage and disadvantage to particular communities with respect to health care, we refer to communities characterized by relative disadvantage as structurally marginalized by health care systems.
Health inequities become evident in relation to virtual care in a variety of ways. For example, access to a connected digital device is essential to engage in virtual care. However, cost-related challenges to acquiring digital devices and the internet exist across many communities including older adults [23, 24] and racialized groups [23, 25, 26]. In remote and rural communities, those who do not experience cost-related challenges might still struggle with infrastructural issues such as limited internet access and connectivity issues [9, 27]. Many communities, including Indigenous communities, contend with a combination of geographic remoteness and inadequate infrastructure and/or resources to engage in virtual visits [9, 23].
Communities also need to have the appropriate skills to engage in virtual care. Digital health literacy involves the knowledge of how to use a specific technology for a health care purpose and the confidence to act on that knowledge in order to participate actively, regularly, and comfortably in virtual appointments [13]. For example, some research shows that older adults who are less educated and live alone struggle more with using digital health technologies compared to other groups [23]. Furthermore, not all technology has incorporated the appropriate design elements to promote ease of access for those with physical, visual, auditory, and cognitive impairments – further contributing to lower levels of virtual care usage among some groups [23, 24].
While the barriers for structurally marginalized communities to access and receive high quality virtual care have been widely documented in the literature, the rapid introduction of widespread virtual care services into mainstream care delivery at the onset of the COVID-19 pandemic was a novel event worldwide. The scale and pace at which services were virtualized did not leave many organizations with sufficient time and resources to ensure optimal and equitable delivery of care for everyone. The strategies that organizations employed to ensure equitable access to virtual care during the pandemic remain unclear. The objective of this paper is todocument the challenges in considering health equity during the rapid implementation of virtual care in Ontario, Canada during COVID-19,document the strategies implemented to mitigate challenges, andexplore how access was hindered or achieved based on the results.
## Study design
This study followed an exploratory, multiple case study approach drawing on the methodological guidance of Yin [28]. Exploratory case studies are used to gain in-depth descriptions of social phenomenon and allow for comparisons across cases. While variations on case study approaches exist, exploratory case studies seek to answer ‘what’ and ‘how’ questions about the phenomenon of interest. In order to situate the position of the researchers in relation to the research project, we are a diverse group of authors committed to acknowledging and countering systemic inequities embedded in our health care system. We are approaching this work from the perspective of how organizations have responded to rapid changes in care delivery with the intent of informing changes to more equitable health care. For more on our positionality, please see the authors’ information.
## Study setting
We completed exploratory case studies of health and social service organizations that rapidly implemented virtual care during the initial onset of the COVID-19 pandemic in Canada across multiple care settings in Ontario (Table 1). We defined our case studies as a health or social care program, identified by one or more health or social care providers delivering a specific service to a particular population of patients or clients, that had historically been delivered via face-to-face visits and had been rendered virtual in response to the COVID-19 pandemic. We used a maximal variation sampling strategy to recruit four cases across the continuum of care that had varying population densities, including urban hospital-based mental health services, rural community support services, urban home care, and rural primary care. A maximum variation sampling approach enabled us to document the unique features of each case as well as the shared patterns across cases. Table 1Description of case study organizationsCaseDescriptionCommunities ServedHospital-based Mental Health ServicesAn acute care hospital located in an urban centre in Southern Ontario, providing outpatient mental health services by phone and video visits. The organization of which the mental health clinic was a part had made large investments in virtual care in the year prior to the COVID-19 pandemic, and yet the clinic had not implemented virtual visits on such a large scale in advance of the pandemic. The clinic implemented virtual care rapidly with the onset of the pandemic. Adults requiring mental health servicesCommunity Support ServicesCommunity support services (e.g., meal delivery, caregiver respite, social stimulation activities, etc) offered to multiple townships in rural Ontario. Staff implemented a standardized surveillance instrument conducted over the phone to identify and triage at-risk clients and connect them to the appropriate health or social care service. They implemented a teleconferencing service for patients due to an initial lack of internet accessibility in the office, then introduced videoconferencing shortly afterwards. The volume of patients using virtual care decreased during summer months of 2020.Rural communities and older adultsHome CareA home care agency offering a wide variety of home healthcare services in urban centres across Southern Ontario (e.g., physiotherapy, occupational therapy, speech language pathology, nursing, etc). Phone and video visits were authorized for clinical needs related to wellness and health checks, monitoring of conditions/symptoms, remote clinical consultation or intervention related to client care plan goals and support for assessment and reassessment of treatment plans. A virtual care strategy was implemented across all regions within the first 2–3 weeks of the COVID-19 pandemic (March–April 2020). There was an initial dip in the total volume of patients, followed by a return to normal volumes when services were virtualized. Older adults, individuals with low income, and people experiencing homelessnessPrimary CarePrimary care services offered through an Aboriginal Health Access Centre located in Northern Ontario. Services were virtualized primarily through telephone visits and were provided to neighbouring First Nations communities. Providers quickly transitioned to conducting virtual visits from their home. After some feedback about their approach, providers then switched to conducting virtual visits from the community clinics located in First Nations communities. They resumed in-person visits in June 2020 and majority of visits switched back to in-person visits. First Nations communities**** Indigenous Peoples in Canada are comprised of First Nations, Inuit, and Métis. This case study included members of some First Nations communities in Northern Ontario.
Table 1 provides an overview of the case studies in this project, including a description of their services and communities served.
## Participant recruitment
We recruited participants through a single study contact (gatekeeper) at each participating organization. Gatekeepers were asked to consider patients who were scheduled for appointments in the past three months. Client participants were included if they were high users of virtual care (those who accepted and used the technology frequently without any problems) and low users (those who struggled with the technology regardless of frequency of use, including those who have declined a virtual visit or those who chose a phone visit over a video visit). Clients were further included to reflect a diverse sample that could offer a variety of perspectives across different characteristics, including multiple gender identities (provide representation of men and women in each case and those who identify outside of the gender identities of man or woman), age (those over the age of 65 and those under the age of 65), and diverse racial and cultural groups (a member of a racialized community, identified by being a person of colour). We also recruited organizational leaders, managers, and providers who were employed at one of the four organizations. A maximum variation sampling approach permitted us to recruit participants with a diversity of perspectives across different identity characteristics such as age, education level, income, and geography (Table 2).Table 2Participant demographic characteristicsCare ContextCharacteristicsPrimary CareHome CareCommunity Support ServicesHospital-based Mental Health ServicesParticipant Type Patient4 ($35.4\%$)3 ($30.0\%$)3 ($33.3\%$)3 ($33.3\%$) Health Care Provider3 ($27.3\%$)3 ($30.0\%$)5 ($55.6\%$)4 ($44.4\%$) Manager3 ($27.3\%$)3 ($30.0\%$) Organizational Leader1 ($9.1\%$)1 ($10.0\%$)1 ($11.1\%$)2 ($22.2\%$)Age 0–558 ($72.7\%$)6 ($60.0\%$)6 ($66.7\%$)2 ($22.2\%$) 56+1 ($9.1\%$)4 ($40.0\%$)3 ($33.3\%$)1 ($11.1\%$) Unknown2 ($18.2\%$)6 ($66.7\%$)Gender Male3 ($27.3\%$)2 ($20.0\%$)1 ($11.1\%$)1 ($11.1\%$) Female7 ($63.6\%$)8 ($80.0\%$)8 ($88.9\%$)2 ($22.2\%$) Unknown1 ($9.1\%$)6 ($66.7\%$)Racial Group White3 ($27.3\%$)8 ($80.0\%$)8 ($88.9\%$)3 ($33.3\%$) Indigenous6 ($52.6\%$) Mixed or other1 ($9.1\%$)1 ($10.0\%$)1 ($1.1\%$) Unknown1 ($9.1\%$)1 ($10.0\%$)6 ($66.7\%$)Education Level High School College3 ($27.3\%$)1 ($10.0\%$)6 ($66.7\%$)2 ($22.2\%$) Undergraduate4 ($36.4\%$)4 ($40.0\%$)1 ($1.1\%$) Masters1 ($9.1\%$)5 ($50.0\%$)1 ($1.1\%$)1 ($11.1\%$) Professional1 ($9.1\%$) Unknown2 ($18.2\%$)6 ($66.7\%$)Geographic Area Rural (less 1000 people)2 ($18.2\%$)4 ($44.4\%$) Small (1000 to 29,999)8 (72.7)5 ($55.6\%$)1 ($11.1\%$) Medium (30,000 to 99,999) Large (100,000 to 999,999)7 ($70.0\%$)2 ($22.2\%$) Urban Centre (1 million+)2 ($20.0\%$) Unknown1 ($9.10\%$)1 ($10.0\%$)6 ($66.7\%$) Table 2 provides an overview of the sociodemographic characteristics of all participants who partook in the qualitative interviews.
## Data collection
We conducted semi-structured qualitative interviews with participants from all four case studies during the second phase (July 2020 to March 2021) of the COVID-19 Pandemic in Canada. The interview guide was designed in order to accurately convey the experiences of organizational leaders, managers, providers, and patients and included questions in these four categories: 1) comfort level and competency, 2) training supports 3) challenges with virtual care, and 4) benefits of virtual care. We employed a snowball sampling technique, enabling participants to identify other potential participants whose experiences would provide crucial insights to our project. Participants were recruited and interviewed until we reached saturation. Over the course of four months, we conducted 39 interviews.
We conducted approximately 10 qualitative interviews per case. All interviews were conducted by phone ($$n = 12$$) or by videoconference ($$n = 27$$). Interview data were collected through audio recordings and detailed note taking. Additional data were acquired through secondary Internet searching for information about the service and about the organizational structure.
## Data analysis
We engaged in rapid analytic methods in order to analyze the interview data on a short timeframe such that our results could be used by case study organizations to improve their virtual services. Rapid evaluation methods have been well documented in the literature [29–34]. Our rapid analytic methods were based on existing best practices in the field [29, 30], and involved summarizing and thematically analyzing the interview data for each case. Two to three members of the research team worked collaboratively to generate domains, themes, and subthemes for each case. Individual themes were identified for each case and used to create a codebook. Interview data were mapped onto the case-specific codebooks and categorized under the themes and subthemes. Research team members met regularly throughout this process to review results and make iterations onto the codebooks. Conflicts arising from divergent data interpretations were resolved through group discussion. A cross-case analysis was subsequently employed to identify differences and similarities across cases and generate high level themes.
## Results
The themes below provide a narrative synthesis of our case studies and outline the challenges experienced by organizations during the rapid implementation of virtual care and the strategies that were implemented to support health equity.
## Challenges
We grouped the challenges identified in our case studies into five distinct categories: [1] Suitability of Virtual Care, [2] Infrastructure for Virtual Care, [3] Digital Health Literacy, [4] Organizational Capacity for Health Equity, and [5] Language and Cultural Appropriateness. Each of the categories produces a distinct form of challenge for health care providers and clients intending to engage in virtual care. Additional file 1: Table S1 details the challenges experienced by organizations offering virtualized services to structurally marginalized communities, and provides supporting quotations from our qualitative data.
Suitability of virtual care relates primarily to whether providers and clients believed virtual care to be a medium through which clinical benefit can be achieved. Many providers were concerned about the usability, effectiveness, and quality of virtual care and believed in-person encounters were more conducive to building better patient-provider relationships. Some providers were so averse to virtual care, they did not even try it; some made a few attempts to engage in virtual care and found it was not suitable for their line of work; and others found that it was appropriate in some instances and should act as a complement to in-person care in the future. The clinical purpose of the virtual visit was essential in determining whether virtual care was an appropriate, or even feasible, mode of care delivery. Appointments requiring hands-on activities, physical tests, and assessments of the home environment were difficult to conduct through virtual modalities.
Infrastructure for virtual care relates to the various material devices and network connections necessary to engage in meaningful virtual care. Service users in our case studies reported experiencing a lack of access to and affordability constraints of the Internet, cellular service, and/or digital devices with sufficient minutes or data. The lack of access was most apparent for those living in rural and remote communities and those who are low-income, including older adults on a fixed income and individuals experiencing homelessness. Additionally, connectivity issues were not limited to patients, and many health care providers also struggled to connect to the Internet for virtual appointments.
Digital health literacy relates to the skills and preparedness to engage in health-related activities in virtual environments, including the telephone. While organizations noted that older adults and individuals who did not speak fluent English experienced the greatest challenge with using technology, even the most technologically savvy patients were described as having difficulties. The low levels of digital health literacy among patients increased the administrative burden for staff and providers who were often left to guide patients through the set-up process of engaging in a video visit. This left less time for the appointment itself, prompting patients and providers to choose phone modalities over video modalities to avoid further impediments to care. Additionally, some of the aversion providers felt about switching to a virtual medium was due to their own lack of digital health literacy. Some providers struggled with learning new software and were not confident in their ability to navigate the technology with enough expertise to provide sufficient and appropriate care.
Organizational capacity for health equity relates to the existing skills, knowledge, and attitudes at an organization regarding the causes of health inequities and strategies to address them. Knowledge about health equity and the role it plays in determining patient access to care was variable among leaders and providers at the organizations. Health equity was not largely considered during the initial implementation process but underwent deeper consideration as virtual care implementation continued. Furthermore, the capacity of organizations to ensure a robust and equitable approach to virtual care delivery was somewhat diminished by their preoccupation with the COVID-19 pandemic. Services were rapidly virtualized during a time with many competing priorities for health care organizations and with ongoing changes with human health resources (e.g., staffing shortages, temporary layoffs, redeployment). Organizations were first and foremost concerned with setting up a virtual care strategy at their respective organizations and began to direct their attention towards reaching specific communities when barriers to access became apparent.
Language and cultural appropriateness relate to the linguistic and cultural meanings of virtual care encounters for clients of varying cultural identities. In some cases, language created immediate and direct barriers because clients did not communicate in English as providers expected. These visits often resulted in interrupted care or shortened visits. Some organizations are currently in the process of developing clinical interpreter processes for virtual appointments, but such processes have yet to be widely implemented. In other cases, cultural beliefs about wellness and care meant that a lack of in-person presence detracted from the potential for healing encounters.
While these categories of challenges transpired across all cases, the primary care case study stands out as a unique case. Indigenous perspectives of health and well-being differ from Western conceptualizations, and we acknowledge that this particular case requires separate treatment and attention in our analysis. The history of settler colonialism in Canada means that members of First Nations communities experienced challenges in a way other participants did not. This, in turn, means the primary care organization needed to consider the diverse needs of their patients in order to respond to these distinct challenges. Table 3 details the challenges experienced by First Nations communities engaging with virtual primary care services. Table 3Experiences in virtual care for first nations communitiesIndigenous cultural safety was an essential component in the primary care case study. Elders and older adults living in the First Nations communities served by the primary care organization were identified as experiencing important challenges with virtual care, especially if they were not fluent in English. Members of the First Nations communities participating in the case study described themselves as being a part of a very visual culture. In this sense, visual culture refers to the importance of seeing as a way of knowing the world, and through visions and dreams, represents connection to the spiritual realm. Members of this community described themselves as visual learners, for whom knowledge is acquired through real-life, practical, and hands-on experiences. Beyond the importance of a visual culture, care was not understood by participants in the First Nations community as “transactional”, but rather as relational. Care and healing were understood to occur through co-presence, not through the exchange of diagnoses and advice. Connected to these understandings, in-person care was valued for its visual and relational presence because of the energy people bring to one another when they interact. Energy helps with the healing process, but with the rapid onset and widespread implementation of virtual care, First Nations communities had to adapt to offering their energies in a new and different way. The rapid switch to virtual care as a result of the pandemic was interpreted by some members of the communities involved to mean that the delivery of health care services had halted, and providers did not want to see them. These beliefs were reinforced by the lived experiences of community members who had experienced health care racism and had been recipients of substandard care from a health care system that has historically refused to treat them. Community clinics had closed in March 2020, during the first wave of the COVID-19 pandemic, and due to the lack of effective communication about the switch to virtualized services, many patients did not realize there were alternative care options available for them. As a result, many issues that could have been resolved virtually went unaddressed and some patients were described to experience poorer health. Initially, there was no plan in place to build the self-efficacy of First Nations communities to engage with virtual care in ways that reflect their culture. In order to implement such a large-scale change, the primary care organization required a more fulsome and repetitive communication strategy to inform clients about the switch to virtual care and increase awareness about the available digital health options being provided. In response to feedback from community members, the primary care organization spent a lot of time engaging with community members and leaders of the neighbouring First Nations communities. *In* general, the organization took its direction from the community leadership when it came to service provision, which now included virtual care. Having incorporated initial feedback about the challenges of virtual care, the organization sought out additional feedback and adapted their services to align with the needs and wishes of communities. An important example was that health care providers began conducting virtual visits from community clinics located in First Nations communities in order to demonstrate their commitment to being present in communities. The clinics were not necessarily open for community members to access, but the physical presence of providers in their local communities reflected the understanding by the organization of the cultural significance of having providers in close proximity to their clients.
## Strategies
We grouped the strategies identified in our case studies into three distinct categories: [1] Blended Models of Care Provision, [2] Volunteer and Staff Support for Outreach and Virtual Care, and [3] Securing Virtual Care Infrastructure. Additional file 2: Table S2 details the strategies used by organizations offering virtualized services to structurally marginalized communities and offers illustrative quotations.
Blended models of care provision represent the observation that the organizations we studied did not rely exclusively on any one modality of care delivery. Although focus shifted substantially to providing virtual care as a result of the pandemic, in-person visits were still offered for those who were unable to access care any other way, such as those who encountered infrastructural barriers to accessing virtual care, had a health care concern that was considered “essential”, or were comfortable with face-to-face encounters during COVID-19. This commitment to maintaining access was the case across all organizations we studied, and led to the development of an approach in which multiple modalities of care delivery (e.g., virtual visits, home visits, in-person visits, porch visits) were offered to clients in need.
Volunteer and staff support for outreach and virtual care refers to the establishment of processes by which volunteers, staff, or care providers at organizations mobilized to provide necessary education, support, and outreach to enable clients to use virtual care. In many cases, this involved training sessions for clients the day prior to health care visits occurring virtually or a dedicated team of staff available for live support during virtual appointments. When human resources were not available to accomplish this, organizations relied on informal supports in the home or provided a central site (e.g., at the clinic itself) where necessary technology and support were made available. In some cases, staff members went in person to clients’ homes to ensure they had the technology necessary to engage in future virtual care visits.
Securing virtual care infrastructure refers to the systematic efforts of organizations oriented toward ensuring that clients had the necessary network connections and digital devices to engage in virtual care. Many organizations developed or partnered with programs that made digital devices available for groups who did not have access to them. In some instances, devices and equipment were purposefully chosen to suit the needs of specific individuals. For example, Chromebooks were chosen for patients requiring larger screens due to visual difficulties or those who required a self-standing device due to physical impairments; and headphones were distributed to those with auditory impairments. These programs were used to identify those who required the most support to access virtual care including individuals experiencing homelessness, and those who absolutely needed to continue to receive care.
## Discussion
We conducted an exploratory, multiple case study of four organizations providing health and social care services that span the continuum of care in Ontario, Canada. We presented a narrative cross-case synthesis that outlined the challenges organizations experienced during the rapid implementation of virtual care during the first wave of COVID-19 and the strategies they put into place to promote health equity. In this discussion section, we address the issue of access to virtual care for structurally marginalized communities by drawing on a leading framework for conceptualizing access to care, and outline implications for policy and practice in this field.
Levesque et al. [ 35] reviewed definitions and conceptual discussions of the concept of access in published literature to produce a comprehensive framework for understanding and studying access to health care (Fig. 1). Attending to both the “supply side”, or features of health care providers and services, and the “demand side”, or features of the individuals and communities seeking out care, they outlined five dimensions of accessibility of health care services: [1] Approachability [2]; Acceptability [3]; Availability and accommodation [4]; Affordability; and [5] Appropriateness. Each of these dimensions of accessibility corresponds to an ability of individuals or communities to realize access to care, requiring a set of structural, social, and geographic circumstances that enable interaction with health services along the process of seeking care and benefiting from services. In this way, they suggest that “access is seen as resulting from the interface between the characteristics of persons, households, social and physical environments and the characteristics of health systems, organisations and providers”. Our multiple case study has focused on the actions of health care organizations to promote access to virtual care during the early phases of the COVID-19 pandemic, and as such, we structure our discussion according to the actions of organizations in each dimension of accessibility. However, the accessibility and ability dimensions are not independent constructs but are interconnected and influence one another. We acknowledge that the ability dimensions are not solely the responsibility of the patient but rather are a reflection of how systems are designed to respond in a way that facilitates or impedes patients to act on those abilities. Fig. 1Levesque et al’s [35] conceptual framework of access to health care
## Approachability
Approachability refers to the possibility that people searching for services can “actually identify that some form of services exists, can be reached, and have an impact on the health of the individual”. At the immediate onset of the COVID-19 pandemic, there was a significant drop in utilization of health care in Canada [36]. However, that quickly recovered as patients and providers engaged with virtual care. Generally speaking, people across our case studies were aware that virtual care was available as a result of the prominence of the impacts of the pandemic on health care in news media and social media. When people contacted the organizations in our study to seek out health care, they were presented with a set of options aligning with the rapid shift to virtual care across organizations. In some cases, patients cancelled appointments or chose not to create appointments that were virtual, however, the widely publicized nature of the shift to virtual care meant that, apart from the primary care case described earlier, approachability was not a primary barrier to access in our dataset.
Nonetheless, organizations in our study eventually sought to enhance the approachability of virtual services as the pandemic evolved. The strategy “volunteer and staff support for outreach and virtual care” included specific activities such as community engagement to educate communities about available virtual services and gain feedback about what efforts are required to make virtual care more appealing and satisfactory. Although beneficial, questions were raised about the sustainability of volunteer and staff approaches to community engagement and education in the longer term. The literature pointed to additional strategies that did not appear in our case studies, including educating the larger community to raise awareness about virtual care initiatives [23, 24, 37], involving the community during the implementation process [37], gradually introducing the virtual care technology [23], and deeply engaging in community consultation to seek input about users’ needs [38]. We propose that investing in health equity in virtual care for the future will need to more fully engage with these approaches to enhancing the approachability of virtual care.
## Acceptability
Acceptability refers to the cultural and social influences on whether people accept aspects of the service. The clearest and most obvious challenges that interfered with access to virtual care under the acceptability domain is the challenge “language and cultural appropriateness” especially as it relates to approaches to care. As detailed in Table 3, many members of the First Nations community included in our study did not feel that virtual care was implemented in a way that resonated with their cultural beliefs about health, wellness, and healing. A distinct but related issue pertains to the language spoken during medical appointments, where patients were less able or unable to communicate with health care providers virtually as a result of low fluency in English. These two examples were obvious barriers to virtual access to care, and will require investment in explicit strategies to address them for sustainable futures of virtual care.
Organizations in our study employed strategies related to acceptability in the “volunteer and staff support for outreach and virtual care” category, as well as the “blended models of care provision” category. With respect to the First Nations community described earlier, a blended model of care involving the community presence of providers, in-person visits where necessary, and virtual visits where acceptable substantially enhanced the acceptability of care. Several other strategies have been documented in the literature, which organizations can consider implementing, to enhance the acceptability of virtual care. Some of these strategies include language and culture matching to facilitate communication [26, 39, 40], and ensuring interpretive services [41, 42] and translated materials [42, 43] are available and accessible. Other strategies involve designing and providing culturally appropriate technology [9, 26] and services [9, 25, 44], and training providers to deliver care in culturally appropriate ways [26, 39, 43–45]. This may involve incorporating elements of spirituality and traditional medicine, considering holistic health, and incorporating cultural norms and local beliefs.
## Availability and accommodation
Availability and accommodation refer to the existence of resources within a health care system that are of sufficient quantity and quality to produce and deliver services. In this way, availability and accommodation relate to when, where and through which modality health care services are offered as well as the characteristics of who is offering the services, and whether these features align with the needs and wishes of people pursuing care. Barriers to access documented in our study linked to a number of dimensions of availability and accommodation. For example, where staff experienced concerns with their own “digital health literacy”, they were less able to seamlessly engage patients in virtual visits. However, the most salient challenge in this dimension of access relates to low levels of “organizational capacity for health equity”, such that organizations lacked deep expertise in the causes and consequences of inequities in care. This is a particularly salient point related to equitable access to virtual care.
Although organizations promoted access in the availability and accommodation dimension via strategies in the “blended models of care provision” category and eventually through the “volunteer and staff support for outreach and virtual care” categroy, the challenge of acknowledging the broader social, structural, and political determinants of the health system structure and access to care was prominent. Most participants were not explicitly aware of these broader challenges or knowledgeable about the ways they impact the availability and accommodation of access to care. Interview discussion rarely approached dialogue about the underlying determinants that generate inequities and influence access to care, such as income inequality, settler colonialism, and systemic racism. Acknowledging these contexts in which virtual care is implemented is a crucial first step in promoting health equity in virtual care. Such acknowledgement will facilitate deeper awareness about the particular kinds of accommodation that are required, and inspire deeper engagement in learning about the causes and consequences of health inequities in systems of care.
While virtual care does not address fundamental issues related to transportation, provider shortages, and insufficient health care services in Indigenous and rural communities, it does have the potential to enhance access by facilitating connections to remote providers and reducing travel time and transportation costs [9, 27, 39]. The absence of adequate infrastructure and providers in these communities belies the continued lack of prioritization of Indigenous health and exemplifies the inequitable and inadequate distribution of resources across regions, organizations, and communities.
## Affordability
Affordability refers to the “economic capacity for people to spend resources and time to use appropriate services”. Importantly, affordability is not solely about the possible direct costs of care that might be covered via public taxation, private insurance, or out-of-pocket spending, but also the indirect costs associated with time away from work, the need to travel, or in the case of virtual care, the need for digital technology and high-speed Internet access. Indeed, the lack of access to and challenges associated with these latter elements of the “infrastructure of virtual care” category was an important finding in our study. This outcome is supported by existing literature detailing the lack of affordability and access to digital devices and/or the Internet among structurally marginalized communities, such as racial and cultural minorities [25, 46], older adults [23, 24, 47], Indigenous Peoples [9, 38, 43], individuals living in rural and remote settings [9, 38], and individuals with low income [46].
Organizations in our study leveraged strategies related to the “blended models of care” (e.g., offering phone visits where patients did not have access to Internet-connected digital devices) category. Blended models of care represent a crucial set of strategies that are necessary to promote access in the affordability dimension and facilitate access for patients in all financial circumstances. Another promising strategy to promote access to virtual care specifically involves “securing virtual care infrastructure” for patients. For example, donating digital devices where necessary is a promising strategy, documented both in the case studies and in the literature [24, 26, 38, 48]. Furthermore, systematic reviews by Bradford et al. [ 49] and Kruse et al. [ 9] recommend using low-cost alternatives to more expensive equipment, while Fang et al. [ 23] recommends creating social policies introducing subsidies to purchase a digital device for individuals with low income, and further facilitating access to virtual care by ensuring that the appropriate technology is available in easily accessible spaces. Where possible, organizations should advocate for subsidies as opposed to lost-cost alternatives, which may have an implication on the quality of the equipment and services being offered. These additional strategies will promote a more comprehensive approach to ensuring affordability for future uses of virtual care.
## Appropriateness
Appropriateness refers to the fit between the person’s needs, the nature of the services offered, and the timeliness of care. Appropriateness is thus about whether the services are of sufficient quality and type to actually meet peoples’ needs. Organizations in our study expressed concerns about the appropriateness or “suitability of virtual care”, especially with regards to the modality being offered (e.g.whether a telephone visit was sufficient to address complex health issues). Challenges related to appropriateness have also been documented in the literature, including low levels of digital health literacy [9, 25, 46, 50, 51] and a mismatch between the technology used and a patient’s sensory, physical, and cognitive ability [24, 52–56].
Organizations in our study produced clearly stated workflows and enhanced health care provider training to address these issues. For example, organizations offered education and practice sessions to patients who uncomfortable with accessing virtual care. By setting up appointments to review the process of a virtual visit and answer patients’ questions, organizations enabled those with lower digital literacy to view a digitally-mediated health care visit as acceptable. This strategy points to the demand for widespread digital health literacy training as a strategy to promote the equitable and ongoing use of virtual care. However, important gaps related to the fit between the modality being offered and the capacity and needs of patients remained. Appropriateness of virtual care is multi-dimensional, relating to digital health literacy, the availability of education and support, and the nature of the clinical or social need being addressed. Future effort to promote the appropriateness of virtual care will require this multi-dimensional perspective.
## Overarching reflections and implications for health systems
Although barriers to access for structurally marginalized communities and their underlying causes are well documented in the literature, health equity was not a universal priority concern during the rapid virtualization of care. The unexpected crisis and the rush of the pandemic left little time for health care organizations to implement virtual care in an equitable way, as their priority was to maintain continuity of care. This strategy may have inadvertently contributed to increasing inequities within the most structurally marginalized communities.
Our research project has illustrated a number of important themes related to the infrastructure for virtual care, organizational capacity to engage with virtual care in meaningful ways, and existing inequities in the broader health care system. However, even as organizations begin to familiarize themselves with the evidence base and shift their attention towards equitable service delivery, it is apparent that these inequities are persisting and are embedded in inequitable systems. Any effort to develop a plan for the sustainability of virtual care services requires an understanding of the potential consequences for members of structurally marginalized communities who may have precarious access to many virtual care initiatives. A lack of awareness and engagement of these existing inequities simply meant these challenges also manifested in virtual care delivery. Ultimately, individual providers and organizations are unable (and do not have the power) to solve system-level challenges on their own.
As health systems in Canada and elsewhere work towards comprehensively integrating virtual care into care delivery, local and national governments will need to work collaboratively to ensure that an equitable and sustainable approach to virtual care delivery is in place. Our analysis points towards the policy recommendations arising from Budhwani et al. [ 57], related to recommendations aimed at the individual, technological, health system, and social/structural determinants level. In addition to the strategies outlined above, governments can consider investing in subsidized options for cellular phone service and high-speed Internet to promote equitable access to the connectivity required to engage in virtual visits [2] and commit to ensuring that high-speed Internet and cellular service is made available across the entire geography of a region. Governments, health systems, and health care leaders can also invest in educational content to build capacity in understanding equity, inclusion, diversity, and anti-racism in health care organizations [2]; and to advocate for the systematic and comprehensive inclusion of equity, diversity, and anti-racism education into the formative training of health care providers and managers.
## Limitations
One limitation of this study is the rapid timeline in which it was conducted. The rapid nature of this project was necessary due to the urgency of producing timely results for organizations looking to improve virtual care delivery during COVID-19. Rapid analytic methods are conducive for analyzing many interviews within a shortened timespan and are an efficient and rigorous approach for identifying key implementation characteristics [30] and providing quick and actionable feedback [29, 31–34].
While case study results are often stated to be ungeneralizable to the wider population, many concepts can still be transferrable to other settings [58, 59]. The insights generated from the cases were intended to help create a foundation for understanding the real-world challenges and strategies that characterize efforts to promote health equity in virtual care.
We acknowledge that despite our effort to recruit a diverse sample of participants to interview, many of them identified as older, educated, medium and high income, white women. Given that our only form of contacting patients was through virtual means, it was difficult for our team to reach those with no or infrequent access to virtual technologies. The need to practice physical distancing during the COVID-19 pandemic interfered with the possibility of meeting participants for in-person interviews.
## Directions for future research
Future studies should focus on organizations whose provision of care is primarily delivered to members of structurally marginalized communities and who have expertise in engaging with these communities. Work in this direction should highlight examples from organizations that have successfully implemented strategies that improve access to virtual care for specific structurally marginalized communities. Future work should also explore perspectives from communities who were underrepresented in our project. Subsequent research could be done in a non-rapid context and employ traditional analytic methods. Such methods should also involve deeper and prolonged community engagement, drawing on methodologies such as community-based participatory research (CBPR).
## Conclusion
While the rapid scale-up of virtual service delivery manifested as a timely response to the COVID-19 pandemic, health equity was not a priority concern during this process. During the rapid implementation of care, organizations prioritized provider-friendly processes and missed the mark in providing equitable client-centered care. Organizations did not initially consider or incorporate the pieces required to make virtual care work for the most structurally marginalized individuals and communities. This paper outlined the challenges and strategies experienced by four organizations in Ontario that rapidly virtualized their health care services. Drawing on a framework that conceptualizes access to health care, we examined the key accessibility dimensions that were emphasized and overlooked by organizations.
The inequitable distribution of health care resources arising from persistent colonial systems and practices have far-ranging consequences, and these include their impact on inequitable access to virtual care.
## Supplementary Information
Additional file 1: Table S1. Challenges Identified during the Rapid Implementation of Virtual Care. Table S1. details the challenges experienced by organizations offering virtualized services to structurally marginalized communities and provides supporting quotations from our qualitative data. Additional file 2: Table S2. Strategies Implemented during the Rapid Implementation of Virtual Care. Table S2 details the strategies used by organizations offering virtualized services to structurally marginalized communities and offers illustrative quotations.
## Authors’ information
SS locates as a Bengali settler and as a first generation Canadian citizen.
SH is a white settler Canadian Citizen.
PS is a Denesuline treaty member of Cold Lake First Nations, of Treaty 6 territory.
SC locates as a second generation Indo-Canadian settler, and Canadian citizen.
RDS locates as a settler, and as a second generation Canadian citizen.
ES is a Canadian Citizen of Chinese descent.
JS is a white settler Canadian Citizen.
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|
---
title: Impact of two ketogenic diet types in refractory childhood epilepsy
authors:
- Ali M. El-Shafie
- Wael A. Bahbah
- Sameh A. Abd El Naby
- Zein A. Omar
- Elsayedamr M. Basma
- Aya A. A. Hegazy
- Heba M. S. El Zefzaf
journal: Pediatric Research
year: 2023
pmcid: PMC10007663
doi: 10.1038/s41390-023-02554-w
license: CC BY 4.0
---
# Impact of two ketogenic diet types in refractory childhood epilepsy
## Abstract
### Background
Ketogenic diet (KD) refers to any diet in which food composition induces a ketogenic state of human metabolism.
### Objective
To assess short- and long-term efficacy, safety, and tolerability of KD [classic KD and modified Atkins diet (MAD)] in childhood drug-resistant epilepsy (DRE) and to investigate the effect of KD on electroencephalographic (EEG) features of children with DRE.
### Methods
Forty patients diagnosed with DRE according to International League Against Epilepsy were included and randomly assigned into classic KD or MAD groups. KD was initiated after clinical, lipid profile and EEG documentation, and regular follow-up was done for 24 months.
### Results
Out of 40 patients with DRE, 30 completed this study. Both classic KD and MAD were effective in seizure control as $60\%$ in classic KD group and $53.33\%$ in MAD group became seizure free, and the remaining showed ≥$50\%$ seizure reduction. Lipid profile remained within acceptable levels throughout the study period in both groups. Adverse effects were mild and managed medically with an improvement of growth parameters and EEG during the study period.
### Conclusions
KD is an effective and safe non-pharmacologic, non-surgical therapy for the management of DRE with a positive impact on growth and EEG.
### Impact
Both common types of KD (classic KD and MAD) are effective for DRE, but unfortunately, nonadherence and dropout rates are frequent. High serum lipid profile (cardiovascular AE) is often suspected in children following a high-fat diet, but lipid profile remained in the acceptable level up to 24 months. Therefore, KD constitutes a safe treatment. KD had a positive impact on growth, despite inconsistent results of the KD’s effect on growth. In addition to showing strong clinical effectiveness, KD also considerably decreased the frequency of interictal epileptiform discharges and enhanced the EEG background rhythm.
## Introduction
Up to 65 million people worldwide are affected by epilepsy.1 Approximately one-third of epileptic patients still have difficulty being treated, even though two-thirds of them can control their seizures with current anti-seizure medication (ASM). The International League Against Epilepsy (ILAE) defines drug-resistant epilepsy (DRE) as the failure of adequate trials of two tolerated, properly selected and utilized antiepileptic drug schedules to achieve sustained relief of seizures.2–4 The three main objectives of epilepsy treatment are total seizure control, maintaining quality of life (QoL), and avoiding adverse effects (AEs).2 The retention rates of ASM can be as low as $50\%$ due to their severe AE, such as a considerable deterioration in QoL.5 Therefore, one should initially attempt surgical therapies if a patient is eligible and if surgery is not a possibility for a patient with DRE, vagus nerve stimulation or dietary therapy like the ketogenic diet (KD) are worthwhile alternatives.6–8 KD (a high-fat, adequate-protein, and low-carbohydrate diet) is an effective, non-invasive, and non-pharmacologic treatment for refractory childhood epilepsy used since 1920s with few to no neurotoxic effects when compared to multiple ASM.7,9 Ketosis (which mimics a starving state), decreased glucose, higher fatty acid levels (which improve bioenergetic reserves), anti-epileptogenic properties and neuroprotective properties are only a few of the various, as-yet-unknown processes through which KD operates.10 Also, ketones may confer neurologic protection and ketone bodies could exert anti-oxidative, anti-inflammatory, cellular, epigenetic, and gut-microbiome alterations.11–13 Despite the KD’s effectiveness, most patients discontinue the diet because of its unpalatable and restrictive features. So, new variants of KD have emerged, including the modified Atkins diet (MAD) and the low-glycemic-index diet.14–18
MAD provides a more palatable and less restrictive dietary treatment option.14 The most notable aspect of the MAD is that it begins in an outpatient setting without fasting. The effectiveness of MAD in comparison to classic KD is still debatable. Some studies focused on the major AEs of classic KD in refractory epilepsy, while others showed that MAD is equally effective as classic KD.15,19,20 However, since KD is not a physiological diet, it is important to identify and carefully monitor any AEs.21 AEs can happen at the start of the diet as well as months after KD initiation. Dehydration, altered electrolytes, hypoglycemia, tiredness, abdominal pain, nausea/vomiting, diarrhea, and constipation are short-term AE, while hypoproteinemia, hypocalcemia, hypercalciuria, urolithiasis, alterations in lipid profiles and increase in transaminases or cardiomyopathy are possible long-term AE.22,23 There are few studies available regarding potential long-term negative effects in children using KD for longer than 2 years; the most mentioned symptoms are an increased risk of bone fractures, kidney stones and growth delay.24 Electroencephalographic (EEG) features are improved by KD in addition to significantly reducing clinical seizures in epileptic patients. Despite this, there have not been any reported prospective studies of the predictive power of baseline EEG or early changes in EEG for KD therapy response. In addition, few research have compared the electrophysiologic properties of KD responders and non-responders.25,26 So, this study aimed to assess short- and long-term efficacy, safety, and tolerability of KD (classic KD and MAD) in childhood DRE and to investigate the effect of KD on EEG features of children with DRE.
## Study design
This prospective randomized study was conducted to evaluate the efficacy, safety, and tolerability of the KD and its effect on EEG features among children with DRE.
Our primary outcome was to assess the clinical effectiveness of KD (classic KD and MAD) regarding onset of seizure control, seizure frequency and seizure severity, and to assess long-term safety of KD regarding AE and the effect of KD on growth and lipid profile (cardiovascular risk). The secondary outcome was the evaluation of the effect of KD on EEG features prior to and 3 and 6 months after the KD treatment with the possibility of withdrawal of ASM.
## Study population
We initially enrolled forty patients with DRE attending Pediatric KD outpatient clinic at Menoufia University Hospital from January 2020 to April 2022, after obtaining the approval of the Institutional Review Boards of the Menoufia Faculty of Medicine (ID number 191019 PEDI 29) and an informed (written) consent was obtained from each parent or caregiver.
Our inclusion criteria were patients who had received two or more types of regular antiepileptic drugs, but frequent seizures continued. Patients with chronic diseases, congenital metabolic disorders, liver diseases, and systemic diseases were excluded from the study.
Patients were randomly assigned to two groups, Group 1 (classic KD group): 20 patients received classic KD in the form of formula (Ketocal milk from Danone, Nutricia) and food with the ratio of 3–4 g of fat for every 1 g of carbohydrate and protein, and Group 2 (MAD group): 20 patients kept on MAD consisting of a nearly balanced diet ($60\%$ fat, $30\%$ protein, and $10\%$ carbohydrates by weight) providing 100 kcal/kg/day, without restrictions on calories, fluids, protein or need for an inpatient fast and admission.
Before starting KD, we collected demographic and clinical data of studied patients including age, sex, age of start of seizures, anthropometric measurements (weight, length/height, weight for length/height, and BMI) according to Egyptian Z score growth references for Egyptian children,27,28 etiology, type of seizures, duration of uncontrolled seizures, seizure frequency, and seizure severity [scored according to Chalfont Seizure Severity Scale (CSSS)].29 Number of antiepileptic drugs used, and baseline EEG were also documented.
## Method of randomization
The allocation sequence was generated using permuted block randomization technique and the block size was variable.30 Allocation sequence/code was concealed from the person allocating the participants to the intervention arms using sealed opaque envelopes.31 Double-blinded approach was adopted.32 In the present study, consecutive sampling technique was adopted.33
## Laboratory procedures
Morning blood samples were taken after 8–12 h fasting. Triglycerides (TG), total cholesterol, low-density lipoprotein (LDL-C) and high-density lipoprotein (HDL-C) were measured. Normal values of laboratory data were considered as follows: serum cholesterol (<170 mg/dL is acceptable, 170–199 mg/dL is borderline and >200 mg/dL is high), serum LDL (<110 mg/dL is acceptable, 110–129 mg/dL is borderline and >130 mg/dL is high) and serum HDL (>45 mg/dL is acceptable, 40–45 mg/dL is borderline and <40 mg/dL is low).34
## Initiation of KD
For the first month, carbohydrates were restricted to 10 g/day but were permitted to increase by 5 g/day at intervals of at least 1 month if the child was having difficulty with the restriction of carbohydrates to a maximum of $10\%$ carbohydrates per day by weight. A qualified dietician also educated the parents or caregivers about diet preparation at home with close monitoring of blood glucose and urinary ketones. Multivitamins, calcium, and vitamin D were given as supplements and the antiepileptic drugs and doses used were not changed from those administered previously throughout the study period. Patients were requested to attend their regular monthly outpatient visits for 6 months, then every 3 months for 24 months and the form and frequency of seizures and adverse reactions were observed and recorded.
## KD tolerability
After 1 month of KD initiation, two patients of classic KD group and one patient of MAD group were excluded from the study due to noncompliance of their caregivers. At 3-month follow-up visit, six patients were excluded from the study as they did not tolerate the MAD and the ketogenic liquid formula. Also, one patient of MAD group died secondary to infection in COVID-19 epidemic, so only 30 patients completed the study for 2 years, 15 in each group.
## Short-term outcome
The clinical efficacy of KD was evaluated regarding the onset of improvement of seizures and seizures frequency and severity (according to CSSS) prior to and 3 and 6 months after the KD treatment were analyzed. Effectiveness was evaluated as complete seizure free or seizure reduction percentage change. Percentage change was calculated as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\rm{Percentage}}}}}}}}\;{{{{{{{\rm{change}}}}}}}}\left(\% \right) = \frac{{{{{{{\rm{Measurement}}}}}}\;\left({{{{{{\rm{after}}}}}}} \right) - {{{{{\rm{Measurement}}}}}}\;\left({{{{{{\rm{before}}}}}}} \right)}}{{{{{{{\rm{Measurement}}}}}}\;\left({{{{{{\rm{before}}}}}}} \right)}}\times100$$\end{document}Percentagechange%=Measurementafter−MeasurementbeforeMeasurementbefore×100
## KD and EEG
The NIHON KOHDEN EEG-1200K was used to record the EEG in a quiet environment. The 21-channel-EEGs were recorded under typical circumstances (rest, hyperventilation, and photostimulation). Throughout the recording period, children were being studied while resting off with their eyes closed. The International 10–20 system was used to record EEG data from 19 scalp electrodes for 20 min using an average reference (MIZAR-sirius 33 Channels, EBNeuro).35 At FP1, F3, C3, P3, O1, F7, T3, T5, FP2, F4, C4, P4, O2, F8, T4, T6, Fz, Cz, and Pz, electrodes were placed. The EEG was digitized at 256 Hz with a time constant of 0.1 sec, a high-frequency filter of 70 Hz and a notch filter in each channel. Based on visual assessment, consecutive, 2-s-long epochs free of artifacts were chosen offline. The provided EEG software was used to perform a rapid Fourier transform on these 20 epochs. Mean-power spectra were gathered for every channel and frequency range in each subject. Six bands were utilized to divide the frequency ranges: Delta (0–4 Hz), Theta (4–8 Hz), Alpha (8–12 Hz), Beta-1 (12–18 Hz), Beta-2 (18–24 Hz), and Gamma (24–64) were the six bands used to separate the frequency ranges.35 By comparing EEG before and 1, 3 and 6 months after the KD therapy regarding the background rhythm in the occipital region under the awake and silent states and variations of interictal spike-wave index (SI), the effect of KD on EEG features was assessed. SI represented the mean of the spike discharge times during a second. In the calculation procedure, 100 s of the awake and quiet EEG findings (free of artifact fragments) were chosen, and SI = n/100 was used to compute the number of spikes (n) present in the recording.
KD had an impact on the EEG after the first month, and after 3 months, improvements in the background rhythm slowing were observed by plus ≥2 Hz in 8 cases and by plus 1–2 Hz in 15 cases, while after 6 months was observed by plus ≥2 Hz in 9 cases and by plus 1–2 Hz in 15 cases compared to baseline with significant correlation with seizure control after 6 months (P value = 0.031 and 0.021), respectively (Table 5). In addition, no change in background rhythm (<1 Hz) which was found in 20 out of 30 cases after 1 month, considerably improved to continue in just 7 cases after 3 months, and in only 6 cases after 6 months, with a significant correlation with seizure control (P value < 0.001). Figure 2 illustrates changes in background rhythm slowing after 1, 3 and 6 months of KD.Table 5EEG changes in response to KD and its correlation with seizure control. Background rhythm ($$n = 30$$)After 1 monthAfter 3 monthsAfter 6 monthsNo seizures ($$n = 5$$)Still seizuring ($$n = 25$$)No seizures ($$n = 12$$)Still seizuring ($$n = 18$$)Test of significanceNo seizures ($$n = 16$$)Still seizuring ($$n = 14$$)Test of significancePlus ≥2 Hz1 ($33.36\%$)2 ($66.67\%$)2 ($25.00\%$)6 ($75.00\%$)$$P \leq 0.063$$ NS3 ($33.33\%$)6 ($66.67\%$)$$P \leq 0.031$$*Plus 1–2 Hz2 ($28.57\%$)5 ($71.43\%$)7 ($46.67\%$)8 ($53.33\%$)$$P \leq 0.021$$*8 ($53.33\%$)7 ($46.67\%$)$$P \leq 0.021$$*No change (<1 Hz)2 ($10.00\%$)18 ($90.00\%$)3 ($42.86\%$)4 ($57.14\%$)$P \leq 0.001$*5 ($83.33\%$)1 ($16.67\%$)$P \leq 0.001$*Baseline EEG ($$n = 30$$)After 1 monthAfter 3 monthsAfter 6 monthsNo seizures ($$n = 5$$)Still seizuring ($$n = 25$$)Test of significanceOR$95\%$ CIP valueNo seizures ($$n = 12$$)Still seizuring ($$n = 18$$)Test of significanceOR$95\%$ CIP valueNo seizures ($$n = 16$$)Still seizuring ($$n = 14$$)Test of significanceNo Epileptiform discharge(R)3 ($37.50\%$)5 ($62.50\%$)6.000.780–46,145P = 0.085 NS8 ($100.00\%$)0 ($0.00\%$)69.883.368–1450.232P = 0.006*8 ($100.00\%$)0 ($0.00\%$)29.001.480–568.234P = 0.026*Epileptiform discharge2 ($9.09\%$)20 ($90.91\%$)4 ($18.18\%$)18 ($81.82\%$)8 ($36.36\%$)14 ($63.64\%$)Spike Index reduction ($$n = 22$$)After 1 monthAfter 3 monthsAfter 6 monthsNo seizures ($$n = 5$$)Still seizuring ($$n = 17$$)No seizures ($$n = 12$$)Still seizuring ($$n = 10$$)Test of significanceNo seizures ($$n = 16$$)Still seizuring ($$n = 6$$)Test of significance>$75\%$1 ($33.33\%$)2 ($66.67\%$)4 ($50.00\%$)4 ($50.00\%$)$$P \leq 0.063$$ NS7 ($77.78\%$)2 ($22.22\%$)$$P \leq 0.031$$*>50–$75\%$1 ($20.00\%$)4 ($80.00\%$)4 ($66.67\%$)2 ($33.33\%$)$$P \leq 1.000$$ NS5 ($71.43\%$)2 ($28.57\%$)$$P \leq 0.754$$ NS30–$50\%$1 ($16.67\%$)5 ($83.33\%$)2 ($40.00\%$)3 ($60.00\%$)$$P \leq 0.687$$ NS3 ($75.00\%$)1 ($25.00\%$)$$P \leq 0.687$$ NS<$30\%$2 ($25.00\%$)6 ($75.00\%$)2 ($66.67\%$)1 ($33.33\%$)$$P \leq 0.250$$ NS1 ($50.00\%$)1 ($50.00\%$)$$P \leq 0.031$$*(R) reference category, CI confidence interval, OR odds ratio, P P value of McNemar test (compared with 1-month findings), n number, NS non significant, Hz Hertz.*Statistically significant. Fig. 2Changes in background rhythm slowing after 1, 3 and 6 months of ketogenic diet.
There was a statistically significant difference regarding seizures control between cases with baseline EEG with no epileptiform discharge and cases with baseline EEG with epileptiform discharge after 3 and 6 months of KD as all 8 cases with baseline EEG with no epileptiform discharge became seizures free 3 months after KD compared to cases with baseline EEG with epileptiform discharge only $\frac{4}{22}$ and $\frac{8}{22}$ became seizures free at 3 and 6 months (P value = 0.006 and 0.026) respectively. Moreover, the interictal SI among cases with baseline EEG with epileptiform discharge (22 cases, 11 in each group) showed a significant reduction of >$50\%$ after 1 month in $\frac{8}{22}$ then this increased to 14 cases after 3 months and 16 cases after 6 months. Also, SI reduction <$30\%$ significantly reduced from 8 cases after 1 month to only 2 cases after 6 months with a significant correlation with seizure control ($$P \leq 0.031$$). The details of EEG changes in response to KD and its correlation with seizure control are illustrated in Table 5.
## Long-term outcome
Anthropometric measurements, lipid profile, and the development of AE were monitored at 3-month intervals up to 24 months. In addition, we assessed the possibility of antiepileptic drug withdrawal in seizure free patients after the first 6 months.
## Statistical methodology
Data were collected and entered into the computer using Statistical Package for Social Science program for statistical analysis (ver 25). Data were described using median and interquartile range. Comparisons were carried out between two studied independent not-normally distributed subgroups using the Mann–Whitney U test. Comparisons were carried out among related samples by Friedman’s test. Odds ratio (OR) was used to quantify the strength of the association between two events. McNemar’s test was used on paired nominal data with matched pairs of subjects to determine whether the row and column marginal frequencies were equal. Statistical significance was tested at P value < 0.05.
Based on El-Rashidy et al. ’s18 results, adopting a power of $80\%$ to detect a non-inferiority margin (d) of $15\%$ in success rate [complete disappearance of seizures (primary outcome)] and level of significance $5\%$ (α = 0.05), the minimum required sample size was found to be 15 patients per group (number of groups = 2) and the total sample size = 30 patients.36,37
## Results
Out of 40 patients enrolled initially in our study, 30 patients (13 males and 17 females) with median age 3 years (36 months) in classic KD group versus 6 years (72 months) in MAD group tolerated this study for 24 months, 11 patients ($36.67\%$) were underweight, 3 patients ($10\%$) were overweight, and 7 patients ($23.33\%$) were wasted with non-significant difference between the two groups. Patient characteristics are listed in Table 1.Table 1Demographic data and anthropometry of studied cases. All patients ($$n = 30$$)GroupsTest of significance (P value)Classic KD ($$n = 15$$)MAD ($$n = 15$$)Age (months) Min–Max4.00–144.004.00–96.006.00–144.00P = 0.161 NS Median [IQR]48.00 [72.00]36.00 [48.00]72.00 [85.00]Sex Male13 ($43.33\%$)6 ($40.00\%$)7 ($46.67\%$)$$P \leq 0.713$$ NS Female17 ($56.67\%$)9 ($60.00\%$)8 ($53.33\%$)Age of start of seizures (months) Min–Max0.00–96.000.00–96.002.00–96.00P = 0.036* Median [IQR]12.00 [44]5.00 [21.5]48.00 [55]Weight for age Underweight11 ($36.67\%$)6 ($40.00\%$)5 ($33.33\%$)$$P \leq 0.70394$$ NS Normal16 ($53.33\%$)7 ($46.67\%$)9 ($60.00\%$)$$P \leq 0.46540$$ NS Overweight3 ($10.00\%$)2 ($13.33\%$)1 ($06.67\%$)$$P \leq 0.54186$$ NSLength/height for age Normal30 ($100.00\%$)30 ($100.00\%$)30 ($100.00\%$)NAWeight for length or BMI Wasted7 ($23.33\%$)4 ($26.67\%$)3 ($20.00\%$)$$P \leq 0.66720$$ NS Normal20 ($66.67\%$)9 ($60.00\%$)11 ($73.33\%$)$$P \leq 0.44130$$ NS Overweight3 ($10.00\%$)2 ($13.33\%$)1 ($06.67\%$)$$P \leq 0.54186$$ NSn number of patients, Min–Max Minimum–Maximum, IQR interquartile range, NA non-applicable statistics.*Statistically significant ($P \leq 0.05$); NS: not statistically significant (P ≥ 0.05).
## Clinical presentation
According to ILAE [2017],38,39 epilepsy of unknown etiology was the commonest etiology among the participants [12 patients ($40\%$)] and Myoclonic seizure was the most prevalent type [10 cases ($33.33\%$)] followed by infantile spasm [6 cases ($20\%$)]. The median duration of uncontrolled seizures before KD was 7 months in classic KD group versus 8 months in MAD group with a median seizure severity scale (according to CSSS) and seizure frequency per day of 165 and 10, respectively. Sixteen patients ($53.33\%$) were on three AEDs, ten ($33.33\%$) patients were on four AEDs and four patients ($13.33\%$) were on two AEDs. The clinical characteristics of the cases before KD are illustrated in Table 2.Table 2Clinical characteristics before KD.All patients ($$n = 30$$)GroupsTest of significance (P value)Classic KD ($$n = 15$$)MAD ($$n = 15$$)Type of seizures Generalized tonic clonic5 ($16.67\%$)2 ($13.33\%$)3 ($20.00\%$)$$P \leq 0.62414$$ NS Myoclonic10 ($33.33\%$)5 ($33.33\%$)5 ($33.33\%$)$$P \leq 1.000$$ NS Infantile spasm6 ($20.00\%$)3 ($20.00\%$)3 ($20.00\%$)NA Focal3 ($10.00\%$)2 ($13.33\%$)1 ($06.67\%$)$$P \leq 0.54186$$ NS Unclassified seizures6 ($20.00\%$)3 ($20.00\%$)3 ($20.00\%$)NADuration of uncontrolled seizures (months) Min–Max2.00–48.002.00–48.002.00–36.00P = 0.983 NS Median [IQR]7.50 [15.00]7.00 [11.00]8.00 [21.00]Etiology of epilepsy Unknown etiology12 ($40.00\%$)8 ($53.33\%$)4 ($33.33\%$)$$P \leq 0.136$$ NS Genetic7 ($23.33\%$)3 ($20.00\%$)4 ($33.33\%$)$$P \leq 0.667$$ NS Structural Post-anoxic4 ($13.33\%$)2 ($13.33\%$)2 ($13.33\%$)NA Sturge–Weber syndrome1 ($03.33\%$)1 ($06.67\%$)0 ($00.00\%$)$$P \leq 0.3077$$ NS Tuberous sclerosis1 ($03.33\%$)0 ($00.00\%$)1 ($06.67\%$)$$P \leq 0.3077$$ NS Miller–Dieker syndrome1 ($03.33\%$)0 ($00.00\%$)1 ($06.67\%$)$$P \leq 0.3077$$ NS Post-traumatic3 ($10.00\%$)0 ($00.00\%$)3 ($20.00\%$)$$P \leq 0.06724$$ NS White matter disease causing leukodystrophy1 ($03.33\%$)0 ($00.00\%$)1 ($06.67\%$)$$P \leq 0.3077$$ NSSeizures frequency (baseline) (days) Min–Max00.28–30.0004.00–30.0000.28–30.00P = 0.611 NS Median [IQR]10.00 [9.00]10.00 [9.00]10.00 [11.00]Seizures severity scale (baseline) Min–Max111.00–177.00127.00–177.00111.00–177.00P = 0.144 NS Median [IQR]165.00 [46.00]169.00 [16.00]157.00 [54.00]Number of antiepileptic drugs Two4 ($13.33\%$)2 ($13.33\%$)2 ($13.33\%$)NA Three16 ($53.33\%$)8 ($53.33\%$)8 ($53.33\%$)NA Four10 ($33.33\%$)5 ($33.33\%$)5 ($33.33\%$)NANS non significant, NA non-applicable.
## Efficacy of KD
The onset of seizure improvement was 14 days in classic KD group versus 10 days in MAD group with a non-significant difference between the two groups. Regarding the CSSS, there was a statistically significant decrease in seizure severity by −$100\%$ percent change after 3 and 6 months of KD compared to baseline ($P \leq 0.0001$) without a significant difference between the two groups (Table 3). Analysis of CSSS after 6 months of KD initiation demonstrates improvement of duration of seizures in all patients and improvement in time to return to normal from the onset in $96.67\%$ of patients. Figure 1 illustrates the improvement in CSSS after 3 and 6 months of KD treatment. Table 3Seizures severity after KD.GroupsTest of significance (P value)Classic KD ($$n = 15$$)MAD ($$n = 15$$)Onset of seizures improvement (days) Min–Max07.00–21.0007.00–21.00P = 0.624 NS Median14.0010.00 IQR10.00–14.0010.00–14.00Seizures Severity Scale baseline (at 0 months) Min–Max127.00–177.00111.00–177.00P = 0.144 NS Median169.00157.00 IQR1654Seizures Severity Scale (after 3 months) Min–Max0.00–127.000.00–129P = 0.646 NS Median0.000.00 IQR98111Seizures Severity Scale (after 6 months) Min–Max0.00–111.000.00–79.00P = 0.963 NS Median0.000.00 IQR7052Friedman testχ2 (Fr) (df = 2) = 27.882P < 0.0001*χ2 (Fr) (df = 2) = 27.846P < 0.0001*Seizure Severity Scale percentage change (3 M vs baseline) n1515P = 0.680 NS Min–Max−100.00 to −24.20−100.00 to −27.12 Median−100.00−100.00 IQR55.3767.86Seizure Severity Scale percentage change (6 M vs baseline) n1515P = 0.909 NS Min–Max−100.00 to −34.32−100.00 to −53.25 Median−100.00−100.00 IQR39.5531.64Seizure Severity Scale percentage change (6 vs 3 M) n67P = 0.086 NS Min–Max−74.79 to −3.80−59.69 to −31.58 Median−20.58−44.88 IQR28.9019.54χ2 (Fr) Friedman Chi-Square, df degree of freedom. Intragroup: statistically significant when compared with baseline values (using Dunn–Sidak method).*Statistically significant; NS: non significant. Fig. 1Chalfont seizures severity scale 3 and 6 months after ketogenic diet. Improvement by percentage in Chalfont Seizure Severity Scale (item by item) after 3 and 6 months of intervention.
Seizure frequency after 3 and 6 months of KD showed a statistically significant decrease in comparison with baseline ($P \leq 0.0001$) with a non-significant difference between both groups. Six months after initiation of KD, $60\%$ of patients in classic KD group and $46.67\%$ of patients in MAD group became seizure free and the other $40\%$ and $53.33\%$ had ≥$50\%$ seizure reduction with a median percent decrease of −$83.33\%$ and −$75\%$ after 3 months in classic KD and MAD, respectively, and −$100\%$ after 6 months in both groups (Table 4).Table 4Seizures frequency after KD.Seizures frequencyClassic KD ($$n = 15$$)MAD ($$n = 15$$)Test of significance (P value)Total ($$n = 30$$)Baseline (days) Median [IQR]10.00 [9.00]10.00 [11.00]$$P \leq 0.611$$ ΝS10.00 [9.00]After 3 months (days) Median [IQR]0.00♯ [ΝΑ]1.00♯ [4.86]$$P \leq 0.367$$ ΝS1.00 [3.00]After 6 months (days) Median [IQR]0.00♯ [ΝΑ]0.00♯ [ΝΑ]$$P \leq 0.890$$ ΝS0.00♯ [ΝΑ] Friedman testχ2 (Fr) (df = 2) = 28.000P < 0.0001*χ2 (Fr) (df = 2) = 25.000P < 0.0001*Percentage change (%) (after 3 months) Median [IQR]−83.33 [−50.00]−75.00 [−43.33]$$P \leq 0.831$$ ΝS−77.50 [−50.00]Percentage change (%) (after 6 months) Median [IQR]−100.0 [ΝΑ]−100.0 [ΝΑ]$$P \leq 0.783$$ ΝS−100.00 [−33.30]Seizure frequency reduction after 3 monthsSeizure free ($100\%$ reduction)8 ($53.33\%$)4 ($26.67\%$)$$P \leq 0.1362$$ ΝS12 ($40.00\%$)SFR (if still convulsions)7 ($46.67\%$)11 ($73.33\%$)18 ($60.00\%$) 501 ($6.67\%$)2 ($13.33\%$)$$P \leq 0.54186$$ ΝS3 ($10.00\%$) 50–<604 ($26.67\%$)2 ($13.33\%$)$$P \leq 0.3628$$ ΝS6 ($20.00\%$) 60–<701 ($6.67\%$)3 ($20.00\%$)$$P \leq 0.2843$$ ΝS4 ($13.33\%$) 70–<800 ($0.00\%$)1 ($6.67\%$)$$P \leq 0.3077$$ ΝS1 ($3.33\%$) 80–<901 ($6.67\%$)2 ($13.33\%$)$$P \leq 0.54186$$ ΝS3 ($10.00\%$) 90–<1000 ($0.00\%$)1 ($6.67\%$)$$P \leq 0.3077$$ ΝS1 ($3.33\%$)Seizure frequency reduction after 6 months Seizure free ($100\%$ reduction)9 ($60.00\%$)7 ($46.67\%$)$$P \leq 0.46540$$ NS16 ($53.33\%$) SFR (if still convulsions)6 ($40.00\%$)8 ($53.33\%$)14 ($46.67\%$) 501 ($6.67\%$)1 ($6.67\%$)NA2 ($6.67\%$) 50–<602 ($13.33\%$)2 ($13.33\%$)NA4 ($13.33\%$) 60–<701 ($6.67\%$)2 ($13.33\%$)$$P \leq 0.54186$$ NS3 ($10.00\%$) 70–<801 ($6.67\%$)2 ($13.33\%$)$$P \leq 0.54186$$ NS3 ($10.00\%$) 80–<901 ($6.67\%$)1 ($6.67\%$)NA2 ($6.67\%$) 90–<1000 ($0.00\%$)0 ($0.00\%$)NA0 ($0.00\%$)SFR seizure frequency reduction, NS non significant, χ2 (Fr) Friedman Chi-Square, df degree of freedom.*Statistically significant.#NA = non applicable. Bold values refer to the total number of cases who became seizure-free or showed seizure frequency reduction 3 months after KD and followed by details of seizure frequency reduction.
The best seizures control was observed in genetic epilepsy, Sturge–Weber syndrome, tuberous sclerosis, Miller–Dieker syndrome and white matter disease causing leukodystrophy, each of them (11 cases) became seizure free 6 months after KD, followed by cases of post-traumatic epilepsy and post-anoxic epilepsy who responded well to the KD treatment with 2 out of 3 and 3 out of 4, respectively, were seizure free and seizures in the remaining $\frac{1}{3}$ and $\frac{1}{4}$ of them were reduced by >80–$90\%$. On the other hand, the efficacy of KD for epilepsy of unknown etiology (12 cases) was poor as no patients became seizure free and seizure reduction ranged from >50 to <$80\%$.
In addition to seizure control, we observed that $46.67\%$ of patients in classic KD group and $66.67\%$ of patients in MAD group showed attention improvement 6 months after initiation of KD.
## KD and ketosis
Our results did not find any correlation between the level of urinary ketones and seizure control 3 and 6 months after KD with a P value of 0.197 and OR 0.300.
## Adverse effects
Our results revealed that gastrointestinal (GI) complications were frequent in our studied cases and constipation was the most common with the same occurrence in the two groups ($33.33\%$), followed by diarrhea with a lower occurrence in classic KD group ($13.33\%$) than MAD group ($20\%$) then vomiting ($20\%$) in classic KD group and ($6.67\%$) in MAD group. We did not document renal/genitourinary complications in our study.
## KD and cardiovascular risk
In our study, median level of HDL decrease did not reach a low level till the end of the study for 24 months. Also, median level of LDL, total cholesterol, and TG increase did not reach a high level (remained in the acceptable level) throughout the study period, without a significant difference between classic KD group and MAD group. Figure 3 demonstrates serial measurements of lipid profile from baseline up to 24 months. Fig. 3Lipid profile of cases throughout the study (24 months).a Simple line graph of median of serum HDL (mg/dL) in the studied groups. b Simple line graph of median of serum LDL (mg/dL) in the studied groups. c Simple line graph of median of serum total cholesterol (mg/dL) in the studied groups. d Simple line graph of median of serum triglycerides (mg/dL) in the studied groups.
## KD and antiepileptic drugs
Upon regular follow-up visits every 3 months after the first 6 months, it was possible to withdraw one AEDs in three patients ($20\%$) of classic KD group and five patients ($33.33\%$) of MAD groups, in addition to withdrawal of two AEDs in three patients ($20\%$) in classic KD group and two patients ($13.33\%$) in MAD group leading to less AEDs side effects and better QoL while on adequate seizure control by KD.
## KD and growth
The current study found a positive impact of KD on growth of studied cases (weight loss and BMI reduction were minimal) except in patients who were significantly overweight at diet initiation. Median weight and BMI of cases up to 24 months are illustrated in Fig. 4.Fig. 4Weight and body mass index of cases throughout the study (24 months).a Simple line graph of median ($95\%$ CI) of BMI SD in the studied groups. b Simple line graph of median ($95\%$ CI) of weight SD in the studied groups.
## Discussion
Forty patients with DRE were initially enrolled in this prospective study, but only 30 patients successfully completed it. Six patients ($15\%$) were unable to tolerate KD and were removed from the study ($85\%$ tolerability), three additional patients were excluded due to parental noncompliance and one case died during the study period. Overall reasons for dropout were mostly intolerance of the diet, AEs (mostly GI tract related), weight loss, parental unhappiness and change of mind.40 Meta-analysis studies have confirmed the efficacy of the KD and showed a seizure frequency reduction (SFR) of ≥$50\%$ for both the classic KD and MAD.41 Our findings revealed that $60\%$ of patients in classic KD group and $46.67\%$ of patients in MAD group became seizure free and the other $40\%$ and $53.33\%$, respectively, had ≥$50\%$ SFR. Also, the effectiveness of the KD treatment showed an increased tendency over time, with a median decrease in seizure frequency of −$83.33\%$ and −$100\%$ versus −$75\%$ and −$100\%$ after 3 and 6 months in the classic KD group and MAD group respectively compared to patients’ baseline.
With a median percent change of −$100\%$, we also documented a statistically significant reduction in seizure severity based on the CSSS 3 and 6 months after the KD compared to baseline in both groups. Two RCTs reported a statistically significant decrease in seizure severity, El-Rashidy et al.18 with a mean reduction in seizure severity of $37.63\%$ (MAD) and $35.89\%$ (KD) after 6 months, and Lambrechts et al.42 with a mean reduction of $65.2\%$.
The KD enhanced patients’ cognitive and functional status while also reducing seizure severity and frequency. Six months following KD, we noticed improvements in functional status and cognition, with $66.67\%$ of patients in the MAD group and $46.67\%$ of patients in the classic KD group displaying better attention. This is essential for DRE patients since uncontrolled seizures and the use of numerous anticonvulsants may negatively impact cognition, behavior, drowsiness, memory, and attention issues.43,44 These cognitive enhancements might be attributable to the KD given as the mean number of AEDs did not change until 6 months after the diet therapy.
Several studies revealed the effectiveness of KD for epileptic syndromes such as myoclonic-astatic epilepsy, Rett syndrome, West syndrome (particularly combined with tuberous sclerosis), and Dravet and Doose syndromes.21,45–48 In our study, all 3 patients with focal seizures, $\frac{4}{5}$ of patients with generalized tonic-clonic seizures, $\frac{3}{6}$ with infantile spasms, $\frac{5}{10}$ with myoclonic seizures and $\frac{1}{6}$ with unclassified seizures became seizure free after 6 months after KD initiation with no significant difference between the two groups. However, because of the small sample size, we were unable to draw any conclusions about which type of seizure was linked to better seizure control.
Although the KD has been proven to be a successful treatment for reducing seizures in DRE patients, its wider effects on cerebral neurophysiology are less clear.49 KD not only demonstrated good clinical efficacy in this study, but it also significantly reduced the frequency of interictal epileptic discharges and improved the EEG background rhythm. This was evident in 22 patients ($73.33\%$) who had baseline EEG with epileptiform abnormalities (11 in each group) with a reduction in the SI >$50\%$ in 8 patients after 1 month, which increased to 14 patients after 3 months and 16 after 6 months. Zhu et al.50 reported ≥$50\%$ reduction in seizure frequency and reduction of epileptiform discharges in the awake state in $69.0\%$ of patients after 3 months of KD treatment.
KD side effects are frequently blamed for trial dropouts since they are observed in a high percentage of young patients.7 More than forty different types of AEs were found with cardiovascular, renal/genitourinary, skeletal systems, and GI (mainly constipation) being the most prevalent.22,51 In our study, the most common AEs were GI related, with constipation reported in $33.33\%$ of patients in each group and we did not report any renal/genitourinary AEs in our study. *In* general, AEs were transient, well controlled by conservative management and did not necessitate for diet discontinuation.
Lipid profile underwent frequent changes throughout the study period. However, HDL median levels did not reach the low level and the median of LDL, total cholesterol and TG measurements remained within the acceptable range till the end of the study for 24 months with a non-significant difference between classic KD and MAD. Kossoff et al., in 2006, 2007, and 2008 over the course of these investigations, observed that there was a comparable increase in total cholesterol and LDL levels, which was within the accepted value.14,52,53 Contrarily, Coppola et al.54 observed hyperlipidemia as a side effect in their group of patients receiving liquid ketogenic formula for refractory epileptic encephalopathies. Variations of the lipid profile especially during the first 12 months of the diet have been described in up to $60\%$ of children22 and may happen during the first month but tend to normalize within the first few months following the diet’s introduction.24,55–57 Acceptable change of lipid profile with a long-term duration of KD (24 months) can be a good indicator for the safety of high-fat diet on cardiovascular system in children with DRE.
Systematic reviews have discovered conflicting results about the KD’s effects on growth, with some indicating a favorable benefit and others indicating a negative impact.55 Poor caloric and protein intake, acidosis or ketosis, the effects of underlying illnesses and therapies, ambulatory status, and related endocrine changes are some of the etiologies of poor growth in children on KDTs.58,59 The current study found that children who were underweight at diet onset [11 ($36.67\%$) underweight and 7 ($23.33\%$) wasted] showed an increase in Z scores of child weight over time. However, those who were overweight at diet onset and remained on the diet showed a decrease in the Z score over the longer term. This positive impact on growth highlights that fears from KD regarding weight loss were not evident.
Due to their excellent response to the diet, it was possible to withdraw AEDs in 13 patients ($43.33\%$) after the first 6 months: one AEDs in 8 patients (3 in classic KD group vs. 5 in the MAD group) and two AEDs in 5 patients (3 in classic KD group vs. 2 in the MAD group). This can be attributed to the patients’ functional status and QoL.
The study’s points of strength include its prospective nature, which increased the accuracy of our data. We studied two types of KD (classic KD and MAD) commonly used for DRE to allow more options and less diet restrictions. In addition, the effectiveness of the KD regarding seizure control was not measured based solely on caregiver reports but included the EEG changes so allowing more objective evaluation and avoiding subjective errors. Along with growth tracking and documenting of the KD’s effects on growth, we also evaluated the KD’s safety for up to 24 months in terms of side effects and cardiovascular risk.
## Limitation of the study
The main limitations of our study were the small sample size and the heterogeneity of the enrolled patients, which influenced the lack of statistical significance. Another point of limitation was the dropped-out cases due to noncompliance as we started the study with 40 patients and only 30 patients completed the study; the most common reasons for discontinuing the diet were intolerability and poor parental compliance when maintaining the diet.
## Conclusion
With no major side effects reported and a positive influence on EEG and growth, KD (classic KD and MAD) appears to be an effective and generally well-tolerated therapy in the treatment of children with DRE. The role of KD as a single line of therapy, whether from the beginning or after withdrawal of all other medications once full control is established, is also recommended to be clarified by more research.
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|
---
title: Specificity protein 1-mediated ACSL4 transcription promoted the osteoarthritis
progression through suppressing the ferroptosis of chondrocytes
authors:
- Wen He
- Xuchao Lin
- Kangyao Chen
journal: Journal of Orthopaedic Surgery and Research
year: 2023
pmcid: PMC10007726
doi: 10.1186/s13018-023-03673-0
license: CC BY 4.0
---
# Specificity protein 1-mediated ACSL4 transcription promoted the osteoarthritis progression through suppressing the ferroptosis of chondrocytes
## Abstract
### Background
Chondrocytes are the main cell damage type involved in the occurrence and development of osteoarthritis (OA). Ferroptosis has been confirmed to be related to many degenerative diseases. This research aimed to explore the role of Sp1 and ACSL4 in ferroptosis in the IL-1β-treated human chondrocyte cells line (HCCs).
### Methods
The cell viability was detected with CCK8 assay. The ROS, MDA, GSH, and Fe2+ levels were assessed with corresponding detecting kits. The Col2a1, Acan, Mmp13, Gpx4 and Tfr1 levels were determined by RT-qPCR assay. Western blot was conducted to evaluate the Acsl4 and Sp1 levels. PI staining was carried out to analyze the cell death. The double luciferase report was conducted to verify the interaction between Acsl4 and Sp1.
### Results
The results showed that IL-1β stimulation elevated the LDH release, cell viability, ROS, MDA and Fe2+ levels and declined the GSH levels in the HCCs. Additionally, the mRNA levels of Col2a1, Acan, and Gpx4 were prominently decreased, while Mmp13 and Tfr1 were prominently elevated in the IL-1β stimulated HCCs. Furthermore, Acsl4 protein levels were upregulated in the IL-1β-stimulated HCCs. Both Acsl4 knockdown and ferrostatin-1 treatment neutralized the role of IL-1β in the HCCs. What’s more, Acsl4 was transcriptionally regulated by Specificity protein 1 (Sp1). Sp1 overexpression enhanced the Acsl4 levels and Sp1 knockdown declined it.
### Conclusion
Upregulation of Sp1 activates Ascl4 transcription and thus mediates the occurrence of ferroptosis. Hence, Acsl4 may be a therapeutic target for intervention of OA.
## Introduction
Osteoarthritis (OA) is a chronic disease characterized by articular cartilage degeneration, cartilage ossification and secondary hyperosteogeny [1, 2]. OA most often affects the knee joint, resulting in severe joint deformity and joint dysfunction. In severe cases, it can cause disability, which directly affects the quality of life of the middle-aged and elderly [3]. The pathogenesis of OA is complex, and inflammatory factors, metabolism and other factors are closely related to OA. As the only cellular component of cartilage, chondrocytes are the main cell damage type involved in the occurrence and development of OA [4]. Research showed that chondrocyte injury can be divided into cell necrosis, apoptosis and autophagy cell death [5].
In 2012, S. J. Dixon et al. [ 6] first reported a new form of cell regulated death, which is different from other forms of cell death in morphology, biochemistry and gene, and named it ferroptosis. Ferroptosis is an iron-dependent non-apoptotic cell death characterized by inactivation of antioxidant enzyme glutathione peroxidase 4 (GPX4) and accumulation of lipid reactive oxygen species [7]. So far, ferroptosis has been confirmed to be related to many degenerative diseases (such as Alzheimer's disease, Parkinson's disease, renal degeneration), carcinogenesis, intracerebral hemorrhage, traumatic brain injury, ischemia–reperfusion injury and stroke [8–11].
Acyl coenzyme A synthetase long-chain member 4 (ACSL4), a member of the long-chain acyl CoA synthetase family, catalyzes the synthesis of acyl CoA in vivo as the first step of fatty acid catabolism [12]. Previous study found that ACSL4 is a key gene in the ferroptosis pathway, which can synthesize arachidonic acid and adrenal acid into arachidonic acid CoA and adrenal acid CoA, respectively, to participate in membrane phospholipid synthesis [13]. Under the treatment of RSL3, a ferroptosis inducer, the long-chain polyunsaturated fatty acids on the membrane are easy to be oxidized, leading to ferroptosis [14]. However, there are few studies on ACSL4-mediated ferroptosis in the progress of OA.
Specificity protein (Sp1) is a member of the SP/KLF transcription factor family, which is located in the nucleus [15]. Sp1 transcription factor is widely expressed in vivo and is involved in regulating the expression of many genes in mammalian cells [16]. Sp1 can interact with a series of proteins, including other transcription factors and epigenetic regulatory factors [17, 18]. Chromosome mapping studies have shown that there are at least 12,000 Sp1 binding sites in the human genome, which are related to genes involved in most cell processes [19]. A recent study has demonstrated the Sp transcription factor family was closely related to the OA progression, whose function in osteosarcoma development was confirmed by bioinformatic analysis [20].
Therefore, this research aimed explore the functions of Sp1 induced ferroptosis in OA progression. We hypothesized that Sp1 was upregulated in OA, which activates the transcription of Ascl4 to induce ferroptosis of.
## Cell culture and treatment
Human chondrocyte cells line CHON-001 (HCCs) were purchased from ATCC. Cells were maintained in DMEM medium (Gibco, GI, USA) containing $10\%$ fetal bovine serum (FBS, Gibco) and $1\%$ penicillin–streptomycin. Then the cells were incubated at 37 °C with an atmosphere of $5\%$ CO2 and $95\%$ humidity. Next, the HCCs were treated with IL-1β(10 ng/ml) for 24 h to establish an OA model in vitro. Additionally, in order to inhibited ferroptosis, 1 µM of Ferrostatin-1 (Fer-1) was used to treat the HCCs.
## Cell transfection
Small interfering RNA Acsl4 (si-Acsl4 1#, si-Acsl4 2#), small interfering RNA Sp1 (si-Sp1 1#, si-Sp1 2#) and their controls (si-nc), Sp1 overexpression vector (oe-Sp1) and empty vector (oe-nc) were all obtained from GenePharma (Shanghai, China). HCCs were seeded in 6-well plates (2 × 105 cells/ well). The plasmids were transfected into HCCs using Lipofectamine 3000 reagent (Life Technologies, USA), with all procedures following the manufacturer’s protocol. The siRNA primer sequence:si-Acsl4 1#, 5′- GGGAGUGAUGAUGCAUCAUAGCAAU-3′;si-Acsl4 2#, 5′- GGCUUCCUAUCUGAUUACCAGUGUU-3′;si-Sp1 1#, 5′-AUGAUCUGUAUUUGACCAGTT-3′;si-Sp1 1#, 5′-UAUUUGGGAUGAUCUGUUGGUTT-3′;si-nc, 5′-ACGUGACACGUUCGGAGAATT-3′.
## Cell viability determination
A CCK-8 kit (Beyotime, Shanghai, China) was purchased to access cell viability of HCCs. In short, cells in each group were seeded in 96-well plates (2 × 103 cells/ well) and cultured for 48 h. Then, HHCs were treated with CCK8 solution reagent for 2 h, and 450 nm absorbancy was chosen in a microreader to assess cell viability.
## LDH release, MDA, ROS, GSH, and Fe2+ levels determination
The LDH release, MDA, ROS, GSH, and Fe2+ levels of the HCCs in each group were assessed the with Corresponding kits purchased from Nanjing Jiangcheng Bioengineering Institute (Nanjing, China). All procedures were followed the manufacturer’s protocol.
## PI staining assay
HCCs were washed with PBS and fixed with cold $70\%$ ethanol at 4 ℃ for 60 min. Then the cells were stained with 1 mL PI (sigma) at 4 ℃ for 30 min. The samples were counterstained with DAPI. The fluorescence signal was observed by a fluorescence microscope.
## Immunofluorescence staining
The cells were fixed with $4\%$ paraformaldehyde for 15 min and washed with PBS. Next, the cells were permeated with 0. $2\%$ Triton X-100 for 5 min, and cultivated with $5\%$ BSA for 30 min. After the blocking solution was discarded, the cells were incubated with anti-Ascl4 overnight. The next day, after rewarmed for 30 min, the cells were incubated with secondary antibody for 1 h. The nucleus was stained with DAPI for 4 min in darkness. Images were acquired on a fluorescence Nikon (DIAPHOT) fluorescence microscope.
## qRT-PCR
qRT-PCR was conducted to measure the expression levels of Col2a1, Acan, Mmp13, Gpx4, and Tfr1. Total RNA of cells was extracted by Trizol (Beyotime), and cDNA was synthesized using the total RNA and a reverse transcription kit (Vazyme, Nanjing, China). Afterward, the cDNA was used as the template for qRT-PCR amplification (Bio-Rad, CFX-96) with a SYBR Green Mix kit (Vazyme). *Relative* gene expression was calculated using the 2−ΔΔCt method, and GAPDH was selected as the internal control. PCR primer sequences were listed below (5′ → 3′): Col2a1, F:TGGACGATCAGGCGAAACC and R: GCTGCGGATGCTCTCAATCT; Acan, F: ACTCTGGGTTTTCGTGACTCT and R: ACACTCAGCGAGTTGTCATGG; Mmp13, R: TCCTGATGTGGGTGAATACAATG and R: GCCATCGTGAAGTCTGGTAAAAT; Gpx4, F: AGTACAGGGGTTTCGTGTGC and R: CATGCAGATCGACTAGCTGAG; Tfr1, F: ATGTTGGATGGGTAGCCAAAG and R: TTCGAGAGCGCAAATCTTCTG; Acsl4, F: CATCCCTGGAGCAGATACTCT and R: TCACTTAGGATTTCCCTGGTCC; GAPDH, F: TGTGGGCATCAATGGATTTGG and R: ACACCATGTATTCCGGGTCAAT.
## Western blot analysis
Total protein was extracted with RIPA buffer (Beyotime) and quantified by the BCA protein detection kit (Beyotime) in accordance with the protocols. Proteins were electrophoresed on $10\%$ SDS-PAGEs and transferred to PVDF membranes (Millipore). Afterward, membranes were blocked with $5\%$ nonfat milk for 1 h and treated with the specific primary antibodies against Acsl4 (1:1000, abcam), Sp1 (1:200, abcam) and GAPDH (1:2000, abcam) at 4 ℃ overnight. Following conjugation to HRP-labeled secondary antibody, the bands were visualized with a super ECL kit following the instructions (Beyotime).
## Co-Immunocoprecipitation (CO-IP)assay
The combination between Ascl4 and Sp1 in HCC cells was detected according to the instructions of CO-IP kit (Biomars Technology Development Co., Ltd, Beijing, China). IgG was used as the control, 5 μg Ascl4/Sp1 antibody was added into the reaction tube. Then the protein levels of Ascl4/Sp1 and GAPDH in anti-Ascl4/Sp1 and IgG precipitation were detected by Western blot. IgG group is used as control.
## Luciferase reporter assay
HCCs were cultivated in 24-well plates and cotransfected with the Sp1 overexpressing plasmids and the report vector carrying wild type or mutant promoter of Ascl4. 48 h after transfection, Luciferase activities were measured using a Dual-Luciferase Reporter Assay System (Promega).
## Statistical analysis
The experimental data were analyzed by GraphPad Prism and was represented by mean ± SD. The comparison between two groups was compared with T test. One-way ANOVA followed by Tukey’s post hoc test was used to compare the difference between multiple groups l. $P \leq 0.05$ means the difference is statistically significant.
## Ferroptosis exists in IL-1β-treated cells
In the HCCs, after IL-1β treatment, the LDH release (Fig. 1A, $P \leq 0.0001$), ROS (Fig. 1C, $$P \leq 0.0019$$), MDA (Fig. 1D, $P \leq 0.0020$), and Fe2+ levels (Fig. 1F, $P \leq 0.0001$) were dramatically enhanced, while cell viability (Fig. 1B, $$P \leq 0.0058$$) and GSH levels (Fig. 1E, $$P \leq 0.0006$$) were dramatically declined. After Fer-1 treatment, the role of IL-1β in the HCCs were neutralized. Additionally, Fer-1 showed no effects on the normal HCCs. Fig. 1Ferroptosis occurred in IL-1β-treated cells. After IL-1β and Fer-1 treatment in the HCCs, the LDH release A was measured with LDH Toxicology Kit. Cell viability B was detected by CCK-8 assay. The ROS (C), MDA (D), GSH (E), and Fe2+ F levels were assessed with Corresponding kits
## IL-1β treatment induced the changes in ferroptosis-related genes
Then, in the HCCs, after IL-1β treatment, the mRNA levels of Col2a1 (Fig. 2A, $$P \leq 0.0017$$), Acan (Fig. 2B, $$P \leq 0.0007$$), and Gpx4 (Fig. 2D, $$P \leq 0.0008$$) were prominently depleted, while mRNA levels of Mmp13 (Fig. 2C, $P \leq 0.0001$), Tfr1 (Fig. 2E, $$P \leq 0.0002$$), and PI-positive cells (Fig. 2F, $$P \leq 0.0001$$) were prominently elevated. After Fer-1 treatment, the role of IL-1β in the HCCs was neutralized. Additionally, Fer-1 showed no effects on the normal HCCs. Fig. 2IL-1β treatment induced the changes in ferroptosis-related genes. After IL-1β and Fer-1 treatment in the HCCs, the mRNA levels of Col2a1 (A), Acan (B), Mmp13 (C), Gpx4 (D) and Tfr1 E were determined by RT-qPCR assay. F The cell death was analyzed by PI staining
## Acsl4 was overexpressed in IL-1β-treated HCCs
Acsl4 has been identified as one of the molecular markers of Ferroptosis. As displayed in Fig. 3A and B, we found that IL-1β treatment dramatically elevated mRNA and protein levels of Acsl4 in the HCCs, while Fer-1 treatment dramatically depleted the mRNA and protein levels of Acsl4 in both the normal HCCs and IL-1β-treated HCCs. Additionally, the immunofluorescence showed the same results as PCR and Western blot (Fig. 3C).Fig. 3Acsl4 was overexpressed in IL-1β-treated HCCs. After IL-1β and Fer-1 treatment in the HCCs, the Acsl4 levels were analyzed by PCR (A), Western blot (B), and immunofluorescence (C)
## Acsl4 knockdown neutralized the ferroptosis progression in the IL-1β-treated HCCs
As ACSL4 was significantly upregulated in IL-1beta-induced ferroptosis, we next checked whether ACSL4 silencing could inhibit the ferroptosis. After si-Acsl4 transfection, the Acsl4 levels were prominently depleted (Fig. 4A). The transfection efficiency of si-Acsl4 1# ($$P \leq 0.0026$$) was higher than that of si-Acsl4 2# ($$P \leq 0.0058$$). Therefore, si-Acsl4 1# was used for the next experiments. Then we found that in the IL-1β-treated HCCs, after si-Acsl4 transfection, the LDH release (Fig. 4C, $$P \leq 0.0010$$), ROS (Fig. 4E, $$P \leq 0.0028$$), MDA (Fig. 4D, $$P \leq 0.0057$$), and Fe2+ levels (Fig. 4H, $$P \leq 0.0005$$) were dramatically depleted, while cell viability (Fig. 4D, $$P \leq 0.0082$$) and GSH levels (Fig. 4G, $$P \leq 0.0035$$) were dramatically enhanced. Fig. 4Acsl4 knockdown neutralized the ferroptosis progression in the IL-1β-treated HCCs. Transfection efficiency of si-Acsl4 was analyzed by RT-qPCR (A). After IL-1β and si-Acsl4 treatment in the HCCs, the LDH release B was measured with LDH Toxicology Kit. Cell viability C was detected by CCK-8 assay. The ROS (D), MDA (E), GSH (F), Fe2+ G levels were assessed with Corresponding kits
## Acsl4 knockdown neutralized the roles of IL-1β in the levels of ferroptosis-related genes
Subsequently, we found that in the IL-1β-treated HCCs, after si-Acsl4 transfection, the mRNA levels of Col2a1 (Fig. 5A, $$P \leq 0.0010$$), Acan (Fig. 5B, $$P \leq 0.0016$$), and Gpx4 (Fig. 5D, $$P \leq 0.0076$$) were prominently elevated, while mRNA levels of Mmp13 (Fig. 5C, $$P \leq 0.0010$$), Tfr1 (Fig. 5E, $$P \leq 0.0009$$), and PI-positive cells (Fig. 5F, $$P \leq 0.0014$$) were prominently depleted. Fig. 5Acsl4 knockdown neutralized the roles of IL-1β in the levels of ferroptosis-related genes. After IL-1β and si-Acsl4 treatment in the HCCs, the mRNA levels of Col2a1 (A), Acan (B), Mmp13 (C), Gpx4 (D) and Tfr1 E were determined by RT-qPCR assay. ( F) The cell death was analyzed by PI staining
## Sp1 regulated the Ascl4 levels in the IL-1β-treated HCCs
Then we found that IL-1β-treated significantly increased the Sp1 levels in the HCCs (Figure 6A, $$P \leq 0.00110$$). After si-Sp1 transfection, the Sp1 levels were declined. The transfection efficiency of si-Sp1 1# ($$P \leq 0.0003$$) was higher than that of si-Sp1 2# ($$P \leq 0.0009$$). Therefore, si-Sp1 1# was used for the next experiments. Oe-Sp1 transfection prominently elevated the Sp1 levels (Fig. 6B, $P \leq 0.0001$). Then we found that si-Sp1 transfection dramatically attenuated Acsl4 levels in both normal HCCs (Fig. 6C, $$P \leq 0.0004$$) and IL-1β-treated HCCs (Fig. 6C, $$P \leq 0.0007$$) at mRNA and protein levels (Fig. 6E). Meanwhile, oe-Sp1 transfection dramatically elevated the Acsl4 levels in both normal HCCs (Fig. 7D, $$P \leq 0.0005$$) and IL-1β-treated HCCs (Fig. 7D, $$P \leq 0.0103$$) at mRNA and protein levels (Fig. 6F).Fig. 6Sp1-regulated Ascl4 levels in the IL-1β-treated HCCs. Sp1 levels in the IL-1β-treated HCCs were detected by RT-qPCR assay (A). Transfection efficiency of si-Acsl4 and oe-Sp1 was analyzed by RT-qPCR (B). After si-Sp1 C, E or oe-Sp1 D and F transfection, the Ascl4 levels in the IL-1β-treated HCCs were detected by RT-qPCR and Western blotFig. 7Acsl4 is a direct target gene of Sp1. A, B Predicted promoter region of Acsl4. The combination between Ascl4 and Sp1 was demonstrated by CO-IP (C) and double luciferase report D assays
## ACSL4 was targeted by Sp1
After confirming the function of ACSL4 in IL-1beta-induced ferroptosis in HCCs, we started to think that how ACSL4 was upregulated. As is well known, gene expression could be enhanced by transcriptional or post-transcriptional regulation. Here, we focused on the transcriptional regulation of ACSL4 in HCCs. First, bioinformatics analysis (https://jaspar.genereg.net/) was performed to predict the transcription factors targeting ACSL4. As showed by the results, the sequence between − 91 and − 101 bp in the Acsl4 promoter region was predicted to bind with Sp1 (Fig. 7A, B). Additionally, the CO-IP assay confirmed the combination between ACSL4 and Sp1 (Fig. 7C). To further confirm the interaction between Sp1 and ACSL4, we carried out luciferase assay. It was found that oe-Sp1 dramatically enhanced the luciferase activity of the WT-Acsl4, but did not change that of mut-ACSL4.(Fig. 7D, $P \leq 0.0001$), while si-Sp1 dramatically declined it ($$P \leq 0.0003$$).
## Discussion
Here, we demonstrated that IL-1β treatment induced the occurrence of ferroptosis in HCCs. ACSL4 knockdown alleviated the injury of HCCs induced by IL-1β. The upregulation of ACSL4 was mediated by transcriptionally activation of Sp1.
The main function of chondrocytes is to maintain the integrity of body cartilage and enable articular cartilage to obtain sufficient weight-bearing energy [21]. Articular chondrocyte apoptosis plays an important role in the progression of OA. Relevant research showed that there was excessive apoptosis of chondrocytes in the articular cartilage of OA [22, 23]. Ferroptosis is a newly defined programmed cell death process different from apoptosis and autophagy, which is characterized by the abnormal increase of intracellular lipid oxygen-free radicals [24]. Previous studies have demonstrated that ferroptosis played an critical role in OA progression. For example, Yao et al. [ 25] found that Fer-1 treatment might be a promising therapeutic tools for the OA through inhibiting the ferroptosis development. Zhou et al. [ 26] proved that D-mannose played a chondroprotective role and relieved the OA progression through mitigating the sensitivity of chondrocytes to ferroptosis. Similarly, our research also confirmed that ferroptosis occurred in the IL-1β-treated HCCs, which was manifested as the increase of ROS, MDA, and Fe2+ levels and the decrease of GSH levels. Fer-1 treatment neutralized it. Thus, the importance of ferroptosis in the development of OA is self-evident. However, the mechanism of ferroptosis-related genes wasn’t clarified.
As the first step in fatty acid metabolism, Acsl4 catalyzes the synthesis of fatty acyl CoA in vivo and activates long-chain polyunsaturated fatty acids to participate in the synthesis of membrane phospholipids. However, long-chain unsaturated fatty acids on these membranes are often oxidized, resulting in ferroptosis [27]. TfrI is a membrane protein transferrin receptor on the cell membrane. When ferroptosis occurs, it transfers Fe3+ into cells and localizes to endosomes when ferroptosis occurs [28, 29]. As a sensor of oxidative stress and cell death signals, the decrease of Gpx4 expression will lead to the significant increase of ROS in vivo, which is considered to be an important target to trigger ferroptosis program [30]. Additionally, Col2a1 mutation can lead to orthopedic diseases such as insufficient cartilage formation, osteoarthritis, congenital spondyloepiphyseal dysplasia, etc. Acan and Col2a1 are the main components of cartilage matrix [31]. IL-1β can stimulate the synthesis of matrix metalloproteinases (MMPs), which can promote the degradation of cartilage matrix [32]. This study found that IL-β stimulation prominently induced the decrease of Col2a1, Acan, and Gpx4, and the increase of Mmp13 and Tfr1 in the HCCs. And Fer-1 treatment neutralized it. Interestingly, we found that Fer-1 treatment decreased the Acsl4 levels in both the normal HCCs and IL-1β-treated HCCs, while other related genes levels in the normal HCCs showed no difference after Fer-1 treatment. Therefore, we speculated that Acsl4 might be the key in the IL-1β-treated HCCs. After the performance of rescue experiment, we found Acsl4 knockdown prominently neutralized the injury of HCCs induced by IL-1β stimulation. All these findings indicated Acsl4-mediated ferroptosis promoted the OA progression.
Subsequently, after bioinformatic analysis, we found the sequence between − 91 and − 101 bp in the Acsl4 promoter region is predicted to bind Sp1. As a classical transcription factor, Sp1 has been demonstrated to bind to the target gene promoter through the DNA binding domain to activate or inhibit the transcription of a variety of target genes, such as CyclinD, E-cadherin, transforming growth factor β, histone deacetylation, etc., thereby regulating cell cycle, angiogenesis, apoptosis, tumorigenesis and development, chromatin remodeling and other biological processes [16, 33]. However, the role of Sp1 in OA development remains unclear. This study further confirmed that Sp1 was located in the nucleus of HCCs. Sp1 knockdown declined the Acsl4 levels and Sp1 overexpression elevated it. Double luciferase report further confirmed the relationship between Sp1 and Acsl4. Li et al. [ 9] found Sp1 enhanced the Acsl4 expressions through binding to the promoter region in intestinal ischemia/reperfusion injury mice, which was similar to our results. However, there are still some limitations in this study.
In conclusion, this study demonstrated that Acsl4-mediated ferroptosis played a critical role in OA progression. Sp1 can elevated the Acsl4 levels through binding to the ACSL4 promoter region. These results guided us to extend ferroptosis inhibition to OA treatment. Our study is a basic research and does not involve in vivo experiments. In the future, we will carry out in vivo experiments for further research.
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|
---
title: 'Sex-related differences in the hypertriglyceridemic-waist phenotype in association
with hyperuricemia: a longitudinal cohort study'
authors:
- Huihui He
- Suhang Wang
- Tianwei Xu
- Wenbin Liu
- Yueping Li
- Guangyu Lu
- Raoping Tu
journal: Lipids in Health and Disease
year: 2023
pmcid: PMC10007733
doi: 10.1186/s12944-023-01795-2
license: CC BY 4.0
---
# Sex-related differences in the hypertriglyceridemic-waist phenotype in association with hyperuricemia: a longitudinal cohort study
## Abstract
### Background
There is limited longitudinal evidence supporting the association between the hypertriglyceridemic-waist (HTGW) phenotype and hyperuricemia. This study aimed to examine the longitudinal relationship between hyperuricemia and the HTGW phenotype among males and females.
### Methods
A total of 5562 hyperuricemia-free participants aged 45 or over from the China Health and Retirement Longitudinal Study (mean age: 59.0) were followed for 4 years. The HTGW phenotype was defined as having elevated triglyceride levels and enlarged waist circumference (cutoffs for males: 2.0 mmol/L and 90 cm; females: 1.5 mmol/L and 85 cm). Hyperuricemia was determined by uric acid cutoffs (males: 7 mg/dl; females: 6 mg/dl. Multivariate logistic regression models were used to assess the association between the HTGW phenotype and hyperuricemia. The joint effect of the HTGW phenotype and sex on hyperuricemia was quantified, and the multiplicative interaction was assessed.
### Results
During the four-year follow-up, 549 ($9.9\%$) incident hyperuricemia cases were ascertained. Compared with those with normal levels of triglycerides and waist circumference, participants with the HTGW phenotype had the highest risk of hyperuricemia (OR: 2.67; $95\%$ CI: 1.95 to 3.66), followed by an OR of 1.96 ($95\%$ CI: 1.40 to 2.74) for only higher triglyceride levels and 1.39 ($95\%$ CI: 1.03 to 1.86) for only greater waist circumference. The association between HTGW and hyperuricemia was more prominent among females (OR = 2.36; $95\%$ CI: 1.77 to 3.15) than males (OR = 1.29; $95\%$ CI: 0.82 to 2.04), with evidence of a multiplicative interaction ($$P \leq 0.006$$).
### Conclusions
Middle-aged and older females with the HTGW phenotype may at the highest risk of hyperuricemia. Future hyperuricemia prevention interventions should be primarily targeted for females with the HTGW phenotype.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12944-023-01795-2.
## Introduction
Hyperuricemia is a metabolic disease caused by excessive production of uric acid or reduced renal excretion and is usually defined as a condition where the level of uric acid exceeds the normal range [1, 2]. Previous studies have revealed that hyperuricemia may increase the risk of several diseases, such as hypertension, diabetes, and kidney disease [3, 4], and lead to gout and nephrolithiasis [2]. However, the prevalence of hyperuricemia has been increasing, e.g., in China, from $8.5\%$ in 2001 to $18.4\%$ in 2017, with the incidence increasing with age [5, 6]. This implies an urgent need to identify people at risk of hyperuricemia.
Previous studies reported that elevated triglycerides and enlarged waist circumference were associated with a higher risk of hyperuricemia [7, 8]. The hypertriglyceridemic-waist (HTGW) phenotype (i.e., coexistence of elevated triglyceride levels and enlarged waist circumference) was first introduced in 2000 and has been confirmed as a measure of increased visceral adiposity and a predictor of chronic kidney disease [9, 10]. A previous study determined the potential mechanism among them, i.e., insulin resistance induced by visceral obesity subsequently reduces the excretion of uric acid from the renal system, resulting in an increased risk of hyperuricemia [11]. To our knowledge, only one study including participants at high risk of cardiovascular disease examined the cross-sectional association of the HTGW phenotype with hyperuricemia, leaving the longitudinal association for the general Chinese population uninvestigated [12].
It is worth noting that sex differences in relation to metabolic syndrome components are common, especially among adults 45 years and older [13]. For example, previous studies have shown a higher prevalence of high triglycerides and high waist circumference among females than males [14]. A cross-sectional study from China observed a higher likelihood among females than males for developing hyperuricemia with higher triglycerides [15]. Recent studies have shown that the correlation between the HTGW phenotype and diabetes and kidney disease might be stronger among females [16, 17]. This aforementioned evidence emphasizes the importance of sex in the association between HTGW and the incidence of hyperuricemia.
In this study, we used 5562 participants from the China Health and Retirement Longitudinal Study (CHARLS) to examine the prospective relationship between the HTGW phenotype and hyperuricemia among middle-aged and older adults. An HTGW phenotype-sex interaction was also investigated.
## Data and sample
The data were obtained from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort survey consisting of community residents aged 45 years or older. Initial samples were recruited from 2011 by multistage probability sampling and followed up every 2 years. Questionnaire surveys and physical measurements are conducted at every follow-up, and blood sample collection is performed once every two follow-up cycles [18, 19]. In the current study, we used three waves of data from CHARLS (2011, 2013, and 2015). As shown in fig. 1, after excluding those who 1) had hyperuricemia or kidney disease or were undergoing chemotherapy for malignancies at baseline ($$n = 1732$$); 2) had missing information on triglycerides ($$n = 10$$), uric acid ($$n = 3$$), waist circumference ($$n = 1524$$) and both triglycerides and uric acid ($$n = 5567$$); 3) were lost or refused to follow-up ($$n = 2926$$); and 4) had no information on uric acid in 2015 ($$n = 13$$), 5562 participants remained in the analytical sample. Fig. 1Flowchart of study participants. Notes: Information on triglycerides, waist circumference and covariates were measured in 2011, and uric acid was measured in 2011 and 2015
## Exposure and outcome
Fasting venous blood samples were collected from participants and tested at the Clinical Laboratory of Capital Medical University in 2011 and 2015 [19]. Triglycerides were measured using an enzymatic color metric test, with an elevated triglyceride level defined as ≥ 1.5 mmol/L for females or ≥ 2.0 mmol/L for males. Waist circumference was measured by trained assessors using soft measuring tape, and enlarged waist circumference was defined as ≥ 85 cm in females or ≥ 90 cm in males [9, 10]. We divided participants into the following four triglyceride-waist phenotypes: 1) NTNW, normal triglyceride levels and normal waist circumference; 2) NTGW, normal triglyceride levels and enlarged waist circumference; 3) HTNW, elevated triglyceride levels and normal waist circumference; and 4) HTGW, elevated triglyceride levels and enlarged waist circumference [10]. Serum uric acid was determined by the Uric Acid Plus method [19]. Hyperuricemia was defined as a serum uric acid concentration ≥ 7 mg/dl in males and ≥ 6 mg/dl in females [1]. To focus on participants with elevated triglyceride levels and enlarged waist circumference and to facilitate the interpretation of the interaction effect between the HTGW phenotype and sex on hyperuricemia, we combined ‘NTNW’, ‘NTGW’ and ‘HTNW’ as ‘non-HTGW’ in the analyses concerning interaction.
## Covariates
Covariates were collected at baseline mainly through standardized questionnaires and anthropometric measurements. Maximum years of schooling (educational level: less than or equal to 6 years vs. more than 6 years), marital status (married vs. nonmarried, i.e., divorced/widowed/single), residential location (rural vs. urban), smoking (current smokers vs. current nonsmokers), alcohol consumption (occasional drinkers, i.e., less than or equal to 3 times per week vs. habitual drinkers, i.e., more than 3 times per week) were dichotomized. Body mass index (BMI) was calculated by dividing weight (kg) by the square of height (m2) and categorized as underweight (< 18.5 kg/m2), normal weight (18.5–23.9 kg/m2), overweight (24–27.9 kg/m2) and obese (≥ 28 kg/m2), according to the revised Asia-Pacific BMI criteria by the World Health Organization [20]. Health status referred to self-reported history of doctor diagnosed diseases (e.g., diabetes, hypertension, and hyperlipidemia) or treatments of these diseases. People who responded affirmatively to one or more diseases were categorized as unhealthy or otherwise healthy.
## Statistical analyses
To test the differences in characteristics between participants with different hyperuricemia statuses, chi-square (χ2) and one-way ANOVA were used for categorical variables and continuous variables, respectively. We also compared the characteristics of those with and without information on triglycerides and waist circumference. Multivariate logistic regression models were performed to detect the associations between the triglyceride-waist phenotypes and hyperuricemia after adjusting for age, sex, education, marital status, residential location, smoking, alcohol consumption, BMI, and health status. Furthermore, the joint effect of the HTGW phenotype and sex on hyperuricemia was quantified, and the two-way multiplicative interaction was examined.
Multiple imputation by chained equations was performed for missing data on triglycerides and waist circumference, and then we repeated the analyses and compared the results with those conducted on the observed data.
To test the reliability in the classification of the HTGW phenotype, we conducted two sensitivity analyses: 1) adjusting the treatment of dyslipidemia as a confounder; 2) people with treatment of dyslipidemia were excluded, and then the main analysis was repeated.
All analyses were performed using Stata 16.0 (Stata Corp, College Station, TX, USA). Odds ratios (ORs) and $95\%$ confidence intervals (CIs) were used to describe the associations.
## Ethics review
All interviewees were required to sign the informed consent form, and the data collection of CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015).
## Demographic characteristics
Table 1 shows the baseline characteristics of participants classified on the basis of their waist circumference and triglyceride levels. Of the 5562 participants at baseline, 3061 ($55.0\%$) were females, with a mean age of 59 years, and 964 ($17.3\%$) participants had the HTGW phenotype. Compared to the participants with normal waist circumference and triglyceride levels, participants with the HTGW phenotype were more likely to be younger, females, current nonsmokers, occasional drinkers, obese, unhealthy, and live in urban areas. Compared with those without missing information on triglycerides and waist circumference, participants with missing information tended to have higher education levels, live in urban areas, and be younger, males, and healthy (Table S1).Table 1Baseline characteristics of 5562 participants aged 45 years and older by triglyceride-waist phenotypes at baselineTotal($$n = 5562$$)NTNW($$n = 2608$$)NTGW($$n = 1397$$)HTNW($$n = 593$$)HTGW($$n = 964$$)Age (years), mean (SD)*59.0 (8.8)59.6 (9.1)59.0 (8.7)57.6 (8.5)58.3 (8.3)Sex, n (%) * Male2501 (45.0)1568 (60.1)524 (37.5)198 (33.4)211 (21.9) Female3061 (55.0)1040 (39.9)873 (62.5)395 (66.6)753 (78.1)Education ≤ 6 years3951 (71.1)1848 (70.9)970 (69.5)429 (72.3)704 (73.0) > 6 years1609 (28.9)759 (29.1)426 (30.5)164 (27.7)260 (27.0)Residential location* Urban830 (15.1)294 (11.4)267 (19.3)76 (13.0)193 (20.4) Rural4662 (84.9)2281 (88.6)1117 (80.7)509 (87.0)755 (79.6)*Marital status* Married4945 (88.9)2304 (88.3)1253 (89.7)522 (88.0)866 (89.8) Nonmarried617 (11.1)304 (11.7)144 (10.3)71 (12.0)98 (10.2)Smoking* Current nonsmokers3898 (70.3)1562 (60.1)1101 (79.0)427 (72.4)808 (84.0) Current smokers1646 (29.7)1036 (39.9)293 (21.0)163 (27.6)154 (16.0)Alcohol consumption* Occasional drinkers4635 (87.7)2022 (83.2)1212 (90.0)520 (90.9)881 (94.1) Habitual drinkers649 (12.3)407 (16.8)135 (10.0)52 (9.1)55 (5.9)Body mass index (kg/m2) * Underweight (< 18.5)320 (5.8)278 (10.7)7 (0.5)34 (5.8)1 (0.1) Normal (18.5–23.9)2924 (53.0)1984 (76.7)338 (24.4)430 (73.5)172 (17.9) Overweight (24–27.9)1635 (29.6)311 (12.0)739 (53.3)107 (18.3)478 (49.8) Obese (≥ 28)641 (11.6)15 (0.6)303 (21.8)14 (2.4)309 (32.2)Health status* Healthy1803 (32.7)957 (37.1)408 (29.3)216 (37.2)222 (23.1) Unhealthy3708 (67.3)1622 (62.9)983 (70.7)364 (62.8)739 (76.9)Notes: 1 missing in age, 2 missing in education, 70 missing in residential location, 18 missing in smoking, 278 missing in drinking, 42 missing in body mass index, 51 missing in health statusNTNW normal triglyceride levels and normal waist circumference; NTGW normal triglyceride levels and enlarged waist circumference; HTNW elevated triglyceride levels and normal waist circumference; HTGW elevated triglyceride levels and enlarged waist circumference. * $P \leq 0.05$
## Triglyceride-waist phenotypes and hyperuricemia
After the four-year follow-up, 549 ($9.9\%$) incident hyperuricemia cases were ascertained. In the fully adjusted model, participants with the NTGW (OR: 1.39; $95\%$ CI: 1.03 to 1.86), HTNW (OR: 1.96; $95\%$ CI: 1.40 to 2.74), and HTGW (OR: 2.67; $95\%$ CI: 1.95 to 3.66) phenotypes had significantly higher hyperuricemia incidence than those with the NTNW phenotype (Table 2). Moreover, the risk of hyperuricemia was obviously higher in participants with the HTGW phenotype (OR: 2.00; $95\%$ CI: 1.58 to 2.54) than in those with the non-HTGW phenotype after adjusting for full covariates (Table 3). Similar results were found in the analyses where uric acid level was treated as a continuous variable (Table 3).Table 2Associations between triglyceride-waist phenotypes and incident hyperuricemiaModel1Model2NOR ($95\%$ CI)POR ($95\%$ CI)PNTNW2608ReferenceReferenceNTGW13971.80 (1.42 to 2.28)< 0.0011.39 (1.03 to 1.86)0.030HTNW5931.94 (1.42 to 2.64)< 0.0011.96 (1.40 to 2.74)< 0.001HTGW9643.60 (2.82 to 4.59)< 0.0012.67 (1.95 to 3.66)< 0.001Notes: NTNW, normal triglyceride levels and normal waist circumference; NTGW normal triglyceride levels and enlarged waist circumference; HTNW elevated triglyceride levels and normal waist circumference; HTGW elevated triglyceride levels and enlarged waist circumference; OR odds ratio; CI confidence intervalModel1: adjusted for age, sex, and educationModel2: adjusted for age, sex, education, marital status, residential location, smoking, alcohol consumption, body mass index, and health statusTable 3Association of HTGW phenotype and hyperuricemia in adults by sexTotalMaleFemale Subtable 1OR ($95\%$ CI)POR ($95\%$ CI)POR ($95\%$ CI)P Non-HTGWReferenceReferenceReference HTGW2.00 (1.58 to 2.54)< 0.0011.29 (0.82 to 2.04)0.2692.36 (1.77 to 3.15)< 0.001TotalMaleFemale Subtable 2β ($95\%$ CI)Pβ ($95\%$ CI)Pβ ($95\%$ CI)P Non-HTGWReferenceReferenceReference HTGW0.36 (0.27 to 0.45)< 0.0010.26 (0.05 to 0.46)0.0130.37 (0.27 to 0.47)< 0.001Notes: Non-HTGW includes 3 phenotypes: NTNW normal triglyceride levels and normal waist circumference; NTGW normal triglyceride levels and enlarged waist circumference; HTNW elevated triglyceride levels and normal waist circumference. HTGW elevated triglyceride levels and enlarged waist circumference; OR odds ratio; CI confidence intervalSubtable 1: The outcome was categorized as hyperuricemia and nonhyperuricemiaSubtable 2: The outcome was calculated by the level of uric acidThe model was adjusted for age, education, marital status, residential location, smoking, alcohol consumption, body mass index, and health status In the sex-stratified analysis, we found that the association of the HTGW phenotype and hyperuricemia was statistically significant in females (OR: 2.36; $95\%$ CI: 1.77 to 3.15; $P \leq 0.001$) but not in males (OR: 1.29; $95\%$ CI: 0.82 to 2.04; $$P \leq 0.269$$) (Table 3). Notably, a significant multiplicative interaction between sex and the triglyceride-waist phenotype for the risk of hyperuricemia (P two-way multiplicative = 0.006) was observed, which suggests that females with the HTGW phenotype had a 1.41-fold ($95\%$ CI: 1.05 to 1.91) higher risk of hyperuricemia than males with non-HTGW conditions (Fig. 2) (Table S2).Fig. 2The interaction between the HTGW phenotype and female sex on the risk of hyperuricemia. Notes: Non-HTGW includes 3 phenotypes: NTNW, normal triglyceride levels and normal waist circumference; NTGW, normal triglyceride levels and enlarged waist circumference; HTNW, elevated triglyceride levels and normal waist circumference. HTGW, elevated triglyceride levels and enlarged waist circumference; OR, odds ratio; CI, confidence interval. The model was adjusted for age, education, marital status, residential location, smoking, alcohol consumption, body mass index, and health status.
## Sensitive analysis
After imputation of missing data on triglycerides and waist circumference, all results remained almost unchanged (Table S3). In addition, similar results were obtained regardless of adjusting the treatment of dyslipidemia or excluding people with treatment of dyslipidemia (Tables S4 and S5).
## Discussion
In this national longitudinal cohort study, we found that both elevated triglyceride levels and enlarged waist circumference (i.e., the HTGW phenotype) were associated with a higher risk of hyperuricemia among middle-aged and older adults. In addition, female sex and the HTGW phenotype interact in their relationship with hyperuricemia, suggesting that the HTGW phenotype was associated with a much higher odds of hyperuricemia in females but not in males.
In our study, the incidence proportion of hyperuricemia was $9.9\%$ after a four-year follow-up, which was comparable with a longitudinal study using the same database (prevalence: $10.12\%$) [8]. The positive association between the HTGW phenotype and hyperuricemia observed in this study was in line with Shuang Chen et al., who showed a cross-sectional association between the HTGW phenotype and a higher prevalence of hyperuricemia among 11,576 Chinese adults (aged ≥35 years) [12]. Our study extends their work by providing longitudinal evidence.
Furthermore, the present study showed that the association between the HTGW phenotype and hyperuricemia was modified by sex, with females experiencing the highest risk. This is consistent with a prospective study that considered the triglyceride-glucose index (Tyg) (a marker of insulin resistance) as a better index of hyperuricemia in females (OR: 6.08; $95\%$ CI: 4.43 to 8.34) than in males (OR: 2.68; $95\%$ CI: 2.11 to 3.41) [21]. However, Shuang Chen et al. showed that males with the HTGW phenotype (OR: 4.59; $95\%$ CI: 3.53 to 5.98) had a higher risk of hyperuricemia than females (OR: 3.55; $95\%$ CI: 2.60 to 4.86) [12]. One of the possible explanations for this inconsistent finding may be the limitation to the rural population, thus, the causal association between the HTGW phenotype and hyperuricemia in the general population could not be determined [12].
Some possible mechanisms may explain our current findings. First, the HTGW phenotype has been proven to be related to increased visceral fat and insulin resistance [22]. The increase in insulin concentration caused by insulin resistance can enhance the reabsorption of sodium in renal tubules, thereby reducing the clearance rate of uric acid and causing the development of hyperuricemia [23]. In this study, we found that compared with the model2 (without adjusting BMI), the model3 (fully adjusted model) experienced a $23.0\%$ attenuation of the effects from 1.82 ($95\%$ CI: 1.36 to 2.42) in model2 to 1.41 ($95\%$ CI: 1.05 to 1.91) in model3, this might support aforementioned pathway (Table S2). Second, estrogen is known to promote the excretion of uric acid [24]. This possibly due to the estrogen level in postmenopausal women decreases, which may cause an increase in lipoprotein lipase activity or a decrease in fat decomposition, leading to more severe abdominal fat accumulation [25], and then increased abdominal fat is associated with a series of metabolic abnormalities, such as insulin resistance and dyslipidemia, which may increase the level of uric acid in postmenopausal women [26]. Previous studies might support this speculation that females have higher risk of elevated triglyceride level, enlarged waist circumference, faster growth level of uric acid in comparison to males after aged 45 or 50 [14, 27]. Therefore, possible reasons for the higher risk of hyperuricemia caused by the HTGW phenotype combined with females are that the coexistence of insulin resistance and estrogen deficiency hinders the clearance rate of uric acid.
Our findings have important public health implications. China is experiencing an epidemic of obesity and metabolic diseases due to rapid economic development and lifestyle changes [28]. For example, an epidemiologic study indicated that the prevalence of abdominal obesity increased greatly among Chinese adults (especially those aged 40–80) from 1993 to 2015 [29]. In addition, hypertriglyceridemia, the most common dyslipidemia in the general population, is less frequent with advancing age in males but more frequent in females [30]. Therefore, intervention strategies aimed at reducing hyperlipidemia or abdominal obesity, such as weight loss, changing dietary habits, physical exercise and drug treatment, are essential to reduce the risk of hyperuricemia [31].
## Study strengths and limitations
The current study has several strengths, including the use of nationally representative data with a large cohort sample size and objective measures of exposures and outcome indicators. The use of longitudinal design minimize the chance of reverse causation. Nevertheless, some limitations should be considered. First, waist circumference and triglyceride levels were only measured at baseline, which prevented assessment of their impact on hyperuricemia over time, resulting in a potential underestimation of the association. Second, there were substantial missing values ($$n = 2766$$) for triglycerides and waist circumference, which may lead to selection bias, as healthy individuals seemed to contribute more to these missing values. However, the results remained similar in the sensitivity analyses when missing values were replaced by imputation. We have also adjusted, e.g., health status into the model to minimize this selection bias. Third, several variables that would have better explained the association between the HTGW phenotype and hyperuricemia, such as diet, genetics, sex hormone levels, and menopausal status of females, were not available in this dataset.
## Conclusion
In summary, females with the HTGW phenotype were more likely to suffer from hyperuricemia among middle-aged and older adults. Future interventions to prevent hyperuricemia should target females with both enlarged waist circumference and elevated triglyceride levels.
## Supplementary Information
Additional file 1.
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